This page list all parameters of the current SCIP version. This list can easily be generated by SCIP via the interactive shell using the following command:
SCIP> set save <file name>
or via the function call:
SCIP_CALL( SCIPwriteParams(scip, <file name>, TRUE, FALSE) );
# SCIP version 9.1.0

# branching score function ('s'um, 'p'roduct, 'q'uotient)
# [type: char, advanced: TRUE, range: {spq}, default: p]
branching/scorefunc = p

# branching score factor to weigh downward and upward gain prediction in sum score function
# [type: real, advanced: TRUE, range: [0,1], default: 0.167]
branching/scorefac = 0.167

# should branching on binary variables be preferred?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/preferbinary = FALSE

# minimal relative distance of branching point to bounds when branching on a continuous variable
# [type: real, advanced: FALSE, range: [0,0.5], default: 0.2]
branching/clamp = 0.2

# fraction by which to move branching point of a continuous variable towards the middle of the domain; a value of 1.0 leads to branching always in the middle of the domain
# [type: real, advanced: FALSE, range: [0,1], default: 0.75]
branching/midpull = 0.75

# multiply midpull by relative domain width if the latter is below this value
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
branching/midpullreldomtrig = 0.5

# strategy for normalization of LP gain when updating pseudocosts of continuous variables (divide by movement of 'l'p value, reduction in 'd'omain width, or reduction in domain width of 's'ibling)
# [type: char, advanced: FALSE, range: {dls}, default: s]
branching/lpgainnormalize = s

# should updating pseudo costs for continuous variables be delayed to the time after separation?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
branching/delaypscostupdate = TRUE

# should pseudo costs be updated also in diving and probing mode?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
branching/divingpscost = TRUE

# should all strong branching children be regarded even if one is detected to be infeasible? (only with propagation)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/forceallchildren = FALSE

# child node to be regarded first during strong branching (only with propagation): 'u'p child, 'd'own child, 'h'istory-based, or 'a'utomatic
# [type: char, advanced: TRUE, range: {aduh}, default: a]
branching/firstsbchild = a

# should LP solutions during strong branching with propagation be checked for feasibility?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/checksol = TRUE

# should LP solutions during strong branching with propagation be rounded? (only when checksbsol=TRUE)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/roundsbsol = TRUE

# score adjustment near zero by adding epsilon (TRUE) or using maximum (FALSE)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/sumadjustscore = FALSE

# should automatic tree compression after the presolving be enabled?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
compression/enable = FALSE

# should conflict analysis be enabled?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
conflict/enable = TRUE

# should conflicts based on an old cutoff bound be removed from the conflict pool after improving the primal bound?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/cleanboundexceedings = TRUE

# use local rows to construct infeasibility proofs
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/uselocalrows = TRUE

# should propagation conflict analysis be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
conflict/useprop = TRUE

# should infeasible LP conflict analysis be used? ('o'ff, 'c'onflict graph, 'd'ual ray, 'b'oth conflict graph and dual ray)
# [type: char, advanced: FALSE, range: {ocdb}, default: b]
conflict/useinflp = b

# should bound exceeding LP conflict analysis be used? ('o'ff, 'c'onflict graph, 'd'ual ray, 'b'oth conflict graph and dual ray)
# [type: char, advanced: FALSE, range: {ocdb}, default: b]
conflict/useboundlp = b

# should infeasible/bound exceeding strong branching conflict analysis be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
conflict/usesb = TRUE

# should pseudo solution conflict analysis be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
conflict/usepseudo = TRUE

# maximal fraction of variables involved in a conflict constraint
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.15]
conflict/maxvarsfac = 0.15

# minimal absolute maximum of variables involved in a conflict constraint
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
conflict/minmaxvars = 0

# maximal number of LP resolving loops during conflict analysis (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 2]
conflict/maxlploops = 2

# maximal number of LP iterations in each LP resolving loop (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
conflict/lpiterations = 10

# number of depth levels up to which first UIP's are used in conflict analysis (-1: use All-FirstUIP rule)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
conflict/fuiplevels = -1

# maximal number of intermediate conflict constraints generated in conflict graph (-1: use every intermediate constraint)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
conflict/interconss = -1

# number of depth levels up to which UIP reconvergence constraints are generated (-1: generate reconvergence constraints in all depth levels)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
conflict/reconvlevels = -1

# maximal number of conflict constraints accepted at an infeasible node (-1: use all generated conflict constraints)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
conflict/maxconss = 10

# maximal size of conflict store (-1: auto, 0: disable storage)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10000]
conflict/maxstoresize = 10000

# should binary conflicts be preferred?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
conflict/preferbinary = FALSE

# prefer infeasibility proof to boundexceeding proof
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/prefinfproof = TRUE

# should conflict constraints be generated that are only valid locally?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/allowlocal = TRUE

# should conflict constraints be attached only to the local subtree where they can be useful?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
conflict/settlelocal = FALSE

# should earlier nodes be repropagated in order to replace branching decisions by deductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/repropagate = TRUE

# should constraints be kept for repropagation even if they are too long?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/keepreprop = TRUE

# should the conflict constraints be separated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/separate = TRUE

# should the conflict constraints be subject to aging?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/dynamic = TRUE

# should the conflict's relaxations be subject to LP aging and cleanup?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/removable = TRUE

# score factor for depth level in bound relaxation heuristic
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
conflict/graph/depthscorefac = 1

# score factor for impact on acticity in bound relaxation heuristic
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
conflict/proofscorefac = 1

# score factor for up locks in bound relaxation heuristic
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0]
conflict/uplockscorefac = 0

# score factor for down locks in bound relaxation heuristic
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0]
conflict/downlockscorefac = 0

# factor to decrease importance of variables' earlier conflict scores
# [type: real, advanced: TRUE, range: [1e-06,1], default: 0.98]
conflict/scorefac = 0.98

# number of successful conflict analysis calls that trigger a restart (0: disable conflict restarts)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
conflict/restartnum = 0

# factor to increase restartnum with after each restart
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1.5]
conflict/restartfac = 1.5

# should relaxed bounds be ignored?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
conflict/ignorerelaxedbd = FALSE

# maximal number of variables to try to detect global bound implications and shorten the whole conflict set (0: disabled)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 250]
conflict/maxvarsdetectimpliedbounds = 250

# try to shorten the whole conflict set or terminate early (depending on the 'maxvarsdetectimpliedbounds' parameter)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
conflict/fullshortenconflict = TRUE

# the weight the VSIDS score is weight by updating the VSIDS for a variable if it is part of a conflict
# [type: real, advanced: FALSE, range: [0,1], default: 0]
conflict/conflictweight = 0

# the weight the VSIDS score is weight by updating the VSIDS for a variable if it is part of a conflict graph
# [type: real, advanced: FALSE, range: [0,1], default: 1]
conflict/conflictgraphweight = 1

# minimal improvement of primal bound to remove conflicts based on a previous incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.05]
conflict/minimprove = 0.05

# weight of the size of a conflict used in score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0.001]
conflict/weightsize = 0.001

# weight of the repropagation depth of a conflict used in score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
conflict/weightrepropdepth = 0.1

# weight of the valid depth of a conflict used in score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 1]
conflict/weightvaliddepth = 1

# apply cut generating functions to construct alternative proofs
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
conflict/sepaaltproofs = FALSE

# maximum age an unnecessary constraint can reach before it is deleted (0: dynamic, -1: keep all constraints)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/agelimit = 0

# age of a constraint after which it is marked obsolete (0: dynamic, -1 do not mark constraints obsolete)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/obsoleteage = -1

# should enforcement of pseudo solution be disabled?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/disableenfops = FALSE

# verbosity level of output
# [type: int, advanced: FALSE, range: [0,5], default: 4]
display/verblevel = 4

# maximal number of characters in a node information line
# [type: int, advanced: FALSE, range: [0,2147483647], default: 143]
display/width = 143

# frequency for displaying node information lines
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
display/freq = 100

# frequency for displaying header lines (every n'th node information line)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 15]
display/headerfreq = 15

# should the LP solver display status messages?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
display/lpinfo = FALSE

# display all violations for a given start solution / the best solution after the solving process?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
display/allviols = FALSE

# should the relevant statistics be displayed at the end of solving?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
display/relevantstats = TRUE

# should setting of common subscip parameters include the activation of the UCT node selector?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/useuctsubscip = FALSE

# should statistics be collected for variable domain value pairs?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
history/valuebased = FALSE

# should variable histories be merged from sub-SCIPs whenever possible?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
history/allowmerge = FALSE

# should variable histories be transferred to initialize SCIP copies?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
history/allowtransfer = FALSE

# maximal time in seconds to run
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1e+20]
limits/time = 1e+20

# maximal number of nodes to process (-1: no limit)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: -1]
limits/nodes = -1

# maximal number of total nodes (incl. restarts) to process (-1: no limit)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: -1]
limits/totalnodes = -1

# solving stops, if the given number of nodes was processed since the last improvement of the primal solution value (-1: no limit)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: -1]
limits/stallnodes = -1

# maximal memory usage in MB; reported memory usage is lower than real memory usage!
# [type: real, advanced: FALSE, range: [0,8796093022207], default: 8796093022207]
limits/memory = 8796093022207

# solving stops, if the relative gap = |primal - dual|/MIN(|dual|,|primal|) is below the given value, the gap is 'Infinity', if primal and dual bound have opposite signs
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0]
limits/gap = 0

# solving stops, if the absolute gap = |primalbound - dualbound| is below the given value
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0]
limits/absgap = 0

# solving stops, if primal bound is at least as good as given value (alias objectivestop)
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1e+99]
limits/primal = 1e+99

# solving stops, if dual bound is at least as good as given value
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1e+99]
limits/dual = 1e+99

# solving stops, if the given number of solutions were found; this limit is first checked in presolving (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
limits/solutions = -1

# solving stops, if the given number of solution improvements were found (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
limits/bestsol = -1

# maximal number of solutions to store in the solution storage
# [type: int, advanced: FALSE, range: [1,2147483647], default: 100]
limits/maxsol = 100

# maximal number of solutions candidates to store in the solution storage of the original problem
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
limits/maxorigsol = 10

# solving stops, if the given number of restarts was triggered (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
limits/restarts = -1

# if solve exceeds this number of nodes for the first time, an automatic restart is triggered (-1: no automatic restart)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
limits/autorestartnodes = -1

# frequency for solving LP at the nodes (-1: never; 0: only root LP)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
lp/solvefreq = 1

# iteration limit for each single LP solve (-1: no limit)
# [type: longint, advanced: TRUE, range: [-1,9223372036854775807], default: -1]
lp/iterlim = -1

# iteration limit for initial root LP solve (-1: no limit)
# [type: longint, advanced: TRUE, range: [-1,9223372036854775807], default: -1]
lp/rootiterlim = -1

# maximal depth for solving LP at the nodes (-1: no depth limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
lp/solvedepth = -1

# LP algorithm for solving initial LP relaxations (automatic 's'implex, 'p'rimal simplex, 'd'ual simplex, 'b'arrier, barrier with 'c'rossover)
# [type: char, advanced: FALSE, range: {spdbc}, default: s]
lp/initalgorithm = s

# LP algorithm for resolving LP relaxations if a starting basis exists (automatic 's'implex, 'p'rimal simplex, 'd'ual simplex, 'b'arrier, barrier with 'c'rossover)
# [type: char, advanced: FALSE, range: {spdbc}, default: s]
lp/resolvealgorithm = s

# LP pricing strategy ('l'pi default, 'a'uto, 'f'ull pricing, 'p'artial, 's'teepest edge pricing, 'q'uickstart steepest edge pricing, 'd'evex pricing)
# [type: char, advanced: FALSE, range: {lafpsqd}, default: l]
lp/pricing = l

# should lp state be cleared at the end of probing mode when lp was initially unsolved, e.g., when called right after presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/clearinitialprobinglp = TRUE

# should the LP be resolved to restore the state at start of diving (if FALSE we buffer the solution values)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/resolverestore = FALSE

# should the buffers for storing LP solution values during diving be freed at end of diving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/freesolvalbuffers = FALSE

# maximum age a dynamic column can reach before it is deleted from the LP (-1: don't delete columns due to aging)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
lp/colagelimit = 10

# maximum age a dynamic row can reach before it is deleted from the LP (-1: don't delete rows due to aging)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
lp/rowagelimit = 10

# should new non-basic columns be removed after LP solving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/cleanupcols = FALSE

# should new non-basic columns be removed after root LP solving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/cleanupcolsroot = FALSE

# should new basic rows be removed after LP solving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/cleanuprows = TRUE

# should new basic rows be removed after root LP solving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/cleanuprowsroot = TRUE

# should LP solver's return status be checked for stability?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/checkstability = TRUE

# maximum condition number of LP basis counted as stable (-1.0: no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
lp/conditionlimit = -1

# minimal Markowitz threshold to control sparsity/stability in LU factorization
# [type: real, advanced: TRUE, range: [0.0001,0.9999], default: 0.01]
lp/minmarkowitz = 0.01

# should LP solutions be checked for primal feasibility, resolving LP when numerical troubles occur?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/checkprimfeas = TRUE

# should LP solutions be checked for dual feasibility, resolving LP when numerical troubles occur?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/checkdualfeas = TRUE

# should infeasibility proofs from the LP be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/checkfarkas = TRUE

# which FASTMIP setting of LP solver should be used? 0: off, 1: low
# [type: int, advanced: TRUE, range: [0,1], default: 1]
lp/fastmip = 1

# LP scaling (0: none, 1: normal, 2: aggressive)
# [type: int, advanced: TRUE, range: [0,2], default: 1]
lp/scaling = 1

# should presolving of LP solver be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/presolving = TRUE

# should the lexicographic dual algorithm be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/lexdualalgo = FALSE

# should the lexicographic dual algorithm be applied only at the root node
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/lexdualrootonly = TRUE

# maximum number of rounds in the lexicographic dual algorithm (-1: unbounded)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 2]
lp/lexdualmaxrounds = 2

# choose fractional basic variables in lexicographic dual algorithm?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
lp/lexdualbasic = FALSE

# turn on the lex dual algorithm only when stalling?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
lp/lexdualstalling = TRUE

# disable the cutoff bound in the LP solver? (0: enabled, 1: disabled, 2: auto)
# [type: int, advanced: TRUE, range: [0,2], default: 2]
lp/disablecutoff = 2

# simplex algorithm shall use row representation of the basis if number of rows divided by number of columns exceeds this value (-1.0 to disable row representation)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: 1.2]
lp/rowrepswitch = 1.2

# number of threads used for solving the LP (0: automatic)
# [type: int, advanced: TRUE, range: [0,64], default: 0]
lp/threads = 0

# factor of average LP iterations that is used as LP iteration limit for LP resolve (-1: unlimited)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
lp/resolveiterfac = -1

# minimum number of iterations that are allowed for LP resolve
# [type: int, advanced: TRUE, range: [1,2147483647], default: 1000]
lp/resolveitermin = 1000

# LP solution polishing method (0: disabled, 1: only root, 2: always, 3: auto)
# [type: int, advanced: TRUE, range: [0,3], default: 3]
lp/solutionpolishing = 3

# LP refactorization interval (0: auto)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
lp/refactorinterval = 0

# should the Farkas duals always be collected when an LP is found to be infeasible?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
lp/alwaysgetduals = FALSE

# solver to use for solving NLPs; leave empty to select NLPI with highest priority
# [type: string, advanced: FALSE, default: ""]
nlp/solver = ""

# should the NLP relaxation be always disabled (also for NLPs/MINLPs)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlp/disable = FALSE

# fraction of maximal memory usage resulting in switch to memory saving mode
# [type: real, advanced: FALSE, range: [0,1], default: 0.8]
memory/savefac = 0.8

# memory growing factor for dynamically allocated arrays
# [type: real, advanced: TRUE, range: [1,10], default: 1.2]
memory/arraygrowfac = 1.2

# initial size of dynamically allocated arrays
# [type: int, advanced: TRUE, range: [0,2147483647], default: 4]
memory/arraygrowinit = 4

# memory growing factor for tree array
# [type: real, advanced: TRUE, range: [1,10], default: 2]
memory/treegrowfac = 2

# initial size of tree array
# [type: int, advanced: TRUE, range: [0,2147483647], default: 65536]
memory/treegrowinit = 65536

# memory growing factor for path array
# [type: real, advanced: TRUE, range: [1,10], default: 2]
memory/pathgrowfac = 2

# initial size of path array
# [type: int, advanced: TRUE, range: [0,2147483647], default: 256]
memory/pathgrowinit = 256

# should the CTRL-C interrupt be caught by SCIP?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/catchctrlc = TRUE

# should a hashtable be used to map from variable names to variables?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/usevartable = TRUE

# should a hashtable be used to map from constraint names to constraints?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/useconstable = TRUE

# should smaller hashtables be used? yields better performance for small problems with about 100 variables
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
misc/usesmalltables = FALSE

# should the statistics be reset if the transformed problem is freed (in case of a Benders' decomposition this parameter should be set to FALSE)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/resetstat = TRUE

# should only solutions be checked which improve the primal bound
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
misc/improvingsols = FALSE

# should the reason be printed if a given start solution is infeasible
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/printreason = TRUE

# should the usage of external memory be estimated?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/estimexternmem = TRUE

# try to avoid running into memory limit by restricting plugins like heuristics?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/avoidmemout = TRUE

# should SCIP try to transfer original solutions to the transformed space (after presolving)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/transorigsols = TRUE

# should SCIP try to transfer transformed solutions to the original space (after solving)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/transsolsorig = TRUE

# should SCIP calculate the primal dual integral value?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/calcintegral = TRUE

# should SCIP try to remove infinite fixings from solutions copied to the solution store?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
misc/finitesolutionstore = FALSE

# should the best solution be transformed to the orignal space and be output in command line run?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/outputorigsol = TRUE

# should strong dual reductions be allowed in propagation and presolving?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/allowstrongdualreds = TRUE

# should weak dual reductions be allowed in propagation and presolving?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/allowweakdualreds = TRUE

# should the objective function be scaled so that it is always integer?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
misc/scaleobj = TRUE

# should detailed statistics for diving heuristics be shown?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
misc/showdivingstats = FALSE

# objective value for reference purposes
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1e+99]
misc/referencevalue = 1e+99

# bitset describing used symmetry handling technique: (0: off; 1: constraint-based (orbitopes and/or symresacks); 2: orbital fixing; 3: orbitopes and orbital fixing; 4: Schreier Sims cuts; 5: Schreier Sims cuts and orbitopes; 6: Schreier Sims cuts and orbital fixing; 7: Schreier Sims cuts, orbitopes, and orbital fixing) See type_symmetry.h.
# [type: int, advanced: FALSE, range: [0,7], default: 7]
misc/usesymmetry = 7

# global shift of all random seeds in the plugins and the LP random seed
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
randomization/randomseedshift = 0

# seed value for permuting the problem after reading/transformation (0: no permutation)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
randomization/permutationseed = 0

# should order of constraints be permuted (depends on permutationseed)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
randomization/permuteconss = TRUE

# should order of variables be permuted (depends on permutationseed)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
randomization/permutevars = FALSE

# random seed for LP solver, e.g. for perturbations in the simplex (0: LP default)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
randomization/lpseed = 0

# child selection rule ('d'own, 'u'p, 'p'seudo costs, 'i'nference, 'l'p value, 'r'oot LP value difference, 'h'ybrid inference/root LP value difference)
# [type: char, advanced: FALSE, range: {dupilrh}, default: h]
nodeselection/childsel = h

# values larger than this are considered infinity
# [type: real, advanced: FALSE, range: [10000000000,1e+98], default: 1e+20]
numerics/infinity = 1e+20

# absolute values smaller than this are considered zero
# [type: real, advanced: FALSE, range: [1e-20,0.001], default: 1e-09]
numerics/epsilon = 1e-09

# absolute values of sums smaller than this are considered zero
# [type: real, advanced: FALSE, range: [1e-17,0.001], default: 1e-06]
numerics/sumepsilon = 1e-06

# feasibility tolerance for constraints
# [type: real, advanced: FALSE, range: [1e-17,0.001], default: 1e-06]
numerics/feastol = 1e-06

# feasibility tolerance factor; for checking the feasibility of the best solution
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1]
numerics/checkfeastolfac = 1

# factor w.r.t. primal feasibility tolerance that determines default (and maximal) primal feasibility tolerance of LP solver
# [type: real, advanced: FALSE, range: [1e-06,1], default: 1]
numerics/lpfeastolfactor = 1

# feasibility tolerance for reduced costs in LP solution
# [type: real, advanced: FALSE, range: [1e-17,0.001], default: 1e-07]
numerics/dualfeastol = 1e-07

# LP convergence tolerance used in barrier algorithm
# [type: real, advanced: TRUE, range: [1e-17,0.001], default: 1e-10]
numerics/barrierconvtol = 1e-10

# minimal relative improve for strengthening bounds
# [type: real, advanced: TRUE, range: [1e-17,1e+98], default: 0.05]
numerics/boundstreps = 0.05

# minimal variable distance value to use for branching pseudo cost updates
# [type: real, advanced: TRUE, range: [1e-17,1], default: 0.1]
numerics/pseudocosteps = 0.1

# minimal objective distance value to use for branching pseudo cost updates
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.0001]
numerics/pseudocostdelta = 0.0001

# minimal decrease factor that causes the recomputation of a value (e.g., pseudo objective) instead of an update
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 10000000]
numerics/recomputefac = 10000000

# values larger than this are considered huge and should be handled separately (e.g., in activity computation)
# [type: real, advanced: TRUE, range: [0,1e+98], default: 1e+15]
numerics/hugeval = 1e+15

# maximal number of presolving rounds (-1: unlimited, 0: off)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/maxrounds = -1

# abort presolve, if at most this fraction of the problem was changed in last presolve round
# [type: real, advanced: TRUE, range: [0,1], default: 0.0008]
presolving/abortfac = 0.0008

# maximal number of restarts (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/maxrestarts = -1

# fraction of integer variables that were fixed in the root node triggering a restart with preprocessing after root node evaluation
# [type: real, advanced: TRUE, range: [0,1], default: 0.025]
presolving/restartfac = 0.025

# limit on number of entries in clique table relative to number of problem nonzeros
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
presolving/clqtablefac = 2

# fraction of integer variables that were fixed in the root node triggering an immediate restart with preprocessing
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
presolving/immrestartfac = 0.1

# fraction of integer variables that were globally fixed during the solving process triggering a restart with preprocessing
# [type: real, advanced: TRUE, range: [0,1], default: 1]
presolving/subrestartfac = 1

# minimal fraction of integer variables removed after restart to allow for an additional restart
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
presolving/restartminred = 0.1

# should multi-aggregation of variables be forbidden?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/donotmultaggr = FALSE

# should aggregation of variables be forbidden?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/donotaggr = FALSE

# maximal number of variables priced in per pricing round
# [type: int, advanced: FALSE, range: [1,2147483647], default: 100]
pricing/maxvars = 100

# maximal number of priced variables at the root node
# [type: int, advanced: FALSE, range: [1,2147483647], default: 2000]
pricing/maxvarsroot = 2000

# pricing is aborted, if fac * pricing/maxvars pricing candidates were found
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 2]
pricing/abortfac = 2

# should variables created at the current node be deleted when the node is solved in case they are not present in the LP anymore?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
pricing/delvars = FALSE

# should variables created at the root node be deleted when the root is solved in case they are not present in the LP anymore?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
pricing/delvarsroot = FALSE

# should the variables be labelled for the application of Benders' decomposition?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
decomposition/benderslabels = FALSE

# if a decomposition exists, should Benders' decomposition be applied?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
decomposition/applybenders = FALSE

# maximum number of edges in block graph computation (-1: no limit, 0: disable block graph computation)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10000]
decomposition/maxgraphedge = 10000

# disable expensive measures
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
decomposition/disablemeasures = FALSE

# the tolerance used for checking optimality in Benders' decomposition. tol where optimality is given by LB + tol > UB.
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1e-06]
benders/solutiontol = 1e-06

# should Benders' cuts be generated from the solution to the LP relaxation?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/cutlpsol = TRUE

# should Benders' decomposition be copied for use in sub-SCIPs?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/copybenders = TRUE

# maximal number of propagation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
propagating/maxrounds = 100

# maximal number of propagation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1000]
propagating/maxroundsroot = 1000

# should propagation be aborted immediately? setting this to FALSE could help conflict analysis to produce more conflict constraints
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
propagating/abortoncutoff = TRUE

# should reoptimization used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/enable = FALSE

# maximal number of saved nodes
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 2147483647]
reoptimization/maxsavednodes = 2147483647

# maximal number of bound changes between two stored nodes on one path
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2147483647]
reoptimization/maxdiffofnodes = 2147483647

# save global constraints to separate infeasible subtrees.
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reoptimization/globalcons/sepainfsubtrees = TRUE

# separate the optimal solution, i.e., for constrained shortest path
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/sepabestsol = FALSE

# use variable history of the previous solve if the objctive function has changed only slightly
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/storevarhistory = FALSE

# re-use pseudo costs if the objective function changed only slightly 
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/usepscost = FALSE

# at which reopttype should the LP be solved? (1: transit, 3: strong branched, 4: w/ added logicor, 5: only leafs).
# [type: int, advanced: TRUE, range: [1,5], default: 1]
reoptimization/solvelp = 1

# maximal number of bound changes at node to skip solving the LP
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
reoptimization/solvelpdiff = 1

# number of best solutions which should be saved for the following runs. (-1: save all)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2147483647]
reoptimization/savesols = 2147483647

# similarity of two sequential objective function to disable solving the root LP.
# [type: real, advanced: TRUE, range: [-1,1], default: 0.8]
reoptimization/objsimrootLP = 0.8

# similarity of two objective functions to re-use stored solutions
# [type: real, advanced: TRUE, range: [-1,1], default: -1]
reoptimization/objsimsol = -1

# minimum similarity for using reoptimization of the search tree.
# [type: real, advanced: TRUE, range: [-1,1], default: -1]
reoptimization/delay = -1

# time limit over all reoptimization rounds?.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/commontimelimit = FALSE

# replace branched inner nodes by their child nodes, if the number of bound changes is not to large
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reoptimization/shrinkinner = TRUE

# try to fix variables at the root node before reoptimizing by probing like strong branching
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reoptimization/strongbranchinginit = TRUE

# delete stored nodes which were not reoptimized
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reoptimization/reducetofrontier = TRUE

# force a restart if the last n optimal solutions were found by heuristic reoptsols
# [type: int, advanced: TRUE, range: [1,2147483647], default: 3]
reoptimization/forceheurrestart = 3

# save constraint propagations
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/saveconsprop = FALSE

# use constraints to reconstruct the subtree pruned be dual reduction when reactivating the node
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reoptimization/usesplitcons = TRUE

# use 'd'efault, 'r'andom or a variable ordering based on 'i'nference score for interdiction branching used during reoptimization
# [type: char, advanced: TRUE, range: {dir}, default: d]
reoptimization/varorderinterdiction = d

# reoptimize cuts found at the root node
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reoptimization/usecuts = FALSE

# maximal age of a cut to be use for reoptimization
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
reoptimization/maxcutage = 0

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separation (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
separating/maxbounddist = 1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying local separation (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 0]
separating/maxlocalbounddist = 0

# maximal ratio between coefficients in strongcg, cmir, and flowcover cuts
# [type: real, advanced: FALSE, range: [1,1e+98], default: 10000]
separating/maxcoefratio = 10000

# maximal ratio between coefficients (as factor of 1/feastol) to ensure in rowprep cleanup
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
separating/maxcoefratiofacrowprep = 10

# minimal efficacy for a cut to enter the LP
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.0001]
separating/minefficacy = 0.0001

# minimal efficacy for a cut to enter the LP in the root node
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.0001]
separating/minefficacyroot = 0.0001

# minimum cut activity quotient to convert cuts into constraints during a restart (0.0: all cuts are converted)
# [type: real, advanced: FALSE, range: [0,1], default: 0.8]
separating/minactivityquot = 0.8

# factor w.r.t. maxcuts for maximal number of cuts generated per separation round (-1.0: no limit, >= 0.0: valid finite limit)
# [type: real, advanced: FALSE, range: [-1,1.79769313486232e+308], default: 2]
separating/maxcutsgenfactor = 2

# factor w.r.t. maxcutsroot for maximal number of generated cuts per separation round at the root node (-1.0: no limit, >= 0.0: valid finite limit)
# [type: real, advanced: FALSE, range: [-1,1.79769313486232e+308], default: 2]
separating/maxcutsrootgenfactor = 2

# function used for calc. scalar prod. in orthogonality test ('e'uclidean, 'd'iscrete)
# [type: char, advanced: TRUE, range: {ed}, default: e]
separating/orthofunc = e

# row norm to use for efficacy calculation ('e'uclidean, 'm'aximum, 's'um, 'd'iscrete)
# [type: char, advanced: TRUE, range: {emsd}, default: e]
separating/efficacynorm = e

# cut selection during restart ('a'ge, activity 'q'uotient)
# [type: char, advanced: TRUE, range: {aq}, default: a]
separating/cutselrestart = a

# cut selection for sub SCIPs  ('a'ge, activity 'q'uotient)
# [type: char, advanced: TRUE, range: {aq}, default: a]
separating/cutselsubscip = a

# should cutpool separate only cuts with high relative efficacy?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/filtercutpoolrel = FALSE

# maximal number of runs for which separation is enabled (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/maxruns = -1

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/maxrounds = -1

# maximal number of separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/maxroundsroot = -1

# maximal number of separation rounds in the root node of a subsequent run (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/maxroundsrootsubrun = -1

# maximal additional number of separation rounds in subsequent price-and-cut loops (-1: no additional restriction)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1]
separating/maxaddrounds = 1

# maximal number of consecutive separation rounds without objective or integrality improvement in local nodes (-1: no additional restriction)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
separating/maxstallrounds = 1

# maximal number of consecutive separation rounds without objective or integrality improvement in the root node (-1: no additional restriction)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/maxstallroundsroot = 10

# maximal number of cuts separated per separation round (0: disable local separation)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
separating/maxcuts = 100

# maximal number of separated cuts per separation round at the root node (0: disable root node separation)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2000]
separating/maxcutsroot = 2000

# maximum age a cut can reach before it is deleted from the global cut pool, or -1 to keep all cuts
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 80]
separating/cutagelimit = 80

# separation frequency for the global cut pool (-1: disable global cut pool, 0: only separate pool at the root)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/poolfreq = 10

# parallel optimisation mode, 0: opportunistic or 1: deterministic.
# [type: int, advanced: FALSE, range: [0,1], default: 1]
parallel/mode = 1

# the minimum number of threads used during parallel solve
# [type: int, advanced: FALSE, range: [0,64], default: 1]
parallel/minnthreads = 1

# the maximum number of threads used during parallel solve
# [type: int, advanced: FALSE, range: [0,64], default: 8]
parallel/maxnthreads = 8

# set different random seeds in each concurrent solver?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
concurrent/changeseeds = TRUE

# use different child selection rules in each concurrent solver?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
concurrent/changechildsel = TRUE

# should the concurrent solvers communicate global variable bound changes?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
concurrent/commvarbnds = TRUE

# should the problem be presolved before it is copied to the concurrent solvers?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
concurrent/presolvebefore = TRUE

# maximum number of solutions that will be shared in a one synchronization
# [type: int, advanced: FALSE, range: [0,2147483647], default: 5131912]
concurrent/initseed = 5131912

# initial frequency of synchronization with other threads
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
concurrent/sync/freqinit = 10

# maximal frequency of synchronization with other threads
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
concurrent/sync/freqmax = 10

# factor by which the frequency of synchronization is changed
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 1.5]
concurrent/sync/freqfactor = 1.5

# when adapting the synchronization frequency this value is the targeted relative difference by which the absolute gap decreases per synchronization
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.001]
concurrent/sync/targetprogress = 0.001

# maximum number of solutions that will be shared in a single synchronization
# [type: int, advanced: FALSE, range: [0,1000], default: 3]
concurrent/sync/maxnsols = 3

# maximum number of synchronizations before reading is enforced regardless of delay
# [type: int, advanced: TRUE, range: [0,100], default: 7]
concurrent/sync/maxnsyncdelay = 7

# minimum delay before synchronization data is read
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
concurrent/sync/minsyncdelay = 10

# how many of the N best solutions should be considered for synchronization?
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
concurrent/sync/nbestsols = 10

# path prefix for parameter setting files of concurrent solvers
# [type: string, advanced: FALSE, default: ""]
concurrent/paramsetprefix = ""

# default clock type (1: CPU user seconds, 2: wall clock time)
# [type: int, advanced: FALSE, range: [1,2], default: 2]
timing/clocktype = 2

# is timing enabled?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
timing/enabled = TRUE

# belongs reading time to solving time?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
timing/reading = FALSE

# should clock checks of solving time be performed less frequently (note: time limit could be exceeded slightly)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
timing/rareclockcheck = FALSE

# should timing for statistic output be performed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
timing/statistictiming = TRUE

# should time for evaluation in NLP solves be measured?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
timing/nlpieval = FALSE

# name of the VBC tool output file, or - if no VBC tool output should be created
# [type: string, advanced: FALSE, default: "-"]
visual/vbcfilename = "-"

# name of the BAK tool output file, or - if no BAK tool output should be created
# [type: string, advanced: FALSE, default: "-"]
visual/bakfilename = "-"

# should the real solving time be used instead of a time step counter in visualization?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
visual/realtime = TRUE

# should the node where solutions are found be visualized?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
visual/dispsols = FALSE

# should lower bound information be visualized?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
visual/displb = FALSE

# should be output the external value of the objective?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
visual/objextern = TRUE

# should model constraints be marked as initial?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/initialconss = TRUE

# should model constraints be subject to aging?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/dynamicconss = TRUE

# should columns be added and removed dynamically to the LP?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/dynamiccols = FALSE

# should rows be added and removed dynamically to the LP?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/dynamicrows = FALSE

# should all constraints be written (including the redundant constraints)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
write/allconss = FALSE

# should variables set to zero be printed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
write/printzeros = FALSE

# when writing a generic problem the index for the first variable should start with?
# [type: int, advanced: FALSE, range: [0,1073741823], default: 0]
write/genericnamesoffset = 0

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/nonlinear/sepafreq = 1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/nonlinear/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/nonlinear/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/nonlinear/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/nonlinear/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/nonlinear/presoltiming = 28

# limit on number of propagation rounds for a set of constraints within one round of SCIP propagation
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
constraints/nonlinear/maxproprounds = 10

# whether to check bounds of all auxiliary variable to seed reverse propagation
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/propauxvars = TRUE

# strategy on how to relax variable bounds during bound tightening: relax (n)ot, relax by (a)bsolute value, relax always by a(b)solute value, relax by (r)relative value
# [type: char, advanced: TRUE, range: {nabr}, default: r]
constraints/nonlinear/varboundrelax = r

# by how much to relax variable bounds during bound tightening if strategy 'a', 'b', or 'r'
# [type: real, advanced: TRUE, range: [0,1], default: 1e-09]
constraints/nonlinear/varboundrelaxamount = 1e-09

# by how much to relax constraint sides during bound tightening
# [type: real, advanced: TRUE, range: [0,1], default: 1e-09]
constraints/nonlinear/conssiderelaxamount = 1e-09

# maximal relative perturbation of reference point when computing facet of envelope of vertex-polyhedral function (dim>2)
# [type: real, advanced: TRUE, range: [0,1], default: 0.001]
constraints/nonlinear/vpmaxperturb = 0.001

# adjust computed facet of envelope of vertex-polyhedral function up to a violation of this value times LP feasibility tolerance
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 10]
constraints/nonlinear/vpadjfacetthresh = 10

# whether to use dual simplex instead of primal simplex for LP that computes facet of vertex-polyhedral function
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/vpdualsimplex = TRUE

# maximal number of auxiliary expressions per bilinear term
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
constraints/nonlinear/bilinmaxnauxexprs = 10

# whether to reformulate products of binary variables during presolving
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/reformbinprods = TRUE

# whether to use the AND constraint handler for reformulating binary products
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/reformbinprodsand = TRUE

# minimum number of terms to reformulate bilinear binary products by factorizing variables (<= 1: disabled)
# [type: int, advanced: FALSE, range: [1,2147483647], default: 50]
constraints/nonlinear/reformbinprodsfac = 50

# whether to forbid multiaggregation of nonlinear variables
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/forbidmultaggrnlvar = TRUE

# whether to tighten LP feasibility tolerance during enforcement, if it seems useful
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/tightenlpfeastol = TRUE

# whether to (re)run propagation in enforcement
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/propinenforce = FALSE

# threshold for when to regard a cut from an estimator as weak (lower values allow more weak cuts)
# [type: real, advanced: TRUE, range: [0,1], default: 0.2]
constraints/nonlinear/weakcutthreshold = 0.2

# "strong" cuts will be scaled to have their maximal coef in [1/strongcutmaxcoef,strongcutmaxcoef]
# [type: real, advanced: TRUE, range: [1,1e+20], default: 1000]
constraints/nonlinear/strongcutmaxcoef = 1000

# consider efficacy requirement when deciding whether a cut is "strong"
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/strongcutefficacy = FALSE

# whether to force "strong" cuts in enforcement
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/forcestrongcut = FALSE

# an expression will be enforced if the "auxiliary" violation is at least this factor times the "original" violation
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
constraints/nonlinear/enfoauxviolfactor = 0.01

# retry enfo of constraint with weak cuts if violation is least this factor of maximal violated constraints
# [type: real, advanced: TRUE, range: [0,2], default: 0.5]
constraints/nonlinear/weakcutminviolfactor = 0.5

# whether to make rows to be non-removable in the node where they are added (can prevent some cycling): 'o'ff, in 'e'nforcement only, 'a'lways
# [type: char, advanced: TRUE, range: {oea}, default: o]
constraints/nonlinear/rownotremovable = o

# method how to scale violations to make them comparable (not used for feasibility check): (n)one, (a)ctivity and side, norm of (g)radient
# [type: char, advanced: TRUE, range: {nag}, default: n]
constraints/nonlinear/violscale = n

# whether variables contained in a single constraint should be forced to be at their lower or upper bounds ('d'isable, change 't'ype, add 'b'ound disjunction)
# [type: char, advanced: TRUE, range: {bdt}, default: t]
constraints/nonlinear/checkvarlocks = t

# from which depth on in the tree to allow branching on auxiliary variables (variables added for extended formulation)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2147483647]
constraints/nonlinear/branching/aux = 2147483647

# whether to use external branching candidates and branching rules for branching
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/branching/external = FALSE

# consider a constraint highly violated if its violation is >= this factor * maximal violation among all constraints
# [type: real, advanced: FALSE, range: [0,1], default: 0]
constraints/nonlinear/branching/highviolfactor = 0

# consider a variable branching score high if its branching score >= this factor * maximal branching score among all variables
# [type: real, advanced: FALSE, range: [0,1], default: 0.9]
constraints/nonlinear/branching/highscorefactor = 0.9

# weight by how much to consider the violation assigned to a variable for its branching score
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1]
constraints/nonlinear/branching/violweight = 1

# weight by how much to consider fractionality of integer variables in branching score for spatial branching
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1]
constraints/nonlinear/branching/fracweight = 1

# weight by how much to consider the dual values of rows that contain a variable for its branching score
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0]
constraints/nonlinear/branching/dualweight = 0

# weight by how much to consider the pseudo cost of a variable for its branching score
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1]
constraints/nonlinear/branching/pscostweight = 1

# weight by how much to consider the domain width in branching score
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0]
constraints/nonlinear/branching/domainweight = 0

# weight by how much to consider variable type (continuous: 0, binary: 1, integer: 0.1, impl-integer: 0.01) in branching score
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.5]
constraints/nonlinear/branching/vartypeweight = 0.5

# how to aggregate several branching scores given for the same expression: 'a'verage, 'm'aximum, 's'um
# [type: char, advanced: FALSE, range: {ams}, default: s]
constraints/nonlinear/branching/scoreagg = s

# method used to split violation in expression onto variables: 'u'niform, 'm'idness of solution, 'd'omain width, 'l'ogarithmic domain width
# [type: char, advanced: FALSE, range: {umdl}, default: m]
constraints/nonlinear/branching/violsplit = m

# minimum pseudo-cost update count required to consider pseudo-costs reliable
# [type: real, advanced: FALSE, range: [0,1e+20], default: 2]
constraints/nonlinear/branching/pscostreliable = 2

# minimal average pseudo cost count for discrete variables at which to start considering spatial branching before branching on fractional integer variables
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1e+20]
constraints/nonlinear/branching/mixfractional = 1e+20

# whether tight linearizations of nonlinear constraints should be added to cutpool when some heuristics finds a new solution ('o'ff, on new 'i'ncumbents, on 'e'very solution)
# [type: char, advanced: FALSE, range: {oie}, default: o]
constraints/nonlinear/linearizeheursol = o

# whether to assume that any constraint is convex
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/nonlinear/assumeconvex = FALSE

# is statistics table <cons_nonlinear> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
table/cons_nonlinear/active = FALSE

# is statistics table <nlhdlr> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/nlhdlr/active = TRUE

# priority of conflict handler <linear>
# [type: int, advanced: TRUE, range: [-2147483648,2147483647], default: -1000000]
conflict/linear/priority = -1000000

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/linear/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/linear/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/linear/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/linear/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/linear/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 20]
constraints/linear/presoltiming = 20

# enable nonlinear upgrading for constraint handler <linear>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/upgrade/linear = TRUE

# multiplier on propagation frequency, how often the bounds are tightened (-1: never, 0: only at root)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 1]
constraints/linear/tightenboundsfreq = 1

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
constraints/linear/maxrounds = 5

# maximal number of separation rounds per node in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
constraints/linear/maxroundsroot = -1

# maximal number of cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 50]
constraints/linear/maxsepacuts = 50

# maximal number of cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 200]
constraints/linear/maxsepacutsroot = 200

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/presolusehashing = TRUE

# number for minimal pairwise presolve comparisons
# [type: int, advanced: TRUE, range: [1,2147483647], default: 200000]
constraints/linear/nmincomparisons = 200000

# minimal gain per minimal pairwise presolve comparisons to repeat pairwise comparison round
# [type: real, advanced: TRUE, range: [0,1], default: 1e-06]
constraints/linear/mingainpernmincomparisons = 1e-06

# maximal allowed relative gain in maximum norm for constraint aggregation (0.0: disable constraint aggregation)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
constraints/linear/maxaggrnormscale = 0

# maximum activity delta to run easy propagation on linear constraint (faster, but numerically less stable)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1000000]
constraints/linear/maxeasyactivitydelta = 1000000

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for separating knapsack cardinality cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0]
constraints/linear/maxcardbounddist = 0

# should all constraints be subject to cardinality cut generation instead of only the ones with non-zero dual value?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/separateall = FALSE

# should presolving search for aggregations in equations
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/aggregatevariables = TRUE

# should presolving try to simplify inequalities
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/simplifyinequalities = TRUE

# should dual presolving steps be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/dualpresolving = TRUE

# should stuffing of singleton continuous variables be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/singletonstuffing = TRUE

# should single variable stuffing be performed, which tries to fulfill constraints using the cheapest variable?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/singlevarstuffing = FALSE

# apply binaries sorting in decr. order of coeff abs value?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/sortvars = TRUE

# should the violation for a constraint with side 0.0 be checked relative to 1.0 (FALSE) or to the maximum absolute value in the activity (TRUE)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/checkrelmaxabs = FALSE

# should presolving try to detect constraints parallel to the objective function defining an upper bound and prevent these constraints from entering the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/detectcutoffbound = TRUE

# should presolving try to detect constraints parallel to the objective function defining a lower bound and prevent these constraints from entering the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/detectlowerbound = TRUE

# should presolving try to detect subsets of constraints parallel to the objective function?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/detectpartialobjective = TRUE

# should presolving and propagation try to improve bounds, detect infeasibility, and extract sub-constraints from ranged rows and equations?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/rangedrowpropagation = TRUE

# should presolving and propagation extract sub-constraints from ranged rows and equations?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/rangedrowartcons = TRUE

# maximum depth to apply ranged row propagation
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2147483647]
constraints/linear/rangedrowmaxdepth = 2147483647

# frequency for applying ranged row propagation
# [type: int, advanced: TRUE, range: [1,1073741822], default: 1]
constraints/linear/rangedrowfreq = 1

# should multi-aggregations only be performed if the constraint can be removed afterwards?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linear/multaggrremove = FALSE

# maximum coefficient dynamism (ie. maxabsval / minabsval) for primal multiaggregation
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1000]
constraints/linear/maxmultaggrquot = 1000

# maximum coefficient dynamism (ie. maxabsval / minabsval) for dual multiaggregation
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1e+20]
constraints/linear/maxdualmultaggrquot = 1e+20

# should Cliques be extracted?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/extractcliques = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/and/sepafreq = 1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/and/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/and/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/and/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/and/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/and/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/and/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 20]
constraints/and/presoltiming = 20

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/and/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/and/presolusehashing = TRUE

# should the AND-constraint get linearized and removed (in presolving)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/and/linearize = FALSE

# should cuts be separated during LP enforcing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/and/enforcecuts = TRUE

# should an aggregated linearization be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/and/aggrlinearization = FALSE

# should all binary resultant variables be upgraded to implicit binary variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/and/upgraderesultant = TRUE

# should dual presolving be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/and/dualpresolving = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/benders/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/benders/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/benders/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/benders/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/benders/maxprerounds = 0

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/benders/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/benders/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/benders/presoltiming = 4

# is the Benders' decomposition constraint handler active?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/benders/active = FALSE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/benderslp/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/benderslp/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/benderslp/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/benderslp/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/benderslp/maxprerounds = 0

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/benderslp/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/benderslp/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/benderslp/presoltiming = 28

# depth at which Benders' decomposition cuts are generated from the LP solution (-1: always, 0: only at root)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
constraints/benderslp/maxdepth = 0

# the depth frequency for generating LP cuts after the max depth is reached (0: never, 1: all nodes, ...)
# [type: int, advanced: TRUE, range: [0,1073741822], default: 0]
constraints/benderslp/depthfreq = 0

# the number of nodes processed without a dual bound improvement before enforcing the LP relaxation, 0: no stall count applied
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
constraints/benderslp/stalllimit = 100

# after the root node, only iterlimit fractional LP solutions are used at each node to generate Benders' decomposition cuts.
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
constraints/benderslp/iterlimit = 100

# is the Benders' decomposition LP cut constraint handler active?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/benderslp/active = FALSE

# maximal percantage of continuous variables within a conflict
# [type: real, advanced: FALSE, range: [0,1], default: 0.4]
conflict/bounddisjunction/continuousfrac = 0.4

# priority of conflict handler <bounddisjunction>
# [type: int, advanced: TRUE, range: [-2147483648,2147483647], default: -3000000]
conflict/bounddisjunction/priority = -3000000

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/bounddisjunction/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/bounddisjunction/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/bounddisjunction/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/bounddisjunction/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/bounddisjunction/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/bounddisjunction/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/bounddisjunction/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/bounddisjunction/presoltiming = 4

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
constraints/cardinality/sepafreq = 10

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/cardinality/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/cardinality/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/cardinality/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/cardinality/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cardinality/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cardinality/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/cardinality/presoltiming = 4

# whether to use balanced instead of unbalanced branching
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cardinality/branchbalanced = FALSE

# maximum depth for using balanced branching (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 20]
constraints/cardinality/balanceddepth = 20

# determines that balanced branching is only used if the branching cut off value w.r.t. the current LP solution is greater than a given value
# [type: real, advanced: TRUE, range: [0.01,1.79769313486232e+308], default: 2]
constraints/cardinality/balancedcutoff = 2

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/conjunction/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/conjunction/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/conjunction/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/conjunction/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/conjunction/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/conjunction/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/conjunction/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/conjunction/presoltiming = 4

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/countsols/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/countsols/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/countsols/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/countsols/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/countsols/maxprerounds = 0

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/countsols/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/countsols/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/countsols/presoltiming = 28

# is the constraint handler active?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/countsols/active = FALSE

# should the sparse solution test be turned on?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/countsols/sparsetest = TRUE

# is it allowed to discard solutions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/countsols/discardsols = TRUE

# should the solutions be collected?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/countsols/collect = FALSE

# counting stops, if the given number of solutions were found (-1: no limit)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: -1]
constraints/countsols/sollimit = -1

# display activation status of display column <sols> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
display/sols/active = 0

# display activation status of display column <feasST> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
display/feasST/active = 0

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/cumulative/sepafreq = 1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/cumulative/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/cumulative/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/cumulative/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/cumulative/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/cumulative/presoltiming = 28

# should time-table (core-times) propagator be used to infer bounds?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/ttinfer = TRUE

# should edge-finding be used to detect an overload?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/efcheck = FALSE

# should edge-finding be used to infer bounds?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/efinfer = FALSE

# should edge-finding be executed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/useadjustedjobs = FALSE

# should time-table edge-finding be used to detect an overload?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/ttefcheck = TRUE

# should time-table edge-finding be used to infer bounds?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/ttefinfer = TRUE

# should the binary representation be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/usebinvars = FALSE

# should cuts be added only locally?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/localcuts = FALSE

# should covering cuts be added every node?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/usecovercuts = TRUE

# should the cumulative constraint create cuts as knapsack constraints?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/cutsasconss = TRUE

# shall old sepa algo be applied?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/sepaold = TRUE

# should branching candidates be added to storage?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/fillbranchcands = FALSE

# should dual presolving be applied?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/dualpresolve = TRUE

# should coefficient tightening be applied?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/cumulative/coeftightening = FALSE

# should demands and capacity be normalized?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/normalize = TRUE

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/presolpairwise = TRUE

# extract disjunctive constraints?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/disjunctive = TRUE

# number of branch-and-bound nodes to solve an independent cumulative constraint (-1: no limit)?
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: 10000]
constraints/cumulative/maxnodes = 10000

# search for conflict set via maximal cliques to detect disjunctive constraints
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/detectdisjunctive = TRUE

# search for conflict set via maximal cliques to detect variable bound constraints
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/detectvarbounds = TRUE

# should bound widening be used during the conflict analysis?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/cumulative/usebdwidening = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/disjunction/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/disjunction/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/disjunction/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/disjunction/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/disjunction/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/disjunction/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/disjunction/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/disjunction/presoltiming = 4

# alawys perform branching if one of the constraints is violated, otherwise only if all integers are fixed
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/disjunction/alwaysbranch = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/fixedvar/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/fixedvar/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/fixedvar/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/fixedvar/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/fixedvar/maxprerounds = 0

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/fixedvar/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/fixedvar/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/fixedvar/presoltiming = 28

# whether to check and enforce bounds on fixed variables
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/fixedvar/enabled = TRUE

# whether to act on subSCIPs
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/fixedvar/subscips = TRUE

# whether to prefer separation over tightening LP feastol in enforcement
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/fixedvar/prefercut = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
constraints/indicator/sepafreq = 10

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/indicator/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/indicator/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/indicator/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/indicator/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/indicator/presoltiming = 4

# enable linear upgrading for constraint handler <indicator>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/indicator = TRUE

# priority of conflict handler <indicatorconflict>
# [type: int, advanced: TRUE, range: [-2147483648,2147483647], default: 200000]
conflict/indicatorconflict/priority = 200000

# Branch on indicator constraints in enforcing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/branchindicators = FALSE

# Generate logicor constraints instead of cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/genlogicor = FALSE

# Add coupling constraints or rows if big-M is small enough?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/indicator/addcoupling = TRUE

# maximum coefficient for binary variable in coupling constraint
# [type: real, advanced: TRUE, range: [0,1000000000], default: 10000]
constraints/indicator/maxcouplingvalue = 10000

# Add initial variable upper bound constraints, if 'addcoupling' is true?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/addcouplingcons = FALSE

# Should the coupling inequalities be separated dynamically?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/indicator/sepacouplingcuts = TRUE

# Allow to use local bounds in order to separate coupling inequalities?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/sepacouplinglocal = FALSE

# maximum coefficient for binary variable in separated coupling constraint
# [type: real, advanced: TRUE, range: [0,1000000000], default: 10000]
constraints/indicator/sepacouplingvalue = 10000

# Separate cuts based on perspective formulation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/sepaperspective = FALSE

# Allow to use local bounds in order to separate perspective cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/indicator/sepapersplocal = TRUE

# maximal number of separated non violated IISs, before separation is stopped
# [type: int, advanced: FALSE, range: [0,2147483647], default: 3]
constraints/indicator/maxsepanonviolated = 3

# Update bounds of original variables for separation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/updatebounds = FALSE

# maximum estimated condition of the solution basis matrix of the alternative LP to be trustworthy (0.0 to disable check)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
constraints/indicator/maxconditionaltlp = 0

# maximal number of cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
constraints/indicator/maxsepacuts = 100

# maximal number of cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2000]
constraints/indicator/maxsepacutsroot = 2000

# Remove indicator constraint if corresponding variable bound constraint has been added?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/removeindicators = FALSE

# Do not generate indicator constraint, but a bilinear constraint instead?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/generatebilinear = FALSE

# Scale slack variable coefficient at construction time?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/scaleslackvar = FALSE

# Try to make solutions feasible by setting indicator variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/indicator/trysolutions = TRUE

# In enforcing try to generate cuts (only if sepaalternativelp is true)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/enforcecuts = FALSE

# Should dual reduction steps be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/indicator/dualreductions = TRUE

# Add opposite inequality in nodes in which the binary variable has been fixed to 0?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/addopposite = FALSE

# Try to upgrade bounddisjunction conflicts by replacing slack variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/conflictsupgrade = FALSE

# fraction of binary variables that need to be fixed before restart occurs (in forcerestart)
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
constraints/indicator/restartfrac = 0.9

# Collect other constraints to alternative LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/useotherconss = FALSE

# Use objective cut with current best solution to alternative LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/useobjectivecut = FALSE

# Try to construct a feasible solution from a cover?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/trysolfromcover = FALSE

# Try to upgrade linear constraints to indicator constraints?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/upgradelinear = FALSE

# Separate using the alternative LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/sepaalternativelp = FALSE

# Force restart if absolute gap is 1 or enough binary variables have been fixed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/forcerestart = FALSE

# Decompose problem (do not generate linear constraint if all variables are continuous)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/indicator/nolinconscont = FALSE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/integral/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/integral/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/integral/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/integral/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/integral/maxprerounds = 0

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/integral/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/integral/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/integral/presoltiming = 28

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/knapsack/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/knapsack/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/knapsack/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/knapsack/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/knapsack/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/knapsack/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/knapsack/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/knapsack/presoltiming = 28

# enable linear upgrading for constraint handler <knapsack>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/knapsack = TRUE

# multiplier on separation frequency, how often knapsack cuts are separated (-1: never, 0: only at root)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 1]
constraints/knapsack/sepacardfreq = 1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for separating knapsack cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0]
constraints/knapsack/maxcardbounddist = 0

# lower clique size limit for greedy clique extraction algorithm (relative to largest clique)
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
constraints/knapsack/cliqueextractfactor = 0.5

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
constraints/knapsack/maxrounds = 5

# maximal number of separation rounds per node in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
constraints/knapsack/maxroundsroot = -1

# maximal number of cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 50]
constraints/knapsack/maxsepacuts = 50

# maximal number of cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 200]
constraints/knapsack/maxsepacutsroot = 200

# should disaggregation of knapsack constraints be allowed in preprocessing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/disaggregation = TRUE

# should presolving try to simplify knapsacks
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/simplifyinequalities = TRUE

# should negated clique information be used in solving process
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/negatedclique = TRUE

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/presolusehashing = TRUE

# should dual presolving steps be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/dualpresolving = TRUE

# should GUB information be used for separation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/knapsack/usegubs = FALSE

# should presolving try to detect constraints parallel to the objective function defining an upper bound and prevent these constraints from entering the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/detectcutoffbound = TRUE

# should presolving try to detect constraints parallel to the objective function defining a lower bound and prevent these constraints from entering the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/knapsack/detectlowerbound = TRUE

# should clique partition information be updated when old partition seems outdated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/knapsack/updatecliquepartitions = FALSE

# factor on the growth of global cliques to decide when to update a previous (negated) clique partition (used only if updatecliquepartitions is set to TRUE)
# [type: real, advanced: TRUE, range: [1,10], default: 1.5]
constraints/knapsack/clqpartupdatefac = 1.5

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/linking/sepafreq = 1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/linking/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/linking/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/linking/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/linking/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linking/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/linking/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/linking/presoltiming = 8

# this constraint will not propagate or separate, linear and setppc are used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/linking/linearize = FALSE

# priority of conflict handler <logicor>
# [type: int, advanced: TRUE, range: [-2147483648,2147483647], default: 800000]
conflict/logicor/priority = 800000

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/logicor/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/logicor/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/logicor/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/logicor/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/logicor/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/logicor/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/logicor/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/logicor/presoltiming = 28

# enable linear upgrading for constraint handler <logicor>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/logicor = TRUE

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/presolusehashing = TRUE

# should dual presolving steps be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/dualpresolving = TRUE

# should negated clique information be used in presolving
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/negatedclique = TRUE

# should implications/cliques be used in presolving
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/implications = TRUE

# should pairwise constraint comparison try to strengthen constraints by removing superflous non-zeros?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/logicor/strengthen = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/or/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/or/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/or/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/or/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/or/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/or/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/or/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/or/presoltiming = 8

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
constraints/orbisack/sepafreq = 5

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
constraints/orbisack/propfreq = 5

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/orbisack/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/orbisack/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/orbisack/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbisack/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbisack/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
constraints/orbisack/presoltiming = 16

# Separate cover inequalities for orbisacks?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/orbisack/coverseparation = TRUE

# Separate orbisack inequalities?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbisack/orbiSeparation = FALSE

# Maximum size of coefficients for orbisack inequalities
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1000000]
constraints/orbisack/coeffbound = 1000000

# Upgrade orbisack constraints to packing/partioning orbisacks?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/orbisack/checkpporbisack = TRUE

# Whether orbisack constraints should be forced to be copied to sub SCIPs.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbisack/forceconscopy = FALSE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/orbitope/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/orbitope/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/orbitope/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/orbitope/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/orbitope/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbitope/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbitope/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/orbitope/presoltiming = 8

# Strengthen orbitope constraints to packing/partioning orbitopes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/orbitope/checkpporbitope = TRUE

# Whether we separate inequalities for full orbitopes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbitope/sepafullorbitope = FALSE

# Whether orbitope constraints should be forced to be copied to sub SCIPs.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/orbitope/forceconscopy = FALSE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/pseudoboolean/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/pseudoboolean/propfreq = -1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/pseudoboolean/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/pseudoboolean/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/pseudoboolean/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/pseudoboolean/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/pseudoboolean/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/pseudoboolean/presoltiming = 8

# decompose all normal pseudo boolean constraint into a "linear" constraint and "and" constraints
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/pseudoboolean/decomposenormal = FALSE

# decompose all indicator pseudo boolean constraint into a "linear" constraint and "and" constraints
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/pseudoboolean/decomposeindicator = TRUE

# should the nonlinear constraints be separated during LP processing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/pseudoboolean/nlcseparate = TRUE

# should the nonlinear constraints be propagated during node processing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/pseudoboolean/nlcpropagate = TRUE

# should the nonlinear constraints be removable?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/pseudoboolean/nlcremovable = TRUE

# priority of conflict handler <setppc>
# [type: int, advanced: TRUE, range: [-2147483648,2147483647], default: 700000]
conflict/setppc/priority = 700000

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/setppc/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/setppc/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/setppc/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/setppc/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/setppc/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/setppc/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/setppc/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/setppc/presoltiming = 28

# enable linear upgrading for constraint handler <setppc>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/setppc = TRUE

# enable nonlinear upgrading for constraint handler <setppc>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/nonlinear/upgrade/setppc = TRUE

# number of children created in pseudo branching (0: disable pseudo branching)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2]
constraints/setppc/npseudobranches = 2

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/setppc/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/setppc/presolusehashing = TRUE

# should dual presolving steps be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/setppc/dualpresolving = TRUE

#  should we try to lift variables into other clique constraints, fix variables, aggregate them, and also shrink the amount of variables in clique constraints
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/setppc/cliquelifting = FALSE

# should we try to generate extra cliques out of all binary variables to maybe fasten redundant constraint detection
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/setppc/addvariablesascliques = FALSE

# should we try to shrink the number of variables in a clique constraints, by replacing more than one variable by only one
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/setppc/cliqueshrinking = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
constraints/SOS1/sepafreq = 10

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/SOS1/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/SOS1/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/SOS1/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/SOS1/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/SOS1/presoltiming = 8

# do not create an adjacency matrix if number of SOS1 variables is larger than predefined value (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10000]
constraints/SOS1/maxsosadjacency = 10000

# maximal number of extensions that will be computed for each SOS1 constraint  (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1]
constraints/SOS1/maxextensions = 1

# maximal number of bound tightening rounds per presolving round (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 5]
constraints/SOS1/maxtightenbds = 5

# if TRUE then perform implication graph analysis (might add additional SOS1 constraints)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/perfimplanalysis = FALSE

# number of recursive calls of implication graph analysis (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/SOS1/depthimplanalysis = -1

# whether to use conflict graph propagation
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/conflictprop = TRUE

# whether to use implication graph propagation
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/implprop = TRUE

# whether to use SOS1 constraint propagation
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/sosconsprop = FALSE

# which branching rule should be applied ? ('n': neighborhood, 'b': bipartite, 's': SOS1/clique) (note: in some cases an automatic switching to SOS1 branching is possible)
# [type: char, advanced: TRUE, range: {nbs}, default: n]
constraints/SOS1/branchingrule = n

# if TRUE then automatically switch to SOS1 branching if the SOS1 constraints do not overlap
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/autosos1branch = TRUE

# if neighborhood branching is used, then fix the branching variable (if positive in sign) to the value of the feasibility tolerance
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/fixnonzero = FALSE

# if TRUE then add complementarity constraints to the branching nodes (can be used in combination with neighborhood or bipartite branching)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/addcomps = FALSE

# maximal number of complementarity constraints added per branching node (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/SOS1/maxaddcomps = -1

# minimal feasibility value for complementarity constraints in order to be added to the branching node
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: -0.6]
constraints/SOS1/addcompsfeas = -0.6

# minimal feasibility value for bound inequalities in order to be added to the branching node
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
constraints/SOS1/addbdsfeas = 1

# should added complementarity constraints be extended to SOS1 constraints to get tighter bound inequalities
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/addextendedbds = TRUE

# Use SOS1 branching in enforcing (otherwise leave decision to branching rules)? This value can only be set to false if all SOS1 variables are binary
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/branchsos = TRUE

# Branch on SOS constraint with most number of nonzeros?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/branchnonzeros = FALSE

# Branch on SOS cons. with highest nonzero-variable weight for branching (needs branchnonzeros = false)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/branchweight = FALSE

# only add complementarity constraints to branching nodes for predefined depth (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 30]
constraints/SOS1/addcompsdepth = 30

# maximal number of strong branching rounds to perform for each node (-1: auto); only available for neighborhood and bipartite branching
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
constraints/SOS1/nstrongrounds = 0

# maximal number LP iterations to perform for each strong branching round (-2: auto, -1: no limit)
# [type: int, advanced: TRUE, range: [-2,2147483647], default: 10000]
constraints/SOS1/nstrongiter = 10000

# if TRUE separate bound inequalities from initial SOS1 constraints
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS1/boundcutsfromsos1 = FALSE

# if TRUE separate bound inequalities from the conflict graph
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/boundcutsfromgraph = TRUE

# if TRUE then automatically switch to separating initial SOS1 constraints if the SOS1 constraints do not overlap
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/autocutsfromsos1 = TRUE

# frequency for separating bound cuts; zero means to separate only in the root node
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 10]
constraints/SOS1/boundcutsfreq = 10

# node depth of separating bound cuts (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 40]
constraints/SOS1/boundcutsdepth = 40

# maximal number of bound cuts separated per branching node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
constraints/SOS1/maxboundcuts = 50

# maximal number of bound cuts separated per iteration in the root node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 150]
constraints/SOS1/maxboundcutsroot = 150

# if TRUE then bound cuts are strengthened in case bound variables are available
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/SOS1/strthenboundcuts = TRUE

# frequency for separating implied bound cuts; zero means to separate only in the root node
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
constraints/SOS1/implcutsfreq = 0

# node depth of separating implied bound cuts (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 40]
constraints/SOS1/implcutsdepth = 40

# maximal number of implied bound cuts separated per branching node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
constraints/SOS1/maximplcuts = 50

# maximal number of implied bound cuts separated per iteration in the root node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 150]
constraints/SOS1/maximplcutsroot = 150

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/SOS2/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/SOS2/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/SOS2/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/SOS2/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/SOS2/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS2/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/SOS2/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
constraints/SOS2/presoltiming = 4

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/superindicator/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/superindicator/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/superindicator/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/superindicator/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/superindicator/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/superindicator/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/superindicator/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
constraints/superindicator/presoltiming = 8

# should type of slack constraint be checked when creating superindicator constraint?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/superindicator/checkslacktype = TRUE

# maximum big-M coefficient of binary variable in upgrade to a linear constraint (relative to smallest coefficient)
# [type: real, advanced: TRUE, range: [0,1e+15], default: 10000]
constraints/superindicator/maxupgdcoeflinear = 10000

# priority for upgrading to an indicator constraint (-1: never)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1]
constraints/superindicator/upgdprioindicator = 1

# priority for upgrading to an indicator constraint (-1: never)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 2]
constraints/superindicator/upgdpriolinear = 2

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
constraints/symresack/sepafreq = 5

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
constraints/symresack/propfreq = 5

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/symresack/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/symresack/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/symresack/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/symresack/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/symresack/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
constraints/symresack/presoltiming = 16

# Upgrade symresack constraints to packing/partioning symresacks?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/symresack/ppsymresack = TRUE

# Check whether permutation is monotone when upgrading to packing/partioning symresacks?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/symresack/checkmonotonicity = TRUE

# Whether symresack constraints should be forced to be copied to sub SCIPs.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/symresack/forceconscopy = FALSE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/varbound/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/varbound/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/varbound/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/varbound/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/varbound/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/varbound/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/varbound/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 12]
constraints/varbound/presoltiming = 12

# enable linear upgrading for constraint handler <varbound>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/varbound = TRUE

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/varbound/presolpairwise = TRUE

# maximum coefficient in varbound constraint to be added as a row into LP
# [type: real, advanced: TRUE, range: [0,1e+20], default: 1000000000]
constraints/varbound/maxlpcoef = 1000000000

# should bound widening be used in conflict analysis?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/varbound/usebdwidening = TRUE

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
constraints/xor/sepafreq = 0

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/xor/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/xor/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 100]
constraints/xor/eagerfreq = 100

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/xor/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/xor/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/xor/delayprop = FALSE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 28]
constraints/xor/presoltiming = 28

# enable linear upgrading for constraint handler <xor>
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
constraints/linear/upgrade/xor = TRUE

# should pairwise constraint comparison be performed in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/xor/presolpairwise = TRUE

# should hash table be used for detecting redundant constraints in advance?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/xor/presolusehashing = TRUE

# should the extended formulation be added in presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/xor/addextendedform = FALSE

# should the extended flow formulation be added (nonsymmetric formulation otherwise)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/xor/addflowextended = FALSE

# should parity inequalities be separated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/xor/separateparity = FALSE

# frequency for applying the Gauss propagator
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 5]
constraints/xor/gausspropfreq = 5

# frequency for separating cuts (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
constraints/components/sepafreq = -1

# frequency for propagating domains (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
constraints/components/propfreq = 1

# timing when constraint propagation should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS)
# [type: int, advanced: TRUE, range: [1,15], default: 1]
constraints/components/proptiming = 1

# frequency for using all instead of only the useful constraints in separation, propagation and enforcement (-1: never, 0: only in first evaluation)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
constraints/components/eagerfreq = -1

# maximal number of presolving rounds the constraint handler participates in (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
constraints/components/maxprerounds = -1

# should separation method be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
constraints/components/delaysepa = FALSE

# should propagation method be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
constraints/components/delayprop = TRUE

# timing mask of the constraint handler's presolving method (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 32]
constraints/components/presoltiming = 32

# maximum depth of a node to run components detection (-1: disable component detection during solving)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
constraints/components/maxdepth = -1

# maximum number of integer (or binary) variables to solve a subproblem during presolving (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 500]
constraints/components/maxintvars = 500

# minimum absolute size (in terms of variables) to solve a component individually during branch-and-bound
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
constraints/components/minsize = 50

# minimum relative size (in terms of variables) to solve a component individually during branch-and-bound
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
constraints/components/minrelsize = 0.1

# maximum number of nodes to be solved in subproblems during presolving
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: 10000]
constraints/components/nodelimit = 10000

# the weight of an integer variable compared to binary variables
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1]
constraints/components/intfactor = 1

# factor to increase the feasibility tolerance of the main SCIP in all sub-SCIPs, default value 1.0
# [type: real, advanced: TRUE, range: [0,1000000], default: 1]
constraints/components/feastolfactor = 1

# should possible "and" constraint be linearized when writing the mps file?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reading/mpsreader/linearize-and-constraints = TRUE

# should an aggregated linearization for and constraints be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reading/mpsreader/aggrlinearization-ands = TRUE

# should possible "and" constraint be linearized when writing the lp file?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reading/lpreader/linearize-and-constraints = TRUE

# should an aggregated linearization for and constraints be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
reading/lpreader/aggrlinearization-ands = TRUE

# shall characters '#', '*', '+', '/', and '-' in variable and constraint names be replaced by '_'?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/gmsreader/replaceforbiddenchars = FALSE

# default M value for big-M reformulation of indicator constraints in case no bound on slack variable is given
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1000000]
reading/gmsreader/bigmdefault = 1000000

# which reformulation to use for indicator constraints: 'b'ig-M, 's'os1
# [type: char, advanced: FALSE, range: {bs}, default: s]
reading/gmsreader/indicatorreform = s

# is it allowed to use the gams function signpower(x,a)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/gmsreader/signpower = FALSE

# should model constraints be subject to aging?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/opbreader/dynamicconss = FALSE

# use '*' between coefficients and variables by writing to problem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
reading/opbreader/multisymbol = FALSE

# should an artificial objective, depending on the number of clauses a variable appears in, be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/cnfreader/useobj = FALSE

# should fixed and aggregated variables be printed (if not, re-parsing might fail)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/cipreader/writefixedvars = TRUE

# should Benders' decomposition be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/storeader/usebenders = FALSE

# only use improving bounds
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
reading/bndreader/improveonly = FALSE

# should the coloring values be relativ or absolute
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/ppmreader/rgbrelativ = TRUE

# should the output format be binary(P6) (otherwise plain(P3) format)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/ppmreader/rgbascii = TRUE

# splitting coefficients in this number of intervals
# [type: int, advanced: FALSE, range: [3,16], default: 3]
reading/ppmreader/coefficientlimit = 3

# maximal color value
# [type: int, advanced: FALSE, range: [0,255], default: 160]
reading/ppmreader/rgblimit = 160

# should the output format be binary(P4) (otherwise plain(P1) format)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
reading/pbmreader/binary = TRUE

# maximum number of rows in the scaled picture (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1000]
reading/pbmreader/maxrows = 1000

# maximum number of columns in the scaled picture (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1000]
reading/pbmreader/maxcols = 1000

# priority of presolver <boundshift>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 7900000]
presolving/boundshift/priority = 7900000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/boundshift/maxrounds = 0

# timing mask of presolver <boundshift> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
presolving/boundshift/timing = 4

# absolute value of maximum shift
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 9223372036854775807]
presolving/boundshift/maxshift = 9223372036854775807

# is flipping allowed (multiplying with -1)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/boundshift/flipping = TRUE

# shift only integer ranges?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/boundshift/integer = TRUE

# priority of presolver <convertinttobin>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 6000000]
presolving/convertinttobin/priority = 6000000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/convertinttobin/maxrounds = 0

# timing mask of presolver <convertinttobin> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
presolving/convertinttobin/timing = 4

# absolute value of maximum domain size for converting an integer variable to binaries variables
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 9223372036854775807]
presolving/convertinttobin/maxdomainsize = 9223372036854775807

# should only integer variables with a domain size of 2^p - 1 be converted(, there we don't need an knapsack-constraint for restricting the sum of the binaries)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/convertinttobin/onlypoweroftwo = FALSE

# should only integer variables with uplocks equals downlocks be converted
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/convertinttobin/samelocksinbothdirections = FALSE

# priority of presolver <domcol>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000]
presolving/domcol/priority = -1000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/domcol/maxrounds = -1

# timing mask of presolver <domcol> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/domcol/timing = 16

# minimal number of pair comparisons
# [type: int, advanced: FALSE, range: [100,1048576], default: 1024]
presolving/domcol/numminpairs = 1024

# maximal number of pair comparisons
# [type: int, advanced: FALSE, range: [1024,1000000000], default: 1048576]
presolving/domcol/nummaxpairs = 1048576

# should predictive bound strengthening be applied?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
presolving/domcol/predbndstr = FALSE

# should reductions for continuous variables be performed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
presolving/domcol/continuousred = TRUE

# priority of presolver <dualagg>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -12000]
presolving/dualagg/priority = -12000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/dualagg/maxrounds = 0

# timing mask of presolver <dualagg> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/dualagg/timing = 16

# priority of presolver <dualcomp>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -50]
presolving/dualcomp/priority = -50

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/dualcomp/maxrounds = -1

# timing mask of presolver <dualcomp> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/dualcomp/timing = 16

# should only discrete variables be compensated?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
presolving/dualcomp/componlydisvars = FALSE

# priority of presolver <dualinfer>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -3000]
presolving/dualinfer/priority = -3000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/dualinfer/maxrounds = 0

# timing mask of presolver <dualinfer> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/dualinfer/timing = 16

# use convex combination of columns for determining dual bounds
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
presolving/dualinfer/twocolcombine = TRUE

# maximal number of dual bound strengthening loops
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 12]
presolving/dualinfer/maxdualbndloops = 12

# maximal number of considered non-zeros within one column (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 100]
presolving/dualinfer/maxconsiderednonzeros = 100

# maximal number of consecutive useless hashtable retrieves
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1000]
presolving/dualinfer/maxretrievefails = 1000

# maximal number of consecutive useless column combines
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1000]
presolving/dualinfer/maxcombinefails = 1000

# Maximum number of hashlist entries as multiple of number of columns in the problem (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
presolving/dualinfer/maxhashfac = 10

# Maximum number of processed column pairs as multiple of the number of columns in the problem (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1]
presolving/dualinfer/maxpairfac = 1

# Maximum number of row's non-zeros for changing inequality to equality
# [type: int, advanced: FALSE, range: [2,2147483647], default: 3]
presolving/dualinfer/maxrowsupport = 3

# priority of presolver <gateextraction>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 1000000]
presolving/gateextraction/priority = 1000000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/gateextraction/maxrounds = -1

# timing mask of presolver <gateextraction> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/gateextraction/timing = 16

# should we only try to extract set-partitioning constraints and no and-constraints
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/gateextraction/onlysetpart = FALSE

# should we try to extract set-partitioning constraint out of one logicor and one corresponding set-packing constraint
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/gateextraction/searchequations = TRUE

# order logicor contraints to extract big-gates before smaller ones (-1), do not order them (0) or order them to extract smaller gates at first (1)
# [type: int, advanced: TRUE, range: [-1,1], default: 1]
presolving/gateextraction/sorting = 1

# priority of presolver <implics>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -10000]
presolving/implics/priority = -10000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/implics/maxrounds = -1

# timing mask of presolver <implics> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
presolving/implics/timing = 8

# priority of presolver <inttobinary>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 7000000]
presolving/inttobinary/priority = 7000000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/inttobinary/maxrounds = -1

# timing mask of presolver <inttobinary> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
presolving/inttobinary/timing = 4

# priority of presolver <qpkktref>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1]
presolving/qpkktref/priority = -1

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/qpkktref/maxrounds = 0

# timing mask of presolver <qpkktref> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 8]
presolving/qpkktref/timing = 8

# if TRUE then allow binary variables for KKT update
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/qpkktref/addkktbinary = FALSE

# if TRUE then only apply the update to QPs with bounded variables; if the variables are not bounded then a finite optimal solution might not exist and the KKT conditions would then be invalid
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/qpkktref/updatequadbounded = TRUE

# if TRUE then apply quadratic constraint update even if the quadratic constraint matrix is known to be indefinite
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/qpkktref/updatequadindef = FALSE

# priority of presolver <redvub>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -9000000]
presolving/redvub/priority = -9000000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/redvub/maxrounds = 0

# timing mask of presolver <redvub> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/redvub/timing = 16

# priority of presolver <trivial>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 9000000]
presolving/trivial/priority = 9000000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/trivial/maxrounds = -1

# timing mask of presolver <trivial> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 4]
presolving/trivial/timing = 4

# priority of presolver <tworowbnd>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2000]
presolving/tworowbnd/priority = -2000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/tworowbnd/maxrounds = 0

# timing mask of presolver <tworowbnd> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/tworowbnd/timing = 16

# should tworowbnd presolver be copied to sub-SCIPs?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/tworowbnd/enablecopy = TRUE

# maximal number of considered non-zeros within one row (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
presolving/tworowbnd/maxconsiderednonzeros = 100

# maximal number of consecutive useless hashtable retrieves
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1000]
presolving/tworowbnd/maxretrievefails = 1000

# maximal number of consecutive useless row combines
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1000]
presolving/tworowbnd/maxcombinefails = 1000

# Maximum number of hashlist entries as multiple of number of rows in the problem (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
presolving/tworowbnd/maxhashfac = 10

# Maximum number of processed row pairs as multiple of the number of rows in the problem (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
presolving/tworowbnd/maxpairfac = 1

# priority of presolver <sparsify>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -24000]
presolving/sparsify/priority = -24000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/sparsify/maxrounds = -1

# timing mask of presolver <sparsify> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/sparsify/timing = 16

# should sparsify presolver be copied to sub-SCIPs?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/sparsify/enablecopy = TRUE

# should we cancel nonzeros in constraints of the linear constraint handler?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/sparsify/cancellinear = TRUE

# should we forbid cancellations that destroy integer coefficients?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/sparsify/preserveintcoefs = TRUE

# maximal fillin for continuous variables (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/sparsify/maxcontfillin = 0

# maximal fillin for binary variables (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/sparsify/maxbinfillin = 0

# maximal fillin for integer variables including binaries (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/sparsify/maxintfillin = 0

# maximal support of one equality to be used for cancelling (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
presolving/sparsify/maxnonzeros = -1

# maximal number of considered non-zeros within one row (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 70]
presolving/sparsify/maxconsiderednonzeros = 70

# order in which to process inequalities ('n'o sorting, 'i'ncreasing nonzeros, 'd'ecreasing nonzeros)
# [type: char, advanced: TRUE, range: {nid}, default: d]
presolving/sparsify/rowsort = d

# limit on the number of useless vs. useful hashtable retrieves as a multiple of the number of constraints
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 100]
presolving/sparsify/maxretrievefac = 100

# number of calls to wait until next execution as a multiple of the number of useless calls
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
presolving/sparsify/waitingfac = 2

# priority of presolver <dualsparsify>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -240000]
presolving/dualsparsify/priority = -240000

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
presolving/dualsparsify/maxrounds = -1

# timing mask of presolver <dualsparsify> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/dualsparsify/timing = 16

# should dualsparsify presolver be copied to sub-SCIPs?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
presolving/dualsparsify/enablecopy = TRUE

# should we forbid cancellations that destroy integer coefficients?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/dualsparsify/preserveintcoefs = FALSE

# should we preserve good locked properties of variables (at most one lock in one direction)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
presolving/dualsparsify/preservegoodlocks = FALSE

# maximal fillin for continuous variables (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
presolving/dualsparsify/maxcontfillin = 1

# maximal fillin for binary variables (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
presolving/dualsparsify/maxbinfillin = 1

# maximal fillin for integer variables including binaries (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
presolving/dualsparsify/maxintfillin = 1

# maximal number of considered nonzeros within one column (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 70]
presolving/dualsparsify/maxconsiderednonzeros = 70

# minimal eliminated nonzeros within one column if we need to add a constraint to the problem
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
presolving/dualsparsify/mineliminatednonzeros = 100

# limit on the number of useless vs. useful hashtable retrieves as a multiple of the number of constraints
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 100]
presolving/dualsparsify/maxretrievefac = 100

# number of calls to wait until next execution as a multiple of the number of useless calls
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
presolving/dualsparsify/waitingfac = 2

# priority of presolver <stuffing>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100]
presolving/stuffing/priority = -100

# maximal number of presolving rounds the presolver participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
presolving/stuffing/maxrounds = 0

# timing mask of presolver <stuffing> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [4,60], default: 16]
presolving/stuffing/timing = 16

# priority of node selection rule <bfs> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 100000]
nodeselection/bfs/stdpriority = 100000

# priority of node selection rule <bfs> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
nodeselection/bfs/memsavepriority = 0

# minimal plunging depth, before new best node may be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/bfs/minplungedepth = -1

# maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/bfs/maxplungedepth = -1

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where plunging is performed
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.25]
nodeselection/bfs/maxplungequot = 0.25

# priority of node selection rule <breadthfirst> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: -10000]
nodeselection/breadthfirst/stdpriority = -10000

# priority of node selection rule <breadthfirst> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000000]
nodeselection/breadthfirst/memsavepriority = -1000000

# priority of node selection rule <dfs> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 0]
nodeselection/dfs/stdpriority = 0

# priority of node selection rule <dfs> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 100000]
nodeselection/dfs/memsavepriority = 100000

# priority of node selection rule <estimate> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 200000]
nodeselection/estimate/stdpriority = 200000

# priority of node selection rule <estimate> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 100]
nodeselection/estimate/memsavepriority = 100

# minimal plunging depth, before new best node may be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/estimate/minplungedepth = -1

# maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/estimate/maxplungedepth = -1

# maximal quotient (estimate - lowerbound)/(cutoffbound - lowerbound) where plunging is performed
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.25]
nodeselection/estimate/maxplungequot = 0.25

# frequency at which the best node instead of the best estimate is selected (0: never)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
nodeselection/estimate/bestnodefreq = 10

# depth until breadth-first search is applied
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
nodeselection/estimate/breadthfirstdepth = -1

# number of nodes before doing plunging the first time
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
nodeselection/estimate/plungeoffset = 0

# priority of node selection rule <hybridestim> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 50000]
nodeselection/hybridestim/stdpriority = 50000

# priority of node selection rule <hybridestim> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 50]
nodeselection/hybridestim/memsavepriority = 50

# minimal plunging depth, before new best node may be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/hybridestim/minplungedepth = -1

# maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
nodeselection/hybridestim/maxplungedepth = -1

# maximal quotient (estimate - lowerbound)/(cutoffbound - lowerbound) where plunging is performed
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.25]
nodeselection/hybridestim/maxplungequot = 0.25

# frequency at which the best node instead of the hybrid best estimate / best bound is selected (0: never)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
nodeselection/hybridestim/bestnodefreq = 1000

# weight of estimate value in node selection score (0: pure best bound search, 1: pure best estimate search)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
nodeselection/hybridestim/estimweight = 0.1

# priority of node selection rule <restartdfs> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 10000]
nodeselection/restartdfs/stdpriority = 10000

# priority of node selection rule <restartdfs> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 50000]
nodeselection/restartdfs/memsavepriority = 50000

# frequency for selecting the best node instead of the deepest one
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
nodeselection/restartdfs/selectbestfreq = 100

# count only leaf nodes (otherwise all nodes)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nodeselection/restartdfs/countonlyleaves = TRUE

# priority of node selection rule <uct> in standard mode
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 10]
nodeselection/uct/stdpriority = 10

# priority of node selection rule <uct> in memory saving mode
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
nodeselection/uct/memsavepriority = 0

# maximum number of nodes before switching to default rule
# [type: int, advanced: TRUE, range: [0,1000000], default: 31]
nodeselection/uct/nodelimit = 31

# weight for visit quotient of node selection rule
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
nodeselection/uct/weight = 0.1

# should the estimate (TRUE) or lower bound of a node be used for UCT score?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
nodeselection/uct/useestimate = FALSE

# priority of branching rule <allfullstrong>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: -1000]
branching/allfullstrong/priority = -1000

# maximal depth level, up to which branching rule <allfullstrong> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/allfullstrong/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/allfullstrong/maxbounddist = 1

# priority of branching rule <cloud>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
branching/cloud/priority = 0

# maximal depth level, up to which branching rule <cloud> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/cloud/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/cloud/maxbounddist = 1

# should a cloud of points be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
branching/cloud/usecloud = TRUE

# should only F2 be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/cloud/onlyF2 = FALSE

# should the union of candidates be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/cloud/useunion = FALSE

# maximum number of points for the cloud (-1 means no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
branching/cloud/maxpoints = -1

# minimum success rate for the cloud
# [type: real, advanced: FALSE, range: [0,1], default: 0]
branching/cloud/minsuccessrate = 0

# minimum success rate for the union
# [type: real, advanced: FALSE, range: [0,1], default: 0]
branching/cloud/minsuccessunion = 0

# maximum depth for the union
# [type: int, advanced: FALSE, range: [0,65000], default: 65000]
branching/cloud/maxdepthunion = 65000

# priority of branching rule <distribution>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
branching/distribution/priority = 0

# maximal depth level, up to which branching rule <distribution> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/distribution/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/distribution/maxbounddist = 1

# the score;largest 'd'ifference, 'l'owest cumulative probability,'h'ighest c.p., 'v'otes lowest c.p., votes highest c.p.('w') 
# [type: char, advanced: TRUE, range: {dhlvw}, default: v]
branching/distribution/scoreparam = v

# should only rows which are active at the current node be considered?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/distribution/onlyactiverows = FALSE

# should the branching score weigh up- and down-scores of a variable
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/distribution/weightedscore = FALSE

# priority of branching rule <fullstrong>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
branching/fullstrong/priority = 0

# maximal depth level, up to which branching rule <fullstrong> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/fullstrong/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/fullstrong/maxbounddist = 1

# number of intermediate LPs solved to trigger reevaluation of strong branching value for a variable that was already evaluated at the current node
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 10]
branching/fullstrong/reevalage = 10

# maximum number of propagation rounds to be performed during strong branching before solving the LP (-1: no limit, -2: parameter settings)
# [type: int, advanced: TRUE, range: [-3,2147483647], default: -2]
branching/fullstrong/maxproprounds = -2

# should valid bounds be identified in a probing-like fashion during strong branching (only with propagation)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/fullstrong/probingbounds = TRUE

# should strong branching be applied even if there is just a single candidate?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/fullstrong/forcestrongbranch = FALSE

# priority of branching rule <gomory>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: -1000]
branching/gomory/priority = -1000

# maximal depth level, up to which branching rule <gomory> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/gomory/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/gomory/maxbounddist = 1

# maximum amount of branching candidates to generate Gomory cuts for (-1: all candidates)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
branching/gomory/maxncands = -1

# weight of efficacy in the weighted sum cut scoring rule
# [type: real, advanced: FALSE, range: [-1,1], default: 1]
branching/gomory/efficacyweight = 1

# weight of objective parallelism in the weighted sum cut scoring rule
# [type: real, advanced: FALSE, range: [-1,1], default: 0]
branching/gomory/objparallelweight = 0

# weight of integer support in the weighted sum cut scoring rule
# [type: real, advanced: FALSE, range: [-1,1], default: 0]
branching/gomory/intsupportweight = 0

# whether relpscost branching should be called without branching (used for bound inferences and conflicts)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/gomory/performrelpscost = FALSE

# use weaker cuts that are exactly derived from the branching split disjunction
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
branching/gomory/useweakercuts = TRUE

# priority of branching rule <inference>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 1000]
branching/inference/priority = 1000

# maximal depth level, up to which branching rule <inference> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/inference/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/inference/maxbounddist = 1

# weight in score calculations for conflict score
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1000]
branching/inference/conflictweight = 1000

# weight in score calculations for inference score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
branching/inference/inferenceweight = 1

# weight in score calculations for cutoff score
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
branching/inference/cutoffweight = 1

# should branching on LP solution be restricted to the fractional variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/inference/fractionals = TRUE

# should a weighted sum of inference, conflict and cutoff weights be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
branching/inference/useweightedsum = TRUE

# weight in score calculations for conflict score
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.001]
branching/inference/reliablescore = 0.001

# priority value for using conflict weights in lex. order
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1]
branching/inference/conflictprio = 1

# priority value for using cutoff weights in lex. order
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1]
branching/inference/cutoffprio = 1

# priority of branching rule <leastinf>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 50]
branching/leastinf/priority = 50

# maximal depth level, up to which branching rule <leastinf> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/leastinf/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/leastinf/maxbounddist = 1

# priority of branching rule <lookahead>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
branching/lookahead/priority = 0

# maximal depth level, up to which branching rule <lookahead> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/lookahead/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/lookahead/maxbounddist = 1

# should binary constraints be collected and applied?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/useimpliedbincons = FALSE

# should binary constraints be added as rows to the base LP? (0: no, 1: separate, 2: as initial rows)
# [type: int, advanced: TRUE, range: [0,2], default: 0]
branching/lookahead/addbinconsrow = 0

# how many constraints that are violated by the base lp solution should be gathered until the rule is stopped and they are added? [0 for unrestricted]
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
branching/lookahead/maxnviolatedcons = 1

# how many binary constraints that are violated by the base lp solution should be gathered until the rule is stopped and they are added? [0 for unrestricted]
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
branching/lookahead/maxnviolatedbincons = 0

# how many domain reductions that are violated by the base lp solution should be gathered until the rule is stopped and they are added? [0 for unrestricted]
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
branching/lookahead/maxnviolateddomreds = 1

# max number of LPs solved after which a previous prob branching results are recalculated
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 10]
branching/lookahead/reevalage = 10

# max number of LPs solved after which a previous FSB scoring results are recalculated
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 10]
branching/lookahead/reevalagefsb = 10

# the max depth of LAB.
# [type: int, advanced: TRUE, range: [1,2147483647], default: 2]
branching/lookahead/recursiondepth = 2

# should domain reductions be collected and applied?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/usedomainreduction = TRUE

# should domain reductions of feasible siblings should be merged?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/mergedomainreductions = FALSE

# should domain reductions only be applied if there are simple bound changes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/prefersimplebounds = FALSE

# should only domain reductions that violate the LP solution be applied?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/onlyvioldomreds = FALSE

# should binary constraints, that are not violated by the base LP, be collected and added?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/addnonviocons = FALSE

# toggles the abbreviated LAB.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/abbreviated = TRUE

# if abbreviated: The max number of candidates to consider at the node.
# [type: int, advanced: TRUE, range: [1,2147483647], default: 4]
branching/lookahead/maxncands = 4

# if abbreviated: The max number of candidates to consider per deeper node.
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2]
branching/lookahead/maxndeepercands = 2

# if abbreviated: Should the information gathered to obtain the best candidates be reused?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/reusebasis = TRUE

# if only non violating constraints are added, should the branching decision be stored till the next call?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/storeunviolatedsol = TRUE

# if abbreviated: Use pseudo costs to estimate the score of a candidate.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/abbrevpseudo = FALSE

# should the average score be used for uninitialized scores in level 2?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/level2avgscore = FALSE

# should uninitialized scores in level 2 be set to 0?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/level2zeroscore = FALSE

# add binary constraints with two variables found at the root node also as a clique
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/addclique = FALSE

# should domain propagation be executed before each temporary node is solved?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/propagate = TRUE

# should branching data generated at depth level 2 be stored for re-using it?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/lookahead/uselevel2data = TRUE

# should bounds known for child nodes be applied?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/applychildbounds = FALSE

# should the maximum number of domain reductions maxnviolateddomreds be enforced?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/enforcemaxdomreds = FALSE

# should branching results (and scores) be updated w.r.t. proven dual bounds?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/updatebranchingresults = FALSE

# maximum number of propagation rounds to perform at each temporary node (-1: unlimited, 0: SCIP default)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
branching/lookahead/maxproprounds = 0

# scoring function to be used at the base level
# [type: char, advanced: TRUE, range: {dfswplcra}, default: a]
branching/lookahead/scoringfunction = a

# scoring function to be used at deeper levels
# [type: char, advanced: TRUE, range: {dfswlcrx}, default: x]
branching/lookahead/deeperscoringfunction = x

# scoring function to be used during FSB scoring
# [type: char, advanced: TRUE, range: {dfswlcr}, default: d]
branching/lookahead/scoringscoringfunction = d

# if scoringfunction is 's', this value is used to weight the min of the gains of two child problems in the convex combination
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.8]
branching/lookahead/minweight = 0.8

# if the FSB score is of a candidate is worse than the best by this factor, skip this candidate (-1: disable)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
branching/lookahead/worsefactor = -1

# should lookahead branching only be applied if the max gain in level 1 is not uniquely that of the best candidate?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/lookahead/filterbymaxgain = FALSE

# priority of branching rule <mostinf>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 100]
branching/mostinf/priority = 100

# maximal depth level, up to which branching rule <mostinf> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/mostinf/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/mostinf/maxbounddist = 1

# priority of branching rule <multaggr>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
branching/multaggr/priority = 0

# maximal depth level, up to which branching rule <multaggr> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/multaggr/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/multaggr/maxbounddist = 1

# number of intermediate LPs solved to trigger reevaluation of strong branching value for a variable that was already evaluated at the current node
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 0]
branching/multaggr/reevalage = 0

# maximum number of propagation rounds to be performed during multaggr branching before solving the LP (-1: no limit, -2: parameter settings)
# [type: int, advanced: TRUE, range: [-2,2147483647], default: 0]
branching/multaggr/maxproprounds = 0

# should valid bounds be identified in a probing-like fashion during multaggr branching (only with propagation)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/multaggr/probingbounds = TRUE

# priority of branching rule <nodereopt>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: -9000000]
branching/nodereopt/priority = -9000000

# maximal depth level, up to which branching rule <nodereopt> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/nodereopt/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/nodereopt/maxbounddist = 1

# priority of branching rule <pscost>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 2000]
branching/pscost/priority = 2000

# maximal depth level, up to which branching rule <pscost> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/pscost/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/pscost/maxbounddist = 1

# strategy for utilizing pseudo-costs of external branching candidates (multiply as in pseudo costs 'u'pdate rule, or by 'd'omain reduction, or by domain reduction of 's'ibling, or by 'v'ariable score)
# [type: char, advanced: FALSE, range: {dsuv}, default: u]
branching/pscost/strategy = u

# weight for minimum of scores of a branching candidate when building weighted sum of min/max/sum of scores
# [type: real, advanced: TRUE, range: [-1e+20,1e+20], default: 0.8]
branching/pscost/minscoreweight = 0.8

# weight for maximum of scores of a branching candidate when building weighted sum of min/max/sum of scores
# [type: real, advanced: TRUE, range: [-1e+20,1e+20], default: 1.3]
branching/pscost/maxscoreweight = 1.3

# weight for sum of scores of a branching candidate when building weighted sum of min/max/sum of scores
# [type: real, advanced: TRUE, range: [-1e+20,1e+20], default: 0.1]
branching/pscost/sumscoreweight = 0.1

# number of children to create in n-ary branching
# [type: int, advanced: FALSE, range: [2,2147483647], default: 2]
branching/pscost/nchildren = 2

# maximal depth where to do n-ary branching, -1 to turn off
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
branching/pscost/narymaxdepth = -1

# minimal domain width in children when doing n-ary branching, relative to global bounds
# [type: real, advanced: FALSE, range: [0,1], default: 0.001]
branching/pscost/naryminwidth = 0.001

# factor of domain width in n-ary branching when creating nodes with increasing distance from branching value
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 2]
branching/pscost/narywidthfactor = 2

# priority of branching rule <random>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: -100000]
branching/random/priority = -100000

# maximal depth level, up to which branching rule <random> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/random/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/random/maxbounddist = 1

# initial random seed value
# [type: int, advanced: FALSE, range: [0,2147483647], default: 41]
branching/random/seed = 41

# priority of branching rule <relpscost>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 10000]
branching/relpscost/priority = 10000

# maximal depth level, up to which branching rule <relpscost> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/relpscost/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/relpscost/maxbounddist = 1

# weight in score calculations for conflict score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0.01]
branching/relpscost/conflictweight = 0.01

# weight in score calculations for conflict length score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0]
branching/relpscost/conflictlengthweight = 0

# weight in score calculations for inference score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0.0001]
branching/relpscost/inferenceweight = 0.0001

# weight in score calculations for cutoff score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0.0001]
branching/relpscost/cutoffweight = 0.0001

# weight in score calculations for average GMI cuts normalized efficacy
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0]
branching/relpscost/gmiavgeffweight = 0

# weight in score calculations for last GMI cuts normalized efficacy
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1e-05]
branching/relpscost/gmilasteffweight = 1e-05

# weight in score calculations for pseudo cost score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
branching/relpscost/pscostweight = 1

# weight in score calculations for nlcount score
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 0.1]
branching/relpscost/nlscoreweight = 0.1

# minimal value for minimum pseudo cost size to regard pseudo cost value as reliable
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
branching/relpscost/minreliable = 1

# maximal value for minimum pseudo cost size to regard pseudo cost value as reliable
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 5]
branching/relpscost/maxreliable = 5

# maximal fraction of strong branching LP iterations compared to node relaxation LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.5]
branching/relpscost/sbiterquot = 0.5

# additional number of allowed strong branching LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100000]
branching/relpscost/sbiterofs = 100000

# maximal number of further variables evaluated without better score
# [type: int, advanced: TRUE, range: [1,2147483647], default: 9]
branching/relpscost/maxlookahead = 9

# maximal number of candidates initialized with strong branching per node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
branching/relpscost/initcand = 100

# iteration limit for strong branching initializations of pseudo cost entries (0: auto)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
branching/relpscost/inititer = 0

# maximal number of bound tightenings before the node is reevaluated (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 5]
branching/relpscost/maxbdchgs = 5

# maximum number of propagation rounds to be performed during strong branching before solving the LP (-1: no limit, -2: parameter settings)
# [type: int, advanced: TRUE, range: [-2,2147483647], default: -2]
branching/relpscost/maxproprounds = -2

# should valid bounds be identified in a probing-like fashion during strong branching (only with propagation)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/relpscost/probingbounds = TRUE

# should reliability be based on relative errors?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/userelerrorreliability = FALSE

# low relative error tolerance for reliability
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.05]
branching/relpscost/lowerrortol = 0.05

# high relative error tolerance for reliability
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
branching/relpscost/higherrortol = 1

# should strong branching result be considered for pseudo costs if the other direction was infeasible?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/storesemiinitcosts = FALSE

# should the scoring function use only local cutoff and inference information obtained for strong branching candidates?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/usesblocalinfo = FALSE

# should the strong branching decision be based on a hypothesis test?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/usehyptestforreliability = FALSE

# should the confidence level be adjusted dynamically?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/usedynamicconfidence = FALSE

# should branching rule skip candidates that have a low probability to be better than the best strong-branching or pseudo-candidate?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/relpscost/skipbadinitcands = TRUE

# the confidence level for statistical methods, between 0 (Min) and 4 (Max).
# [type: int, advanced: TRUE, range: [0,4], default: 2]
branching/relpscost/confidencelevel = 2

# should candidates be initialized in randomized order?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/randinitorder = FALSE

# should smaller weights be used for pseudo cost updates after hitting the LP iteration limit?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/usesmallweightsitlim = FALSE

# should the weights of the branching rule be adjusted dynamically during solving based on objective and infeasible leaf counters?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
branching/relpscost/dynamicweights = TRUE

# should degeneracy be taken into account to update weights and skip strong branching? (0: off, 1: after root, 2: always)
# [type: int, advanced: TRUE, range: [0,2], default: 1]
branching/relpscost/degeneracyaware = 1

# start seed for random number generation
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5]
branching/relpscost/startrandseed = 5

# Use symmetry to filter branching candidates?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/filtercandssym = FALSE

# Transfer pscost information to symmetric variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/relpscost/transsympscost = FALSE

# should candidate branching variables be scored using the Treemodel branching rules?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/treemodel/enable = FALSE

# scoring function to use at nodes predicted to be high in the tree ('d'efault, 's'vts, 'r'atio, 't'ree sample)
# [type: char, advanced: FALSE, range: {dsrt}, default: r]
branching/treemodel/highrule = r

# scoring function to use at nodes predicted to be low in the tree ('d'efault, 's'vts, 'r'atio, 't'ree sample)
# [type: char, advanced: FALSE, range: {dsrt}, default: r]
branching/treemodel/lowrule = r

# estimated tree height at which we switch from using the low rule to the high rule
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
branching/treemodel/height = 10

# should dominated candidates be filtered before using the high scoring function? ('a'uto, 't'rue, 'f'alse)
# [type: char, advanced: TRUE, range: {atf}, default: a]
branching/treemodel/filterhigh = a

# should dominated candidates be filtered before using the low scoring function? ('a'uto, 't'rue, 'f'alse)
# [type: char, advanced: TRUE, range: {atf}, default: a]
branching/treemodel/filterlow = a

# maximum number of fixed-point iterations when computing the ratio
# [type: int, advanced: TRUE, range: [1,2147483647], default: 24]
branching/treemodel/maxfpiter = 24

# maximum height to compute the SVTS score exactly before approximating
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
branching/treemodel/maxsvtsheight = 100

# which method should be used as a fallback if the tree size estimates are infinite? ('d'efault, 'r'atio)
# [type: char, advanced: TRUE, range: {dr}, default: r]
branching/treemodel/fallbackinf = r

# which method should be used as a fallback if there is no primal bound available? ('d'efault, 'r'atio)
# [type: char, advanced: TRUE, range: {dr}, default: r]
branching/treemodel/fallbacknoprim = r

# threshold at which pseudocosts are considered small, making hybrid scores more likely to be the deciding factor in branching
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.1]
branching/treemodel/smallpscost = 0.1

# priority of branching rule <vanillafullstrong>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: -2000]
branching/vanillafullstrong/priority = -2000

# maximal depth level, up to which branching rule <vanillafullstrong> should be used (-1 for no limit)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
branching/vanillafullstrong/maxdepth = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying branching rule (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
branching/vanillafullstrong/maxbounddist = 1

# should integral variables in the current LP solution be considered as branching candidates?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/vanillafullstrong/integralcands = FALSE

# should strong branching side-effects be prevented (e.g., domain changes, stat updates etc.)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
branching/vanillafullstrong/idempotent = FALSE

# should strong branching scores be computed for all candidates, or can we early stop when a variable has infinite score?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/vanillafullstrong/scoreall = FALSE

# should strong branching scores be collected?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/vanillafullstrong/collectscores = FALSE

# should candidates only be scored, but no branching be performed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
branching/vanillafullstrong/donotbranch = FALSE

# restart policy: (a)lways, (c)ompletion, (e)stimation, (n)ever
# [type: char, advanced: FALSE, range: {acen}, default: e]
estimation/restarts/restartpolicy = e

# tree size estimation method: (c)ompletion, (e)nsemble, time series forecasts on either (g)ap, (l)eaf frequency, (o)open nodes, tree (w)eight, (s)sg, or (t)ree profile or w(b)e
# [type: char, advanced: FALSE, range: {bceglostw}, default: w]
estimation/method = w

# restart limit
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
estimation/restarts/restartlimit = 1

# minimum number of nodes before restart
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: 1000]
estimation/restarts/minnodes = 1000

# should only leaves count for the minnodes parameter?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
estimation/restarts/countonlyleaves = FALSE

# factor by which the estimated number of nodes should exceed the current number of nodes
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 50]
estimation/restarts/restartfactor = 50

# whether to apply a restart when nonlinear constraints are present
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
estimation/restarts/restartnonlinear = FALSE

# whether to apply a restart when active pricers are used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
estimation/restarts/restartactpricers = FALSE

# coefficient of tree weight in monotone approximation of search completion
# [type: real, advanced: FALSE, range: [0,1], default: 0.3667]
estimation/coefmonoweight = 0.3667

# coefficient of 1 - SSG in monotone approximation of search completion
# [type: real, advanced: FALSE, range: [0,1], default: 0.6333]
estimation/coefmonossg = 0.6333

# limit on the number of successive samples to really trigger a restart
# [type: int, advanced: FALSE, range: [1,2147483647], default: 50]
estimation/restarts/hitcounterlim = 50

# report frequency on estimation: -1: never, 0:always, k >= 1: k times evenly during search
# [type: int, advanced: TRUE, range: [-1,1073741823], default: -1]
estimation/reportfreq = -1

# user regression forest in RFCSV format
# [type: string, advanced: FALSE, default: "-"]
estimation/regforestfilename = "-"

# approximation of search tree completion: (a)uto, (g)ap, tree (w)eight, (m)onotone regression, (r)egression forest, (s)sg
# [type: char, advanced: FALSE, range: {agmrsw}, default: a]
estimation/completiontype = a

# should the event handler collect data?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
estimation/treeprofile/enabled = FALSE

# minimum average number of nodes at each depth before producing estimations
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 20]
estimation/treeprofile/minnodesperdepth = 20

# use leaf nodes as basic observations for time series, or all nodes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
estimation/useleafts = TRUE

# should statistics be shown at the end?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
estimation/showstats = FALSE

# the maximum number of individual SSG subtrees; -1: no limit
# [type: int, advanced: FALSE, range: [-1,1073741823], default: -1]
estimation/ssg/nmaxsubtrees = -1

# minimum number of nodes to process between two consecutive SSG splits
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 0]
estimation/ssg/nminnodeslastsplit = 0

# is statistics table <estim> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/estim/active = TRUE

# display activation status of display column <completed> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/completed/active = 1

# display activation status of display column <nrank1nodes> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
display/nrank1nodes/active = 0

# display activation status of display column <nnodesbelowinc> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
display/nnodesbelowinc/active = 0

# should the event handler adapt the solver behavior?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/enabled = FALSE

# should the event handler test all phase transitions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/testmode = FALSE

# settings file for feasibility phase -- precedence over emphasis settings
# [type: string, advanced: FALSE, default: "-"]
solvingphases/feassetname = "-"

# settings file for improvement phase -- precedence over emphasis settings
# [type: string, advanced: FALSE, default: "-"]
solvingphases/improvesetname = "-"

# settings file for proof phase -- precedence over emphasis settings
# [type: string, advanced: FALSE, default: "-"]
solvingphases/proofsetname = "-"

# node offset for rank-1 and estimate transitions
# [type: longint, advanced: FALSE, range: [1,9223372036854775807], default: 50]
solvingphases/nodeoffset = 50

# should the event handler fall back from optimal phase?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/fallback = FALSE

# transition method: Possible options are 'e'stimate,'l'ogarithmic regression,'o'ptimal-value based,'r'ank-1
# [type: char, advanced: FALSE, range: {elor}, default: r]
solvingphases/transitionmethod = r

# should the event handler interrupt the solving process after optimal solution was found?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/interruptoptimal = FALSE

# should a restart be applied between the feasibility and improvement phase?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/userestart1to2 = FALSE

# should a restart be applied between the improvement and the proof phase?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
solvingphases/userestart2to3 = FALSE

# optimal solution value for problem
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1e+99]
solvingphases/optimalvalue = 1e+99

# x-type for logarithmic regression - (t)ime, (n)odes, (l)p iterations
# [type: char, advanced: FALSE, range: {lnt}, default: n]
solvingphases/xtype = n

# should emphasis settings for the solving phases be used, or settings files?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
solvingphases/useemphsettings = TRUE

# priority of compression <largestrepr>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 2000]
compression/largestrepr/priority = 2000

# minimal number of leave nodes for calling tree compression <largestrepr>
# [type: int, advanced: FALSE, range: [1,2147483647], default: 20]
compression/largestrepr/minnleaves = 20

# number of runs in the constrained part.
# [type: int, advanced: FALSE, range: [1,2147483647], default: 5]
compression/largestrepr/iterations = 5

# minimal number of common variables.
# [type: int, advanced: FALSE, range: [1,2147483647], default: 3]
compression/largestrepr/mincommonvars = 3

# priority of compression <weakcompr>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 1000]
compression/weakcompr/priority = 1000

# minimal number of leave nodes for calling tree compression <weakcompr>
# [type: int, advanced: FALSE, range: [1,2147483647], default: 50]
compression/weakcompr/minnleaves = 50

# convert constraints into nodes
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
compression/weakcompr/convertconss = FALSE

# priority of heuristic <actconsdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003700]
heuristics/actconsdiving/priority = -1003700

# frequency for calling primal heuristic <actconsdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/actconsdiving/freq = -1

# frequency offset for calling primal heuristic <actconsdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 5]
heuristics/actconsdiving/freqofs = 5

# maximal depth level to call primal heuristic <actconsdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/actconsdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/actconsdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/actconsdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/actconsdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/actconsdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/actconsdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/actconsdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/actconsdiving/maxdiveubquotnosol = 1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
heuristics/actconsdiving/maxdiveavgquotnosol = 1

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/actconsdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/actconsdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/actconsdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/actconsdiving/onlylpbranchcands = TRUE

# priority of heuristic <adaptivediving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -70000]
heuristics/adaptivediving/priority = -70000

# frequency for calling primal heuristic <adaptivediving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
heuristics/adaptivediving/freq = 5

# frequency offset for calling primal heuristic <adaptivediving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 3]
heuristics/adaptivediving/freqofs = 3

# maximal depth level to call primal heuristic <adaptivediving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/adaptivediving/maxdepth = -1

# parameter that increases probability of exploration among divesets (only active if seltype is 'e')
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1]
heuristics/adaptivediving/epsilon = 1

# score parameter for selection: minimize either average 'n'odes, LP 'i'terations,backtrack/'c'onflict ratio, 'd'epth, 1 / 's'olutions, or 1 / solutions'u'ccess
# [type: char, advanced: FALSE, range: {cdinsu}, default: c]
heuristics/adaptivediving/scoretype = c

# selection strategy: (e)psilon-greedy, (w)eighted distribution, (n)ext diving
# [type: char, advanced: FALSE, range: {enw}, default: w]
heuristics/adaptivediving/seltype = w

# should the heuristic use its own statistics, or shared statistics?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/adaptivediving/useadaptivecontext = FALSE

# coefficient c to decrease initial confidence (calls + 1.0) / (calls + c) in scores
# [type: real, advanced: FALSE, range: [1,2147483647], default: 10]
heuristics/adaptivediving/selconfidencecoeff = 10

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.1]
heuristics/adaptivediving/maxlpiterquot = 0.1

# additional number of allowed LP iterations
# [type: longint, advanced: FALSE, range: [0,2147483647], default: 1500]
heuristics/adaptivediving/maxlpiterofs = 1500

# weight of incumbent solutions compared to other solutions in computation of LP iteration limit
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
heuristics/adaptivediving/bestsolweight = 10

# priority of heuristic <bound>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1107000]
heuristics/bound/priority = -1107000

# frequency for calling primal heuristic <bound> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/bound/freq = -1

# frequency offset for calling primal heuristic <bound>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/bound/freqofs = 0

# maximal depth level to call primal heuristic <bound> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/bound/maxdepth = -1

# Should heuristic only be executed if no primal solution was found, yet?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/bound/onlywithoutsol = TRUE

# maximum number of propagation rounds during probing (-1 infinity, -2 parameter settings)
# [type: int, advanced: TRUE, range: [-1,536870911], default: 0]
heuristics/bound/maxproprounds = 0

# to which bound should integer variables be fixed? ('l'ower, 'u'pper, or 'b'oth)
# [type: char, advanced: FALSE, range: {lub}, default: l]
heuristics/bound/bound = l

# priority of heuristic <clique>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 5000]
heuristics/clique/priority = 5000

# frequency for calling primal heuristic <clique> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/clique/freq = 0

# frequency offset for calling primal heuristic <clique>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/clique/freqofs = 0

# maximal depth level to call primal heuristic <clique> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/clique/maxdepth = -1

# minimum percentage of integer variables that have to be fixable
# [type: real, advanced: FALSE, range: [0,1], default: 0.65]
heuristics/clique/minintfixingrate = 0.65

# minimum percentage of fixed variables in the sub-MIP
# [type: real, advanced: FALSE, range: [0,1], default: 0.65]
heuristics/clique/minmipfixingrate = 0.65

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/clique/maxnodes = 5000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/clique/nodesofs = 500

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/clique/minnodes = 500

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/clique/nodesquot = 0.1

# factor by which clique heuristic should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/clique/minimprove = 0.01

# maximum number of propagation rounds during probing (-1 infinity)
# [type: int, advanced: TRUE, range: [-1,536870911], default: 2]
heuristics/clique/maxproprounds = 2

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/clique/copycuts = TRUE

# should more variables be fixed based on variable locks if the fixing rate was not reached?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/clique/uselockfixings = FALSE

# maximum number of backtracks during the fixing process
# [type: int, advanced: TRUE, range: [-1,536870911], default: 10]
heuristics/clique/maxbacktracks = 10

# priority of heuristic <coefdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1001000]
heuristics/coefdiving/priority = -1001000

# frequency for calling primal heuristic <coefdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/coefdiving/freq = -1

# frequency offset for calling primal heuristic <coefdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 1]
heuristics/coefdiving/freqofs = 1

# maximal depth level to call primal heuristic <coefdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/coefdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/coefdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/coefdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/coefdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/coefdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/coefdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/coefdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/coefdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/coefdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/coefdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/coefdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/coefdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/coefdiving/onlylpbranchcands = FALSE

# priority of heuristic <completesol>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
heuristics/completesol/priority = 0

# frequency for calling primal heuristic <completesol> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/completesol/freq = 0

# frequency offset for calling primal heuristic <completesol>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/completesol/freqofs = 0

# maximal depth level to call primal heuristic <completesol> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
heuristics/completesol/maxdepth = 0

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/completesol/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/completesol/minnodes = 50

# maximal rate of unknown solution values
# [type: real, advanced: FALSE, range: [0,1], default: 0.85]
heuristics/completesol/maxunknownrate = 0.85

# should all subproblem solutions be added to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/completesol/addallsols = FALSE

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/completesol/nodesofs = 500

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/completesol/nodesquot = 0.1

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/completesol/lplimfac = 2

# weight of the original objective function (1: only original objective)
# [type: real, advanced: TRUE, range: [0.001,1], default: 1]
heuristics/completesol/objweight = 1

# bound widening factor applied to continuous variables (0: fix variables to given solution values, 1: relax to global bounds)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/completesol/boundwidening = 0.1

# factor by which the incumbent should be improved at least
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/completesol/minimprove = 0.01

# should number of continuous variables be ignored?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/completesol/ignorecont = FALSE

# heuristic stops, if the given number of improving solutions were found (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
heuristics/completesol/solutions = 5

# maximal number of iterations in propagation (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
heuristics/completesol/maxproprounds = 10

# should the heuristic run before presolving?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/completesol/beforepresol = TRUE

# maximal number of LP iterations (-1: no limit)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: -1]
heuristics/completesol/maxlpiter = -1

# maximal number of continuous variables after presolving
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
heuristics/completesol/maxcontvars = -1

# priority of heuristic <conflictdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000100]
heuristics/conflictdiving/priority = -1000100

# frequency for calling primal heuristic <conflictdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/conflictdiving/freq = 10

# frequency offset for calling primal heuristic <conflictdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/conflictdiving/freqofs = 0

# maximal depth level to call primal heuristic <conflictdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/conflictdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/conflictdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/conflictdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/conflictdiving/maxlpiterquot = 0.15

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/conflictdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/conflictdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/conflictdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/conflictdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/conflictdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/conflictdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/conflictdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/conflictdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/conflictdiving/onlylpbranchcands = FALSE

# try to maximize the violation
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/conflictdiving/maxviol = TRUE

# perform rounding like coefficient diving
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/conflictdiving/likecoef = FALSE

# minimal number of conflict locks per variable
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5]
heuristics/conflictdiving/minconflictlocks = 5

# weight used in a convex combination of conflict and variable locks
# [type: real, advanced: TRUE, range: [0,1], default: 0.75]
heuristics/conflictdiving/lockweight = 0.75

# priority of heuristic <crossover>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1104000]
heuristics/crossover/priority = -1104000

# frequency for calling primal heuristic <crossover> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 30]
heuristics/crossover/freq = 30

# frequency offset for calling primal heuristic <crossover>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/crossover/freqofs = 0

# maximal depth level to call primal heuristic <crossover> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/crossover/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/crossover/nodesofs = 500

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/crossover/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/crossover/minnodes = 50

# number of solutions to be taken into account
# [type: int, advanced: FALSE, range: [2,2147483647], default: 3]
heuristics/crossover/nusedsols = 3

# number of nodes without incumbent change that heuristic should wait
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 200]
heuristics/crossover/nwaitingnodes = 200

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/crossover/nodesquot = 0.1

# minimum percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [0,1], default: 0.666]
heuristics/crossover/minfixingrate = 0.666

# factor by which Crossover should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/crossover/minimprove = 0.01

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/crossover/lplimfac = 2

# should the choice which sols to take be randomized?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/crossover/randomization = TRUE

# should the nwaitingnodes parameter be ignored at the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/crossover/dontwaitatroot = FALSE

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/crossover/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/crossover/copycuts = TRUE

# should the subproblem be permuted to increase diversification?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/crossover/permute = FALSE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
heuristics/crossover/bestsollimit = -1

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/crossover/useuct = FALSE

# priority of heuristic <dins>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1105000]
heuristics/dins/priority = -1105000

# frequency for calling primal heuristic <dins> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/dins/freq = -1

# frequency offset for calling primal heuristic <dins>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/dins/freqofs = 0

# maximal depth level to call primal heuristic <dins> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/dins/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 5000]
heuristics/dins/nodesofs = 5000

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.05]
heuristics/dins/nodesquot = 0.05

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 50]
heuristics/dins/minnodes = 50

# number of pool-solutions to be checked for flag array update (for hard fixing of binary variables)
# [type: int, advanced: FALSE, range: [1,2147483647], default: 5]
heuristics/dins/solnum = 5

# radius (using Manhattan metric) of the incumbent's neighborhood to be searched
# [type: int, advanced: FALSE, range: [1,2147483647], default: 18]
heuristics/dins/neighborhoodsize = 18

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/dins/maxnodes = 5000

# factor by which dins should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/dins/minimprove = 0.01

# number of nodes without incumbent change that heuristic should wait
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 200]
heuristics/dins/nwaitingnodes = 200

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1.5]
heuristics/dins/lplimfac = 1.5

# minimum percentage of integer variables that have to be fixable
# [type: real, advanced: FALSE, range: [0,1], default: 0.3]
heuristics/dins/minfixingrate = 0.3

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dins/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/dins/copycuts = TRUE

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dins/useuct = FALSE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/dins/bestsollimit = 3

# priority of heuristic <distributiondiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003300]
heuristics/distributiondiving/priority = -1003300

# frequency for calling primal heuristic <distributiondiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/distributiondiving/freq = 10

# frequency offset for calling primal heuristic <distributiondiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 3]
heuristics/distributiondiving/freqofs = 3

# maximal depth level to call primal heuristic <distributiondiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/distributiondiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/distributiondiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/distributiondiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/distributiondiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/distributiondiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/distributiondiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/distributiondiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/distributiondiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/distributiondiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/distributiondiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/distributiondiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/distributiondiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/distributiondiving/onlylpbranchcands = TRUE

# the score;largest 'd'ifference, 'l'owest cumulative probability,'h'ighest c.p., 'v'otes lowest c.p., votes highest c.p.('w'), 'r'evolving
# [type: char, advanced: TRUE, range: {lvdhwr}, default: r]
heuristics/distributiondiving/scoreparam = r

# priority of heuristic <dps>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 75000]
heuristics/dps/priority = 75000

# frequency for calling primal heuristic <dps> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/dps/freq = -1

# frequency offset for calling primal heuristic <dps>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/dps/freqofs = 0

# maximal depth level to call primal heuristic <dps> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/dps/maxdepth = -1

# maximal number of iterations
# [type: int, advanced: FALSE, range: [1,2147483647], default: 50]
heuristics/dps/maxiterations = 50

# maximal linking score of used decomposition (equivalent to percentage of linking constraints)
# [type: real, advanced: FALSE, range: [0,1], default: 1]
heuristics/dps/maxlinkscore = 1

# multiplier for absolute increase of penalty parameters (0: no increase)
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 100]
heuristics/dps/penalty = 100

# should the problem get reoptimized with the original objective function?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dps/reoptimize = FALSE

# should solutions get reused in subproblems?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dps/reuse = FALSE

# should strict limits for reoptimization be set?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/dps/reoptlimits = TRUE

# should the heuristic run before or after the processing of the node? (0: before, 1: after, 2: both)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
heuristics/dps/timing = 0

# priority of heuristic <dualval>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -10]
heuristics/dualval/priority = -10

# frequency for calling primal heuristic <dualval> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/dualval/freq = -1

# frequency offset for calling primal heuristic <dualval>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/dualval/freqofs = 0

# maximal depth level to call primal heuristic <dualval> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/dualval/maxdepth = -1

# exit if objective doesn't improve
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dualval/forceimprovements = FALSE

# add constraint to ensure that discrete vars are improving
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/dualval/onlycheaper = TRUE

# disable the heuristic if it was not called at a leaf of the B&B tree
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dualval/onlyleaves = FALSE

# relax the indicator variables by introducing continuous copies
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dualval/relaxindicators = FALSE

# relax the continous variables
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/dualval/relaxcontvars = FALSE

# verblevel of the heuristic, default is 0 to display nothing
# [type: int, advanced: FALSE, range: [0,4], default: 0]
heuristics/dualval/heurverblevel = 0

# verblevel of the nlp solver, can be 0 or 1
# [type: int, advanced: FALSE, range: [0,1], default: 0]
heuristics/dualval/nlpverblevel = 0

# number of ranks that should be displayed when the heuristic is called
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
heuristics/dualval/rankvalue = 10

# maximal number of recursive calls of the heuristic (if dynamicdepth is off)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 25]
heuristics/dualval/maxcalls = 25

# says if and how the recursion depth is computed at runtime
# [type: int, advanced: FALSE, range: [0,1], default: 0]
heuristics/dualval/dynamicdepth = 0

# maximal number of variables that may have maximal rank, quit if there are more, turn off by setting -1
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 50]
heuristics/dualval/maxequalranks = 50

# minimal gap for which we still run the heuristic, if gap is less we return without doing anything
# [type: real, advanced: FALSE, range: [0,100], default: 5]
heuristics/dualval/mingap = 5

# value added to objective of slack variables, must not be zero
# [type: real, advanced: FALSE, range: [0.1,1e+20], default: 1]
heuristics/dualval/lambdaslack = 1

# scaling factor for the objective function
# [type: real, advanced: FALSE, range: [0,1], default: 0]
heuristics/dualval/lambdaobj = 0

# priority of heuristic <farkasdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -900000]
heuristics/farkasdiving/priority = -900000

# frequency for calling primal heuristic <farkasdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/farkasdiving/freq = 10

# frequency offset for calling primal heuristic <farkasdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/farkasdiving/freqofs = 0

# maximal depth level to call primal heuristic <farkasdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/farkasdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/farkasdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/farkasdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/farkasdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/farkasdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/farkasdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/farkasdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/farkasdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/farkasdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/farkasdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/farkasdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1]
heuristics/farkasdiving/lpsolvefreq = 1

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/farkasdiving/onlylpbranchcands = FALSE

# should diving candidates be checked before running?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/farkasdiving/checkcands = FALSE

# should the score be scaled?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/farkasdiving/scalescore = TRUE

# should the heuristic only run within the tree if at least one solution was found at the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/farkasdiving/rootsuccess = TRUE

# maximal occurance factor of an objective coefficient
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/farkasdiving/maxobjocc = 1

# minimal objective dynamism (log) to run
# [type: real, advanced: TRUE, range: [0,1e+20], default: 0.0001]
heuristics/farkasdiving/objdynamism = 0.0001

# scale score by [f]ractionality or [i]mpact on farkasproof
# [type: char, advanced: TRUE, range: {fi}, default: i]
heuristics/farkasdiving/scaletype = i

# priority of heuristic <feaspump>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000000]
heuristics/feaspump/priority = -1000000

# frequency for calling primal heuristic <feaspump> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/feaspump/freq = 20

# frequency offset for calling primal heuristic <feaspump>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/feaspump/freqofs = 0

# maximal depth level to call primal heuristic <feaspump> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/feaspump/maxdepth = -1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.01]
heuristics/feaspump/maxlpiterquot = 0.01

# factor by which the regard of the objective is decreased in each round, 1.0 for dynamic
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/feaspump/objfactor = 0.1

# initial weight of the objective function in the convex combination
# [type: real, advanced: FALSE, range: [0,1], default: 1]
heuristics/feaspump/alpha = 1

# threshold difference for the convex parameter to perform perturbation
# [type: real, advanced: FALSE, range: [0,1], default: 1]
heuristics/feaspump/alphadiff = 1

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/feaspump/maxlpiterofs = 1000

# total number of feasible solutions found up to which heuristic is called (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
heuristics/feaspump/maxsols = 10

# maximal number of pumping loops (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10000]
heuristics/feaspump/maxloops = 10000

# maximal number of pumping rounds without fractionality improvement (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
heuristics/feaspump/maxstallloops = 10

# minimum number of random variables to flip, if a 1-cycle is encountered
# [type: int, advanced: TRUE, range: [1,2147483647], default: 10]
heuristics/feaspump/minflips = 10

# maximum length of cycles to be checked explicitly in each round
# [type: int, advanced: TRUE, range: [1,100], default: 3]
heuristics/feaspump/cyclelength = 3

# number of iterations until a random perturbation is forced
# [type: int, advanced: TRUE, range: [1,2147483647], default: 100]
heuristics/feaspump/perturbfreq = 100

# radius (using Manhattan metric) of the neighborhood to be searched in stage 3
# [type: int, advanced: FALSE, range: [1,2147483647], default: 18]
heuristics/feaspump/neighborhoodsize = 18

# should the feasibility pump be called at root node before cut separation?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/feaspump/beforecuts = TRUE

# should an iterative round-and-propagate scheme be used to find the integral points?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/feaspump/usefp20 = FALSE

# should a random perturbation be performed if a feasible solution was found?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/feaspump/pertsolfound = TRUE

# should we solve a local branching sub-MIP if no solution could be found?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/feaspump/stage3 = FALSE

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/feaspump/copycuts = TRUE

# priority of heuristic <fixandinfer>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -500000]
heuristics/fixandinfer/priority = -500000

# frequency for calling primal heuristic <fixandinfer> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/fixandinfer/freq = -1

# frequency offset for calling primal heuristic <fixandinfer>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/fixandinfer/freqofs = 0

# maximal depth level to call primal heuristic <fixandinfer> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/fixandinfer/maxdepth = -1

# maximal number of propagation rounds in probing subproblems (-1: no limit, 0: auto)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
heuristics/fixandinfer/proprounds = 0

# minimal number of fixings to apply before dive may be aborted
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
heuristics/fixandinfer/minfixings = 100

# priority of heuristic <fracdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003000]
heuristics/fracdiving/priority = -1003000

# frequency for calling primal heuristic <fracdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/fracdiving/freq = 10

# frequency offset for calling primal heuristic <fracdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 3]
heuristics/fracdiving/freqofs = 3

# maximal depth level to call primal heuristic <fracdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/fracdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/fracdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/fracdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/fracdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/fracdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/fracdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/fracdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/fracdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/fracdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/fracdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/fracdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/fracdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/fracdiving/onlylpbranchcands = FALSE

# priority of heuristic <gins>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1103000]
heuristics/gins/priority = -1103000

# frequency for calling primal heuristic <gins> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/gins/freq = 20

# frequency offset for calling primal heuristic <gins>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 8]
heuristics/gins/freqofs = 8

# maximal depth level to call primal heuristic <gins> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/gins/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
heuristics/gins/nodesofs = 500

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5000]
heuristics/gins/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
heuristics/gins/minnodes = 50

# number of nodes without incumbent change that heuristic should wait
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
heuristics/gins/nwaitingnodes = 100

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.15]
heuristics/gins/nodesquot = 0.15

# percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [1e-06,0.999999], default: 0.66]
heuristics/gins/minfixingrate = 0.66

# factor by which gins should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/gins/minimprove = 0.01

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/gins/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/gins/copycuts = TRUE

# should continuous variables outside the neighborhoods be fixed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/gins/fixcontvars = FALSE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/gins/bestsollimit = 3

# maximum distance to selected variable to enter the subproblem, or -1 to select the distance that best approximates the minimum fixing rate from below
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/gins/maxdistance = 3

# the reference point to compute the neighborhood potential: (r)oot, (l)ocal lp, or (p)seudo solution
# [type: char, advanced: TRUE, range: {lpr}, default: r]
heuristics/gins/potential = r

# should the heuristic solve a sequence of sub-MIP's around the first selected variable
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/gins/userollinghorizon = TRUE

# should dense constraints (at least as dense as 1 - minfixingrate) be ignored by connectivity graph?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/gins/relaxdenseconss = FALSE

# limiting percentage for variables already used in sub-SCIPs to terminate rolling horizon approach
# [type: real, advanced: TRUE, range: [0,1], default: 0.4]
heuristics/gins/rollhorizonlimfac = 0.4

# overlap of blocks between runs - 0.0: no overlap, 1.0: shift by only 1 block
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/gins/overlap = 0

# should user decompositions be considered, if available?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/gins/usedecomp = TRUE

# should user decompositions be considered for initial selection in rolling horizon, if available?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/gins/usedecomprollhorizon = FALSE

# should random initial variable selection be used if decomposition was not successful?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/gins/useselfallback = TRUE

# should blocks be treated consecutively (sorted by ascending label?)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/gins/consecutiveblocks = TRUE

# priority of heuristic <guideddiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1007000]
heuristics/guideddiving/priority = -1007000

# frequency for calling primal heuristic <guideddiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/guideddiving/freq = 10

# frequency offset for calling primal heuristic <guideddiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 7]
heuristics/guideddiving/freqofs = 7

# maximal depth level to call primal heuristic <guideddiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/guideddiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/guideddiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/guideddiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/guideddiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/guideddiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/guideddiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/guideddiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/guideddiving/maxdiveubquotnosol = 1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
heuristics/guideddiving/maxdiveavgquotnosol = 1

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/guideddiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/guideddiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/guideddiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/guideddiving/onlylpbranchcands = FALSE

# priority of heuristic <zeroobj>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 100]
heuristics/zeroobj/priority = 100

# frequency for calling primal heuristic <zeroobj> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/zeroobj/freq = -1

# frequency offset for calling primal heuristic <zeroobj>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/zeroobj/freqofs = 0

# maximal depth level to call primal heuristic <zeroobj> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
heuristics/zeroobj/maxdepth = 0

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 1000]
heuristics/zeroobj/maxnodes = 1000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 100]
heuristics/zeroobj/nodesofs = 100

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 100]
heuristics/zeroobj/minnodes = 100

# maximum number of LP iterations to be performed in the subproblem
# [type: longint, advanced: TRUE, range: [-1,9223372036854775807], default: 5000]
heuristics/zeroobj/maxlpiters = 5000

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/zeroobj/nodesquot = 0.1

# factor by which zeroobj should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/zeroobj/minimprove = 0.01

# should all subproblem solutions be added to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/zeroobj/addallsols = FALSE

# should heuristic only be executed if no primal solution was found, yet?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/zeroobj/onlywithoutsol = TRUE

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/zeroobj/useuct = FALSE

# priority of heuristic <indicator>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -20200]
heuristics/indicator/priority = -20200

# frequency for calling primal heuristic <indicator> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/indicator/freq = 1

# frequency offset for calling primal heuristic <indicator>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/indicator/freqofs = 0

# maximal depth level to call primal heuristic <indicator> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/indicator/maxdepth = -1

# whether the one-opt heuristic should be started
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/indicator/oneopt = FALSE

# Try to improve other solutions by one-opt?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/indicator/improvesols = FALSE

# priority of heuristic <indicatordiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -150000]
heuristics/indicatordiving/priority = -150000

# frequency for calling primal heuristic <indicatordiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/indicatordiving/freq = 0

# frequency offset for calling primal heuristic <indicatordiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/indicatordiving/freqofs = 0

# maximal depth level to call primal heuristic <indicatordiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/indicatordiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/indicatordiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/indicatordiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/indicatordiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/indicatordiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/indicatordiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/indicatordiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/indicatordiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/indicatordiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/indicatordiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/indicatordiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 30]
heuristics/indicatordiving/lpsolvefreq = 30

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/indicatordiving/onlylpbranchcands = FALSE

# in violation case all fractional below this value are fixed to constant
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
heuristics/indicatordiving/roundingfrac = 0.5

# decides which roundingmode is selected (0: conservative, 1: aggressive)
# [type: int, advanced: FALSE, range: [0,1], default: 0]
heuristics/indicatordiving/roundingmode = 0

# which values of semi-continuous variables should get a high score? (0: low, 1: middle, 2: high)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
heuristics/indicatordiving/semicontscoremode = 0

# should varbound constraints be considered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/indicatordiving/usevarbounds = TRUE

# should heur run if there are no indicator constraints modeling semicont. vars?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/indicatordiving/runwithoutscinds = FALSE

# priority of heuristic <intdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003500]
heuristics/intdiving/priority = -1003500

# frequency for calling primal heuristic <intdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/intdiving/freq = -1

# frequency offset for calling primal heuristic <intdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 9]
heuristics/intdiving/freqofs = 9

# maximal depth level to call primal heuristic <intdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/intdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/intdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/intdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/intdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/intdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/intdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/intdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/intdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/intdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/intdiving/backtrack = TRUE

# priority of heuristic <intshifting>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -10000]
heuristics/intshifting/priority = -10000

# frequency for calling primal heuristic <intshifting> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/intshifting/freq = 10

# frequency offset for calling primal heuristic <intshifting>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/intshifting/freqofs = 0

# maximal depth level to call primal heuristic <intshifting> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/intshifting/maxdepth = -1

# priority of heuristic <linesearchdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1006000]
heuristics/linesearchdiving/priority = -1006000

# frequency for calling primal heuristic <linesearchdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/linesearchdiving/freq = 10

# frequency offset for calling primal heuristic <linesearchdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 6]
heuristics/linesearchdiving/freqofs = 6

# maximal depth level to call primal heuristic <linesearchdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/linesearchdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/linesearchdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/linesearchdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/linesearchdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/linesearchdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/linesearchdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/linesearchdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/linesearchdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/linesearchdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/linesearchdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/linesearchdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/linesearchdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/linesearchdiving/onlylpbranchcands = FALSE

# priority of heuristic <localbranching>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1102000]
heuristics/localbranching/priority = -1102000

# frequency for calling primal heuristic <localbranching> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/localbranching/freq = -1

# frequency offset for calling primal heuristic <localbranching>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/localbranching/freqofs = 0

# maximal depth level to call primal heuristic <localbranching> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/localbranching/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/localbranching/nodesofs = 1000

# radius (using Manhattan metric) of the incumbent's neighborhood to be searched
# [type: int, advanced: FALSE, range: [1,2147483647], default: 18]
heuristics/localbranching/neighborhoodsize = 18

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.05]
heuristics/localbranching/nodesquot = 0.05

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1.5]
heuristics/localbranching/lplimfac = 1.5

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1000]
heuristics/localbranching/minnodes = 1000

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10000]
heuristics/localbranching/maxnodes = 10000

# number of nodes without incumbent change that heuristic should wait
# [type: int, advanced: TRUE, range: [0,2147483647], default: 200]
heuristics/localbranching/nwaitingnodes = 200

# factor by which localbranching should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/localbranching/minimprove = 0.01

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/localbranching/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/localbranching/copycuts = TRUE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/localbranching/bestsollimit = 3

# priority of heuristic <locks>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 3000]
heuristics/locks/priority = 3000

# frequency for calling primal heuristic <locks> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/locks/freq = 0

# frequency offset for calling primal heuristic <locks>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/locks/freqofs = 0

# maximal depth level to call primal heuristic <locks> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/locks/maxdepth = -1

# maximum number of propagation rounds to be performed in each propagation call (-1: no limit, -2: parameter settings)
# [type: int, advanced: TRUE, range: [-2,2147483647], default: 2]
heuristics/locks/maxproprounds = 2

# minimum percentage of integer variables that have to be fixable
# [type: real, advanced: FALSE, range: [0,1], default: 0.65]
heuristics/locks/minfixingrate = 0.65

# probability for rounding a variable up in case of ties
# [type: real, advanced: FALSE, range: [0,1], default: 0.67]
heuristics/locks/roundupprobability = 0.67

# should a final sub-MIP be solved to costruct a feasible solution if the LP was not roundable?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/locks/usefinalsubmip = TRUE

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/locks/maxnodes = 5000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/locks/nodesofs = 500

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/locks/minnodes = 500

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/locks/nodesquot = 0.1

# factor by which locks heuristic should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/locks/minimprove = 0.01

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/locks/copycuts = TRUE

# should the locks be updated based on LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/locks/updatelocks = TRUE

# minimum fixing rate over all variables (including continuous) to solve LP
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/locks/minfixingratelp = 0

# priority of heuristic <lpface>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1104010]
heuristics/lpface/priority = -1104010

# frequency for calling primal heuristic <lpface> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 15]
heuristics/lpface/freq = 15

# frequency offset for calling primal heuristic <lpface>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/lpface/freqofs = 0

# maximal depth level to call primal heuristic <lpface> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/lpface/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 200]
heuristics/lpface/nodesofs = 200

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/lpface/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/lpface/minnodes = 50

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/lpface/nodesquot = 0.1

# required percentage of fixed integer variables in sub-MIP to run
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/lpface/minfixingrate = 0.1

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/lpface/lplimfac = 2

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/lpface/uselprows = TRUE

# should dually nonbasic rows be turned into equations?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/lpface/dualbasisequations = FALSE

# should the heuristic continue solving the same sub-SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/lpface/keepsubscip = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/lpface/copycuts = TRUE

# objective function in the sub-SCIP: (z)ero, (r)oot-LP-difference, (i)nference, LP (f)ractionality, (o)riginal
# [type: char, advanced: TRUE, range: {forzi}, default: z]
heuristics/lpface/subscipobjective = z

# the minimum active search tree path length along which lower bound hasn't changed before heuristic becomes active
# [type: int, advanced: TRUE, range: [0,65531], default: 5]
heuristics/lpface/minpathlen = 5

# priority of heuristic <alns>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1100500]
heuristics/alns/priority = -1100500

# frequency for calling primal heuristic <alns> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/alns/freq = 20

# frequency offset for calling primal heuristic <alns>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/alns/freqofs = 0

# maximal depth level to call primal heuristic <alns> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/alns/maxdepth = -1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/rens/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/rens/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/rens/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/rens/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/rins/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/rins/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/rins/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/rins/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/mutation/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/mutation/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/mutation/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/mutation/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/localbranching/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/localbranching/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/localbranching/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/localbranching/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/crossover/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/crossover/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/crossover/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/crossover/priority = 1

# the number of solutions that crossover should combine
# [type: int, advanced: TRUE, range: [2,10], default: 2]
heuristics/alns/crossover/nsols = 2

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/proximity/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/proximity/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/proximity/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/proximity/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/zeroobjective/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/zeroobjective/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/zeroobjective/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/zeroobjective/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/dins/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/dins/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/dins/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/dins/priority = 1

# number of pool solutions where binary solution values must agree
# [type: int, advanced: TRUE, range: [1,100], default: 5]
heuristics/alns/dins/npoolsols = 5

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/alns/trustregion/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/alns/trustregion/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/trustregion/active = FALSE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/alns/trustregion/priority = 1

# the penalty for each change in the binary variables from the candidate solution
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 100]
heuristics/alns/trustregion/violpenalty = 100

# show statistics on neighborhoods?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/shownbstats = FALSE

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/alns/maxnodes = 5000

# offset added to the nodes budget
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/alns/nodesofs = 500

# minimum number of nodes required to start a sub-SCIP
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/alns/minnodes = 50

# number of nodes since last incumbent solution that the heuristic should wait
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 25]
heuristics/alns/waitingnodes = 25

# fraction of nodes compared to the main SCIP for budget computation
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/alns/nodesquot = 0.1

# lower bound fraction of nodes compared to the main SCIP for budget computation
# [type: real, advanced: FALSE, range: [0,1], default: 0]
heuristics/alns/nodesquotmin = 0

# initial factor by which ALNS should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/alns/startminimprove = 0.01

# lower threshold for the minimal improvement over the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/alns/minimprovelow = 0.01

# upper bound for the minimal improvement over the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/alns/minimprovehigh = 0.01

# limit on the number of improving solutions in a sub-SCIP call
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/alns/nsolslim = 3

# the bandit algorithm: (u)pper confidence bounds, (e)xp.3, epsilon (g)reedy, exp.3-(i)x
# [type: char, advanced: TRUE, range: {uegi}, default: i]
heuristics/alns/banditalgo = i

# weight between uniform (gamma ~ 1) and weight driven (gamma ~ 0) probability distribution for exp3
# [type: real, advanced: TRUE, range: [0,1], default: 0.07041455]
heuristics/alns/gamma = 0.07041455

# reward offset between 0 and 1 at every observation for Exp.3
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/alns/beta = 0

# parameter to increase the confidence width in UCB
# [type: real, advanced: TRUE, range: [0,100], default: 0.0016]
heuristics/alns/alpha = 0.0016

# distances from fixed variables be used for variable prioritization
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/usedistances = TRUE

# should reduced cost scores be used for variable prioritization?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/useredcost = TRUE

# should the ALNS heuristic do more fixings by itself based on variable prioritization until the target fixing rate is reached?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/domorefixings = TRUE

# should the heuristic adjust the target fixing rate based on the success?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/adjustfixingrate = TRUE

# should the heuristic activate other sub-SCIP heuristics during its search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/usesubscipheurs = FALSE

# reward control to increase the weight of the simple solution indicator and decrease the weight of the closed gap reward
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/alns/rewardcontrol = 0.8

# factor by which target node number is eventually increased
# [type: real, advanced: TRUE, range: [1,100000], default: 1.05]
heuristics/alns/targetnodefactor = 1.05

# initial random seed for bandit algorithms and random decisions by neighborhoods
# [type: int, advanced: FALSE, range: [0,2147483647], default: 113]
heuristics/alns/seed = 113

# number of allowed executions of the heuristic on the same incumbent solution (-1: no limit, 0: number of active neighborhoods)
# [type: int, advanced: TRUE, range: [-1,100], default: -1]
heuristics/alns/maxcallssamesol = -1

# should the factor by which the minimum improvement is bound be dynamically updated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/adjustminimprove = FALSE

# should the target nodes be dynamically adjusted?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/adjusttargetnodes = TRUE

# increase exploration in epsilon-greedy bandit algorithm
# [type: real, advanced: TRUE, range: [0,1], default: 0.4685844]
heuristics/alns/eps = 0.4685844

# the reward baseline to separate successful and failed calls
# [type: real, advanced: TRUE, range: [0,0.99], default: 0.5]
heuristics/alns/rewardbaseline = 0.5

# should the bandit algorithms be reset when a new problem is read?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/resetweights = TRUE

# file name to store all rewards and the selection of the bandit
# [type: string, advanced: TRUE, default: "-"]
heuristics/alns/rewardfilename = "-"

# should random seeds of sub-SCIPs be altered to increase diversification?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/subsciprandseeds = FALSE

# should the reward be scaled by the effort?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/scalebyeffort = TRUE

# should cutting planes be copied to the sub-SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/copycuts = FALSE

# tolerance by which the fixing rate may be missed without generic fixing
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/alns/fixtol = 0.1

# tolerance by which the fixing rate may be exceeded without generic unfixing
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/alns/unfixtol = 0.1

# should local reduced costs be used for generic (un)fixing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/uselocalredcost = FALSE

# should pseudo cost scores be used for variable priorization?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/alns/usepscost = TRUE

# should the heuristic be executed multiple times during the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/alns/initduringroot = FALSE

# is statistics table <neighborhood> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/neighborhood/active = TRUE

# priority of heuristic <nlpdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003010]
heuristics/nlpdiving/priority = -1003010

# frequency for calling primal heuristic <nlpdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/nlpdiving/freq = 10

# frequency offset for calling primal heuristic <nlpdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 3]
heuristics/nlpdiving/freqofs = 3

# maximal depth level to call primal heuristic <nlpdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/nlpdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/nlpdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/nlpdiving/maxreldepth = 1

# minimial absolute number of allowed NLP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 200]
heuristics/nlpdiving/maxnlpiterabs = 200

# additional allowed number of NLP iterations relative to successfully found solutions
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
heuristics/nlpdiving/maxnlpiterrel = 10

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/nlpdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/nlpdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/nlpdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/nlpdiving/maxdiveavgquotnosol = 0

# maximal number of NLPs with feasible solution to solve during one dive
# [type: int, advanced: FALSE, range: [1,2147483647], default: 10]
heuristics/nlpdiving/maxfeasnlps = 10

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/nlpdiving/backtrack = TRUE

# should the LP relaxation be solved before the NLP relaxation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/nlpdiving/lp = FALSE

# prefer variables that are also fractional in LP solution?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/nlpdiving/preferlpfracs = FALSE

# heuristic will not run if less then this percentage of calls succeeded (0.0: no limit)
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/nlpdiving/minsuccquot = 0.1

# percentage of fractional variables that should be fixed before the next NLP solve
# [type: real, advanced: FALSE, range: [0,1], default: 0.2]
heuristics/nlpdiving/fixquot = 0.2

# should variables in a minimal cover be preferred?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/nlpdiving/prefercover = TRUE

# should a sub-MIP be solved if all cover variables are fixed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/nlpdiving/solvesubmip = FALSE

# should the NLP solver stop early if it converges slow?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/nlpdiving/nlpfastfail = TRUE

# which point should be used as starting point for the NLP solver? ('n'one, last 'f'easible, from dive's'tart)
# [type: char, advanced: TRUE, range: {fns}, default: s]
heuristics/nlpdiving/nlpstart = s

# which variable selection should be used? ('f'ractionality, 'c'oefficient, 'p'seudocost, 'g'uided, 'd'ouble, 'v'eclen)
# [type: char, advanced: FALSE, range: {fcpgdv}, default: d]
heuristics/nlpdiving/varselrule = d

# priority of heuristic <mutation>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1103010]
heuristics/mutation/priority = -1103010

# frequency for calling primal heuristic <mutation> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/mutation/freq = -1

# frequency offset for calling primal heuristic <mutation>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 8]
heuristics/mutation/freqofs = 8

# maximal depth level to call primal heuristic <mutation> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/mutation/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
heuristics/mutation/nodesofs = 500

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5000]
heuristics/mutation/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 500]
heuristics/mutation/minnodes = 500

# number of nodes without incumbent change that heuristic should wait
# [type: int, advanced: TRUE, range: [0,2147483647], default: 200]
heuristics/mutation/nwaitingnodes = 200

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/mutation/nodesquot = 0.1

# percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [1e-06,0.999999], default: 0.8]
heuristics/mutation/minfixingrate = 0.8

# factor by which mutation should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/mutation/minimprove = 0.01

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/mutation/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/mutation/copycuts = TRUE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
heuristics/mutation/bestsollimit = -1

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/mutation/useuct = FALSE

# priority of heuristic <multistart>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2100000]
heuristics/multistart/priority = -2100000

# frequency for calling primal heuristic <multistart> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/multistart/freq = 0

# frequency offset for calling primal heuristic <multistart>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/multistart/freqofs = 0

# maximal depth level to call primal heuristic <multistart> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/multistart/maxdepth = -1

# number of random points generated per execution call
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
heuristics/multistart/nrndpoints = 100

# maximum variable domain size for unbounded variables
# [type: real, advanced: FALSE, range: [0,1e+20], default: 20000]
heuristics/multistart/maxboundsize = 20000

# number of iterations to reduce the maximum violation of a point
# [type: int, advanced: FALSE, range: [0,2147483647], default: 300]
heuristics/multistart/maxiter = 300

# minimum required improving factor to proceed in improvement of a single point
# [type: real, advanced: FALSE, range: [-1e+20,1e+20], default: 0.05]
heuristics/multistart/minimprfac = 0.05

# number of iteration when checking the minimum improvement
# [type: int, advanced: FALSE, range: [1,2147483647], default: 10]
heuristics/multistart/minimpriter = 10

# maximum distance between two points in the same cluster
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.15]
heuristics/multistart/maxreldist = 0.15

# limit for gradient computations for all improvePoint() calls (0 for no limit)
# [type: real, advanced: FALSE, range: [0,1e+20], default: 5000000]
heuristics/multistart/gradlimit = 5000000

# maximum number of considered clusters per heuristic call
# [type: int, advanced: FALSE, range: [0,2147483647], default: 3]
heuristics/multistart/maxncluster = 3

# should the heuristic run only on continuous problems?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/multistart/onlynlps = TRUE

# priority of heuristic <mpec>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2050000]
heuristics/mpec/priority = -2050000

# frequency for calling primal heuristic <mpec> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 50]
heuristics/mpec/freq = 50

# frequency offset for calling primal heuristic <mpec>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/mpec/freqofs = 0

# maximal depth level to call primal heuristic <mpec> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/mpec/maxdepth = -1

# initial regularization right-hand side value
# [type: real, advanced: FALSE, range: [0,0.25], default: 0.125]
heuristics/mpec/inittheta = 0.125

# regularization update factor
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
heuristics/mpec/sigma = 0.5

# maximum number of NLP iterations per solve
# [type: real, advanced: FALSE, range: [0,1], default: 0.001]
heuristics/mpec/subnlptrigger = 0.001

# maximum cost available for solving NLPs per call of the heuristic
# [type: real, advanced: FALSE, range: [0,1e+20], default: 100000000]
heuristics/mpec/maxnlpcost = 100000000

# factor by which heuristic should at least improve the incumbent
# [type: real, advanced: FALSE, range: [0,1], default: 0.01]
heuristics/mpec/minimprove = 0.01

# minimum amount of gap left in order to call the heuristic
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.05]
heuristics/mpec/mingapleft = 0.05

# maximum number of iterations of the MPEC loop
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
heuristics/mpec/maxiter = 100

# maximum number of NLP iterations per solve
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
heuristics/mpec/maxnlpiter = 500

# maximum number of consecutive calls for which the heuristic did not find an improving solution
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
heuristics/mpec/maxnunsucc = 10

# priority of heuristic <objpscostdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1004000]
heuristics/objpscostdiving/priority = -1004000

# frequency for calling primal heuristic <objpscostdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/objpscostdiving/freq = 20

# frequency offset for calling primal heuristic <objpscostdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 4]
heuristics/objpscostdiving/freqofs = 4

# maximal depth level to call primal heuristic <objpscostdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/objpscostdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/objpscostdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/objpscostdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to total iteration number
# [type: real, advanced: FALSE, range: [0,1], default: 0.01]
heuristics/objpscostdiving/maxlpiterquot = 0.01

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/objpscostdiving/maxlpiterofs = 1000

# total number of feasible solutions found up to which heuristic is called (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
heuristics/objpscostdiving/maxsols = -1

# maximal diving depth: number of binary/integer variables times depthfac
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.5]
heuristics/objpscostdiving/depthfac = 0.5

# maximal diving depth factor if no feasible solution was found yet
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
heuristics/objpscostdiving/depthfacnosol = 2

# priority of heuristic <octane>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1008000]
heuristics/octane/priority = -1008000

# frequency for calling primal heuristic <octane> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/octane/freq = -1

# frequency offset for calling primal heuristic <octane>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/octane/freqofs = 0

# maximal depth level to call primal heuristic <octane> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/octane/maxdepth = -1

# number of 0-1-points to be tested as possible solutions by OCTANE
# [type: int, advanced: TRUE, range: [1,2147483647], default: 100]
heuristics/octane/fmax = 100

# number of 0-1-points to be tested at first whether they violate a common row
# [type: int, advanced: TRUE, range: [1,2147483647], default: 10]
heuristics/octane/ffirst = 10

# execute OCTANE only in the space of fractional variables (TRUE) or in the full space?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/octane/usefracspace = TRUE

# should the inner normal of the objective be used as one ray direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/octane/useobjray = TRUE

# should the average of the basic cone be used as one ray direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/octane/useavgray = TRUE

# should the difference between the root solution and the current LP solution be used as one ray direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/octane/usediffray = FALSE

# should the weighted average of the basic cone be used as one ray direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/octane/useavgwgtray = TRUE

# should the weighted average of the nonbasic cone be used as one ray direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/octane/useavgnbray = TRUE

# priority of heuristic <ofins>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 60000]
heuristics/ofins/priority = 60000

# frequency for calling primal heuristic <ofins> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/ofins/freq = 0

# frequency offset for calling primal heuristic <ofins>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/ofins/freqofs = 0

# maximal depth level to call primal heuristic <ofins> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
heuristics/ofins/maxdepth = 0

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/ofins/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/ofins/minnodes = 50

# maximal rate of changed coefficients
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
heuristics/ofins/maxchangerate = 0.5

# maximal rate of change per coefficient to get fixed
# [type: real, advanced: FALSE, range: [0,1], default: 0.04]
heuristics/ofins/maxchange = 0.04

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/ofins/copycuts = TRUE

# should all subproblem solutions be added to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/ofins/addallsols = FALSE

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/ofins/nodesofs = 500

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/ofins/nodesquot = 0.1

# factor by which RENS should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/ofins/minimprove = 0.01

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/ofins/lplimfac = 2

# priority of heuristic <oneopt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -20000]
heuristics/oneopt/priority = -20000

# frequency for calling primal heuristic <oneopt> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/oneopt/freq = 1

# frequency offset for calling primal heuristic <oneopt>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/oneopt/freqofs = 0

# maximal depth level to call primal heuristic <oneopt> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/oneopt/maxdepth = -1

# should the objective be weighted with the potential shifting value when sorting the shifting candidates?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/oneopt/weightedobj = TRUE

# should the heuristic be called before and during the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/oneopt/duringroot = TRUE

# should the construction of the LP be forced even if LP solving is deactivated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/oneopt/forcelpconstruction = FALSE

# should the heuristic be called before presolving?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/oneopt/beforepresol = FALSE

# should the heuristic continue to run as long as improvements are found?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/oneopt/useloop = TRUE

# priority of heuristic <padm>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 70000]
heuristics/padm/priority = 70000

# frequency for calling primal heuristic <padm> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/padm/freq = 0

# frequency offset for calling primal heuristic <padm>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/padm/freqofs = 0

# maximal depth level to call primal heuristic <padm> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/padm/maxdepth = -1

# maximum number of nodes to regard in all subproblems
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/padm/maxnodes = 5000

# minimum number of nodes to regard in one subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/padm/minnodes = 50

# factor to control nodelimits of subproblems
# [type: real, advanced: TRUE, range: [0,0.99], default: 0.8]
heuristics/padm/nodefac = 0.8

# maximal number of ADM iterations in each penalty loop
# [type: int, advanced: TRUE, range: [1,100], default: 4]
heuristics/padm/admiterations = 4

# maximal number of penalty iterations
# [type: int, advanced: TRUE, range: [1,100000], default: 100]
heuristics/padm/penaltyiterations = 100

# mipgap at start
# [type: real, advanced: TRUE, range: [0,16], default: 2]
heuristics/padm/gap = 2

# should the problem get reoptimized with the original objective function?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/padm/reoptimize = TRUE

# enable sigmoid rescaling of penalty parameters
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/padm/scaling = TRUE

# should linking constraints be assigned?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/padm/assignlinking = TRUE

# should the original problem be used? This is only for testing and not recommended!
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/padm/original = FALSE

# should the heuristic run before or after the processing of the node? (0: before, 1: after, 2: both)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
heuristics/padm/timing = 0

# priority of heuristic <proximity>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2000000]
heuristics/proximity/priority = -2000000

# frequency for calling primal heuristic <proximity> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/proximity/freq = -1

# frequency offset for calling primal heuristic <proximity>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/proximity/freqofs = 0

# maximal depth level to call primal heuristic <proximity> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/proximity/maxdepth = -1

# should subproblem be constructed based on LP row information?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/proximity/uselprows = FALSE

# should the heuristic immediately run again on its newly found solution?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/proximity/restart = TRUE

# should the heuristic solve a final LP in case of continuous objective variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/proximity/usefinallp = FALSE

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 10000]
heuristics/proximity/maxnodes = 10000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/proximity/nodesofs = 50

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 1]
heuristics/proximity/minnodes = 1

# maximum number of LP iterations to be performed in the subproblem
# [type: longint, advanced: TRUE, range: [-1,9223372036854775807], default: 100000]
heuristics/proximity/maxlpiters = 100000

# minimum number of LP iterations performed in subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 200]
heuristics/proximity/minlpiters = 200

# waiting nodes since last incumbent before heuristic is executed
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 100]
heuristics/proximity/waitingnodes = 100

# factor by which proximity should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.02]
heuristics/proximity/minimprove = 0.02

# sub-MIP node limit w.r.t number of original nodes
# [type: real, advanced: TRUE, range: [0,1e+20], default: 0.1]
heuristics/proximity/nodesquot = 0.1

# threshold for percentage of binary variables required to start
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/proximity/binvarquot = 0.1

# quotient of sub-MIP LP iterations with respect to LP iterations so far
# [type: real, advanced: TRUE, range: [0,1], default: 0.2]
heuristics/proximity/lpitersquot = 0.2

# minimum primal-dual gap for which the heuristic is executed
# [type: real, advanced: TRUE, range: [0,1e+20], default: 0.01]
heuristics/proximity/mingap = 0.01

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/proximity/useuct = FALSE

# priority of heuristic <pscostdiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1002000]
heuristics/pscostdiving/priority = -1002000

# frequency for calling primal heuristic <pscostdiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/pscostdiving/freq = 10

# frequency offset for calling primal heuristic <pscostdiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 2]
heuristics/pscostdiving/freqofs = 2

# maximal depth level to call primal heuristic <pscostdiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/pscostdiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/pscostdiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/pscostdiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/pscostdiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/pscostdiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/pscostdiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/pscostdiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/pscostdiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/pscostdiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/pscostdiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/pscostdiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/pscostdiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/pscostdiving/onlylpbranchcands = TRUE

# priority of heuristic <randrounding>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -200]
heuristics/randrounding/priority = -200

# frequency for calling primal heuristic <randrounding> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/randrounding/freq = 20

# frequency offset for calling primal heuristic <randrounding>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/randrounding/freqofs = 0

# maximal depth level to call primal heuristic <randrounding> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/randrounding/maxdepth = -1

# should the heuristic only be called once per node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/randrounding/oncepernode = FALSE

# should the heuristic apply the variable lock strategy of simple rounding, if possible?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/randrounding/usesimplerounding = FALSE

# should the probing part of the heuristic be applied exclusively at the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/randrounding/propagateonlyroot = TRUE

# limit of rounds for each propagation call
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1]
heuristics/randrounding/maxproprounds = 1

# priority of heuristic <rens>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1100000]
heuristics/rens/priority = -1100000

# frequency for calling primal heuristic <rens> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/rens/freq = 0

# frequency offset for calling primal heuristic <rens>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/rens/freqofs = 0

# maximal depth level to call primal heuristic <rens> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/rens/maxdepth = -1

# minimum percentage of integer variables that have to be fixable
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
heuristics/rens/minfixingrate = 0.5

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/rens/maxnodes = 5000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/rens/nodesofs = 500

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/rens/minnodes = 50

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/rens/nodesquot = 0.1

# factor by which RENS should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/rens/minimprove = 0.01

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/rens/lplimfac = 2

# solution that is used for fixing values ('l'p relaxation, 'n'lp relaxation)
# [type: char, advanced: FALSE, range: {nl}, default: l]
heuristics/rens/startsol = l

# should general integers get binary bounds [floor(.),ceil(.)] ?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/rens/binarybounds = TRUE

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rens/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/rens/copycuts = TRUE

# should the RENS sub-CIP get its own full time limit? This is only for testing and not recommended!
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rens/extratime = FALSE

# should all subproblem solutions be added to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rens/addallsols = FALSE

# should the RENS sub-CIP be solved with cuts, conflicts, strong branching,... This is only for testing and not recommended!
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rens/fullscale = FALSE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
heuristics/rens/bestsollimit = -1

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rens/useuct = FALSE

# priority of heuristic <reoptsols>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 40000]
heuristics/reoptsols/priority = 40000

# frequency for calling primal heuristic <reoptsols> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/reoptsols/freq = 0

# frequency offset for calling primal heuristic <reoptsols>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/reoptsols/freqofs = 0

# maximal depth level to call primal heuristic <reoptsols> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
heuristics/reoptsols/maxdepth = 0

# maximal number solutions which should be checked. (-1: all)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1000]
heuristics/reoptsols/maxsols = 1000

# check solutions of the last k runs. (-1: all)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
heuristics/reoptsols/maxruns = -1

# priority of heuristic <repair>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -20]
heuristics/repair/priority = -20

# frequency for calling primal heuristic <repair> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/repair/freq = -1

# frequency offset for calling primal heuristic <repair>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/repair/freqofs = 0

# maximal depth level to call primal heuristic <repair> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/repair/maxdepth = -1

# file name of a solution to be used as infeasible starting point, [-] if not available
# [type: string, advanced: FALSE, default: "-"]
heuristics/repair/filename = "-"

# True : fractional variables which are not fractional in the given solution are rounded, FALSE : solving process of this heuristic is stopped. 
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/repair/roundit = TRUE

# should a scaled objective function for original variables be used in repair subproblem?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/repair/useobjfactor = FALSE

# should variable fixings be used in repair subproblem?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/repair/usevarfix = TRUE

# should slack variables be used in repair subproblem?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/repair/useslackvars = FALSE

# factor for the potential of var fixings
# [type: real, advanced: TRUE, range: [0,100], default: 2]
heuristics/repair/alpha = 2

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
heuristics/repair/nodesofs = 500

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5000]
heuristics/repair/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
heuristics/repair/minnodes = 50

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/repair/nodesquot = 0.1

# minimum percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [0,1], default: 0.3]
heuristics/repair/minfixingrate = 0.3

# priority of heuristic <rins>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1101000]
heuristics/rins/priority = -1101000

# frequency for calling primal heuristic <rins> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 25]
heuristics/rins/freq = 25

# frequency offset for calling primal heuristic <rins>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/rins/freqofs = 0

# maximal depth level to call primal heuristic <rins> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/rins/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
heuristics/rins/nodesofs = 500

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5000]
heuristics/rins/maxnodes = 5000

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
heuristics/rins/minnodes = 50

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.3]
heuristics/rins/nodesquot = 0.3

# number of nodes without incumbent change that heuristic should wait
# [type: int, advanced: TRUE, range: [0,2147483647], default: 200]
heuristics/rins/nwaitingnodes = 200

# factor by which rins should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/rins/minimprove = 0.01

# minimum percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [0,1], default: 0.3]
heuristics/rins/minfixingrate = 0.3

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
heuristics/rins/lplimfac = 2

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rins/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/rins/copycuts = TRUE

# should uct node selection be used at the beginning of the search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rins/useuct = FALSE

# priority of heuristic <rootsoldiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1005000]
heuristics/rootsoldiving/priority = -1005000

# frequency for calling primal heuristic <rootsoldiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 20]
heuristics/rootsoldiving/freq = 20

# frequency offset for calling primal heuristic <rootsoldiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 5]
heuristics/rootsoldiving/freqofs = 5

# maximal depth level to call primal heuristic <rootsoldiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/rootsoldiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/rootsoldiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/rootsoldiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.01]
heuristics/rootsoldiving/maxlpiterquot = 0.01

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/rootsoldiving/maxlpiterofs = 1000

# total number of feasible solutions found up to which heuristic is called (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
heuristics/rootsoldiving/maxsols = -1

# maximal diving depth: number of binary/integer variables times depthfac
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.5]
heuristics/rootsoldiving/depthfac = 0.5

# maximal diving depth factor if no feasible solution was found yet
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
heuristics/rootsoldiving/depthfacnosol = 2

# soft rounding factor to fade out objective coefficients
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/rootsoldiving/alpha = 0.9

# priority of heuristic <rounding>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000]
heuristics/rounding/priority = -1000

# frequency for calling primal heuristic <rounding> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/rounding/freq = 1

# frequency offset for calling primal heuristic <rounding>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/rounding/freqofs = 0

# maximal depth level to call primal heuristic <rounding> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/rounding/maxdepth = -1

# number of calls per found solution that are considered as standard success, a higher factor causes the heuristic to be called more often
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 100]
heuristics/rounding/successfactor = 100

# should the heuristic only be called once per node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/rounding/oncepernode = FALSE

# priority of heuristic <scheduler>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -30000]
heuristics/scheduler/priority = -30000

# frequency for calling primal heuristic <scheduler> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/scheduler/freq = -1

# frequency offset for calling primal heuristic <scheduler>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/scheduler/freqofs = 0

# maximal depth level to call primal heuristic <scheduler> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/scheduler/maxdepth = -1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/rens/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/rens/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/rens/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/rens/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/rins/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/rins/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/rins/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/rins/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/mutation/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/mutation/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/mutation/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/mutation/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/localbranching/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/localbranching/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/localbranching/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/localbranching/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/crossover/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/crossover/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/crossover/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/crossover/priority = 1

# the number of solutions that crossover should combine
# [type: int, advanced: TRUE, range: [2,10], default: 2]
heuristics/scheduler/crossover/nsols = 2

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/proximity/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/proximity/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/proximity/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/proximity/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/zeroobjective/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/zeroobjective/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/zeroobjective/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/zeroobjective/priority = 1

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/dins/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/dins/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/dins/active = TRUE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/dins/priority = 1

# number of pool solutions where binary solution values must agree
# [type: int, advanced: TRUE, range: [1,100], default: 5]
heuristics/scheduler/dins/npoolsols = 5

# minimum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/trustregion/minfixingrate = 0.3

# maximum fixing rate for this neighborhood
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/scheduler/trustregion/maxfixingrate = 0.9

# is this neighborhood active?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/trustregion/active = FALSE

# positive call priority to initialize bandit algorithms
# [type: real, advanced: TRUE, range: [0.01,1], default: 1]
heuristics/scheduler/trustregion/priority = 1

# the penalty for each change in the binary variables from the candidate solution
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 100]
heuristics/scheduler/trustregion/violpenalty = 100

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/scheduler/maxnodes = 500

# offset added to the nodes budget
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/scheduler/nodesofs = 500

# minimum number of nodes required to start a sub-SCIP
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 50]
heuristics/scheduler/minnodes = 50

# number of nodes since last incumbent solution that the heuristic should wait
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 0]
heuristics/scheduler/waitingnodes = 0

# initial node limit for LNS heuristics
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
heuristics/scheduler/initlnsnodelimit = 50

# initial node limit for diving heuristics
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/scheduler/initdivingnodelimit = 500

# fraction of nodes compared to the main SCIP for budget computation
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/scheduler/nodesquot = 0.1

# lower bound fraction of nodes compared to the main SCIP for budget computation
# [type: real, advanced: FALSE, range: [0,1], default: 0]
heuristics/scheduler/nodesquotmin = 0

# limit on the number of improving solutions in a sub-SCIP call
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/scheduler/nsolslim = 3

# the bandit algorithm: (u)pper confidence bounds, (e)xp.3, epsilon (g)reedy, exp.3-(i)x
# [type: char, advanced: TRUE, range: {uegi}, default: i]
heuristics/scheduler/banditalgo = i

# weight between uniform (gamma ~ 1) and weight driven (gamma ~ 0) probability distribution for exp3
# [type: real, advanced: TRUE, range: [0,1], default: 0.07041455]
heuristics/scheduler/gamma = 0.07041455

# reward offset between 0 and 1 at every observation for Exp.3
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/scheduler/beta = 0

# parameter to increase the confidence width in UCB
# [type: real, advanced: TRUE, range: [0,100], default: 0.0016]
heuristics/scheduler/alpha = 0.0016

# distances from fixed variables be used for variable prioritization
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/usedistances = TRUE

# should reduced cost scores be used for variable prioritization?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/useredcost = TRUE

# should pseudo cost scores be used for variable priorization?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/usepscost = TRUE

# should local reduced costs be used for generic (un)fixing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/uselocalredcost = FALSE

# should the heuristic activate other sub-SCIP heuristics during its search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/usesubscipheurs = FALSE

# factor by which target node number is eventually increased
# [type: real, advanced: TRUE, range: [1,100000], default: 1.05]
heuristics/scheduler/targetnodefactor = 1.05

# initial random seed for bandit algorithms and random decisions by neighborhoods
# [type: int, advanced: FALSE, range: [0,2147483647], default: 113]
heuristics/scheduler/seed = 113

# number of allowed executions of the heuristic on the same incumbent solution (-1: no limit, 0: number of active neighborhoods)
# [type: int, advanced: TRUE, range: [-1,100], default: -1]
heuristics/scheduler/maxcallssamesol = -1

# increase exploration in epsilon-greedy bandit algorithm
# [type: real, advanced: TRUE, range: [0,1], default: 0.4685844]
heuristics/scheduler/eps = 0.4685844

# TRUE if modified version of the epsilon-greedy bandit algorithm should be used
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/epsgreedy_usemod = TRUE

# weight by how much finding a new incumbent is rewarded in reward function
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/solrewardweight = 0.3

# weight by how much effort is rewarded in reward function
# [type: real, advanced: TRUE, range: [0,1], default: 0.2]
heuristics/scheduler/effortrewardweight = 0.2

# weight by how much quality of a new incumbent is rewarded in reward function
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
heuristics/scheduler/qualrewardweight = 0.3

# weight by how much number of conflicts found by diving is rewarded in reward function
# [type: real, advanced: TRUE, range: [0,1], default: 0.2]
heuristics/scheduler/conflictrewardweight = 0.2

# should the bandit algorithms be reset when a new problem is read?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/resetweights = FALSE

# should random seeds of sub-SCIPs be altered to increase diversification?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/subsciprandseeds = FALSE

# should cutting planes be copied to the sub-SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/copycuts = FALSE

# tolerance by which the fixing rate may be missed without generic fixing
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/scheduler/fixtol = 0.1

# tolerance by which the fixing rate may be exceeded without generic unfixing
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/scheduler/unfixtol = 0.1

# time limit for a single heuristic run
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 60]
heuristics/scheduler/heurtimelimit = 60

# should the heuristic be executed multiple times during the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/scheduler/initduringroot = FALSE

# should the default priorities be used at the root node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/scheduler/defaultroot = TRUE

# number of heuristics picked by the scheduler in one call (-1: number of controlled heuristics, 0: until new incumbent is found)
# [type: int, advanced: TRUE, range: [-1,100], default: 5]
heuristics/scheduler/nselections = 5

# is statistics table <scheduler> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/scheduler/active = TRUE

# priority of heuristic <shiftandpropagate>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 1000]
heuristics/shiftandpropagate/priority = 1000

# frequency for calling primal heuristic <shiftandpropagate> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/shiftandpropagate/freq = 0

# frequency offset for calling primal heuristic <shiftandpropagate>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/shiftandpropagate/freqofs = 0

# maximal depth level to call primal heuristic <shiftandpropagate> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/shiftandpropagate/maxdepth = -1

# The number of propagation rounds used for each propagation
# [type: int, advanced: TRUE, range: [-1,1000], default: 10]
heuristics/shiftandpropagate/nproprounds = 10

# Should continuous variables be relaxed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/relax = TRUE

# Should domains be reduced by probing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/probing = TRUE

# Should heuristic only be executed if no primal solution was found, yet?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/onlywithoutsol = TRUE

# The number of cutoffs before heuristic stops
# [type: int, advanced: TRUE, range: [-1,1000000], default: 15]
heuristics/shiftandpropagate/cutoffbreaker = 15

# the key for variable sorting: (n)orms down, norms (u)p, (v)iolations down, viola(t)ions up, or (r)andom
# [type: char, advanced: TRUE, range: {nrtuv}, default: v]
heuristics/shiftandpropagate/sortkey = v

# Should variables be sorted for the heuristic?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/sortvars = TRUE

# should variable statistics be collected during probing?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/collectstats = TRUE

# Should the heuristic stop calculating optimal shift values when no more rows are violated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/stopafterfeasible = TRUE

# Should binary variables be shifted first?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/preferbinaries = TRUE

# should variables with a zero shifting value be delayed instead of being fixed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/shiftandpropagate/nozerofixing = FALSE

# should binary variables with no locks in one direction be fixed to that direction?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/fixbinlocks = TRUE

# should binary variables with no locks be preferred in the ordering?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/shiftandpropagate/binlocksfirst = FALSE

# should coefficients and left/right hand sides be normalized by max row coeff?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/normalize = TRUE

# should row weight be increased every time the row is violated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/shiftandpropagate/updateweights = FALSE

# should implicit integer variables be treated as continuous variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/shiftandpropagate/impliscontinuous = TRUE

# should the heuristic choose the best candidate in every round? (set to FALSE for static order)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/shiftandpropagate/selectbest = FALSE

# maximum percentage of allowed cutoffs before stopping the heuristic
# [type: real, advanced: TRUE, range: [0,2], default: 0]
heuristics/shiftandpropagate/maxcutoffquot = 0

# minimum fixing rate over all variables (including continuous) to solve LP
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/shiftandpropagate/minfixingratelp = 0

# priority of heuristic <shifting>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -5000]
heuristics/shifting/priority = -5000

# frequency for calling primal heuristic <shifting> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/shifting/freq = 10

# frequency offset for calling primal heuristic <shifting>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/shifting/freqofs = 0

# maximal depth level to call primal heuristic <shifting> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/shifting/maxdepth = -1

# priority of heuristic <simplerounding>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -30]
heuristics/simplerounding/priority = -30

# frequency for calling primal heuristic <simplerounding> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/simplerounding/freq = 1

# frequency offset for calling primal heuristic <simplerounding>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/simplerounding/freqofs = 0

# maximal depth level to call primal heuristic <simplerounding> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/simplerounding/maxdepth = -1

# should the heuristic only be called once per node?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/simplerounding/oncepernode = FALSE

# priority of heuristic <subnlp>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2000010]
heuristics/subnlp/priority = -2000010

# frequency for calling primal heuristic <subnlp> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/subnlp/freq = 1

# frequency offset for calling primal heuristic <subnlp>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/subnlp/freqofs = 0

# maximal depth level to call primal heuristic <subnlp> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/subnlp/maxdepth = -1

# verbosity level of NLP solver
# [type: int, advanced: FALSE, range: [0,65535], default: 0]
heuristics/subnlp/nlpverblevel = 0

# number of nodes added to the current number of nodes when computing itercontingent (higher value runs heuristic more often in early search)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1600]
heuristics/subnlp/nodesoffset = 1600

# factor on number of nodes in SCIP (plus nodesoffset) to compute itercontingent (higher value runs heuristics more frequently)
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.3]
heuristics/subnlp/nodesfactor = 0.3

# exponent for power of success rate to be multiplied with itercontingent (lower value decreases impact of success rate)
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1]
heuristics/subnlp/successrateexp = 1

# number of iterations used for initial NLP solves
# [type: int, advanced: FALSE, range: [0,2147483647], default: 300]
heuristics/subnlp/iterinit = 300

# number of successful NLP solves until switching to iterlimit guess and using success rate
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2]
heuristics/subnlp/ninitsolves = 2

# minimal number of iterations for NLP solves
# [type: int, advanced: FALSE, range: [0,2147483647], default: 20]
heuristics/subnlp/itermin = 20

# absolute optimality tolerance to use for NLP solves
# [type: real, advanced: TRUE, range: [0,1], default: 1e-07]
heuristics/subnlp/opttol = 1e-07

# factor on SCIP feasibility tolerance for NLP solves if resolving when NLP solution not feasible in CIP
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/subnlp/feastolfactor = 0.1

# limit on number of presolve rounds in sub-SCIP (-1 for unlimited, 0 for no presolve)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
heuristics/subnlp/maxpresolverounds = -1

# presolve emphasis in sub-SCIP (0: default, 1: aggressive, 2: fast, 3: off)
# [type: int, advanced: FALSE, range: [0,3], default: 2]
heuristics/subnlp/presolveemphasis = 2

# whether to set cutoff in sub-SCIP to current primal bound
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/subnlp/setcutoff = TRUE

# whether to add constraints that forbid specific fixings that turned out to be infeasible
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/subnlp/forbidfixings = FALSE

# whether to keep SCIP copy or to create new copy each time heuristic is applied
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/subnlp/keepcopy = TRUE

# percentage of NLP solves with infeasible status required to tell NLP solver to expect an infeasible NLP
# [type: real, advanced: FALSE, range: [0,1], default: 0]
heuristics/subnlp/expectinfeas = 0

# priority of heuristic <trivial>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10000]
heuristics/trivial/priority = 10000

# frequency for calling primal heuristic <trivial> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/trivial/freq = 0

# frequency offset for calling primal heuristic <trivial>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/trivial/freqofs = 0

# maximal depth level to call primal heuristic <trivial> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/trivial/maxdepth = -1

# priority of heuristic <trivialnegation>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 39990]
heuristics/trivialnegation/priority = 39990

# frequency for calling primal heuristic <trivialnegation> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/trivialnegation/freq = 0

# frequency offset for calling primal heuristic <trivialnegation>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/trivialnegation/freqofs = 0

# maximal depth level to call primal heuristic <trivialnegation> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: 0]
heuristics/trivialnegation/maxdepth = 0

# priority of heuristic <trustregion>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1102010]
heuristics/trustregion/priority = -1102010

# frequency for calling primal heuristic <trustregion> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/trustregion/freq = -1

# frequency offset for calling primal heuristic <trustregion>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/trustregion/freqofs = 0

# maximal depth level to call primal heuristic <trustregion> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/trustregion/maxdepth = -1

# number of nodes added to the contingent of the total nodes
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/trustregion/nodesofs = 1000

# the number of binary variables necessary to run the heuristic
# [type: int, advanced: FALSE, range: [1,2147483647], default: 10]
heuristics/trustregion/minbinvars = 10

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.05]
heuristics/trustregion/nodesquot = 0.05

# factor by which the limit on the number of LP depends on the node limit
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1.5]
heuristics/trustregion/lplimfac = 1.5

# minimum number of nodes required to start the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
heuristics/trustregion/minnodes = 100

# maximum number of nodes to regard in the subproblem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10000]
heuristics/trustregion/maxnodes = 10000

# number of nodes without incumbent change that heuristic should wait
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
heuristics/trustregion/nwaitingnodes = 1

# should subproblem be created out of the rows in the LP rows?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/trustregion/uselprows = FALSE

# if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/trustregion/copycuts = TRUE

# limit on number of improving incumbent solutions in sub-CIP
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 3]
heuristics/trustregion/bestsollimit = 3

# the penalty for each change in the binary variables from the candidate solution
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 100]
heuristics/trustregion/violpenalty = 100

# the minimum absolute improvement in the objective function value
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.01]
heuristics/trustregion/objminimprove = 0.01

# priority of heuristic <trysol>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -3000010]
heuristics/trysol/priority = -3000010

# frequency for calling primal heuristic <trysol> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/trysol/freq = 1

# frequency offset for calling primal heuristic <trysol>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/trysol/freqofs = 0

# maximal depth level to call primal heuristic <trysol> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/trysol/maxdepth = -1

# priority of heuristic <twoopt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -20100]
heuristics/twoopt/priority = -20100

# frequency for calling primal heuristic <twoopt> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
heuristics/twoopt/freq = -1

# frequency offset for calling primal heuristic <twoopt>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/twoopt/freqofs = 0

# maximal depth level to call primal heuristic <twoopt> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/twoopt/maxdepth = -1

#  Should Integer-2-Optimization be applied or not?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/twoopt/intopt = FALSE

# user parameter to determine number of nodes to wait after last best solution before calling heuristic
# [type: int, advanced: TRUE, range: [0,10000], default: 0]
heuristics/twoopt/waitingnodes = 0

# maximum number of slaves for one master variable
# [type: int, advanced: TRUE, range: [-1,1000000], default: 199]
heuristics/twoopt/maxnslaves = 199

# parameter to determine the percentage of rows two variables have to share before they are considered equal
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
heuristics/twoopt/matchingrate = 0.5

# priority of heuristic <undercover>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1110000]
heuristics/undercover/priority = -1110000

# frequency for calling primal heuristic <undercover> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/undercover/freq = 0

# frequency offset for calling primal heuristic <undercover>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/undercover/freqofs = 0

# maximal depth level to call primal heuristic <undercover> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/undercover/maxdepth = -1

# prioritized sequence of fixing values used ('l'p relaxation, 'n'lp relaxation, 'i'ncumbent solution)
# [type: string, advanced: FALSE, default: "li"]
heuristics/undercover/fixingalts = "li"

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/undercover/maxnodes = 500

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/undercover/minnodes = 500

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/undercover/nodesofs = 500

# weight for conflict score in fixing order
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1000]
heuristics/undercover/conflictweight = 1000

# weight for cutoff score in fixing order
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
heuristics/undercover/cutoffweight = 1

# weight for inference score in fixing order
# [type: real, advanced: TRUE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 1]
heuristics/undercover/inferenceweight = 1

# maximum coversize (as fraction of total number of variables)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/undercover/maxcoversizevars = 1

# maximum coversize (as ratio to the percentage of non-affected constraints)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1.79769313486232e+308]
heuristics/undercover/maxcoversizeconss = 1.79769313486232e+308

# minimum percentage of nonlinear constraints in the original problem
# [type: real, advanced: TRUE, range: [0,1], default: 0.15]
heuristics/undercover/mincoveredrel = 0.15

# factor by which the heuristic should at least improve the incumbent
# [type: real, advanced: TRUE, range: [-1,1], default: 0]
heuristics/undercover/minimprove = 0

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/undercover/nodesquot = 0.1

# fraction of covering variables in the last cover which need to change their value when recovering
# [type: real, advanced: TRUE, range: [0,1], default: 0.9]
heuristics/undercover/recoverdiv = 0.9

# minimum number of nonlinear constraints in the original problem
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5]
heuristics/undercover/mincoveredabs = 5

# maximum number of backtracks in fix-and-propagate
# [type: int, advanced: TRUE, range: [0,2147483647], default: 6]
heuristics/undercover/maxbacktracks = 6

# maximum number of recoverings
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
heuristics/undercover/maxrecovers = 0

# maximum number of reorderings of the fixing order
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
heuristics/undercover/maxreorders = 1

# objective function of the covering problem (influenced nonlinear 'c'onstraints/'t'erms, 'd'omain size, 'l'ocks, 'm'in of up/down locks, 'u'nit penalties)
# [type: char, advanced: TRUE, range: {cdlmtu}, default: u]
heuristics/undercover/coveringobj = u

# order in which variables should be fixed (increasing 'C'onflict score, decreasing 'c'onflict score, increasing 'V'ariable index, decreasing 'v'ariable index
# [type: char, advanced: TRUE, range: {CcVv}, default: v]
heuristics/undercover/fixingorder = v

# should the heuristic be called at root node before cut separation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/beforecuts = TRUE

# should integer variables in the cover be fixed first?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/undercover/fixintfirst = FALSE

# shall LP values for integer vars be rounded according to locks?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/locksrounding = TRUE

# should we only fix variables in order to obtain a convex problem?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/undercover/onlyconvexify = FALSE

# should the NLP heuristic be called to polish a feasible solution?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/postnlp = TRUE

# should and constraints be covered (or just copied)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/coverand = TRUE

# should bounddisjunction constraints be covered (or just copied)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/undercover/coverbd = FALSE

# should indicator constraints be covered (or just copied)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/undercover/coverind = FALSE

# should nonlinear constraints be covered (or just copied)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/covernl = TRUE

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/undercover/copycuts = TRUE

# shall the cover be reused if a conflict was added after an infeasible subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/undercover/reusecover = FALSE

# priority of heuristic <vbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 2500]
heuristics/vbounds/priority = 2500

# frequency for calling primal heuristic <vbounds> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
heuristics/vbounds/freq = 0

# frequency offset for calling primal heuristic <vbounds>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/vbounds/freqofs = 0

# maximal depth level to call primal heuristic <vbounds> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/vbounds/maxdepth = -1

# minimum percentage of integer variables that have to be fixed
# [type: real, advanced: FALSE, range: [0,1], default: 0.65]
heuristics/vbounds/minintfixingrate = 0.65

# minimum percentage of variables that have to be fixed within sub-SCIP (integer and continuous)
# [type: real, advanced: FALSE, range: [0,1], default: 0.65]
heuristics/vbounds/minmipfixingrate = 0.65

# maximum number of nodes to regard in the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 5000]
heuristics/vbounds/maxnodes = 5000

# number of nodes added to the contingent of the total nodes
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 500]
heuristics/vbounds/nodesofs = 500

# minimum number of nodes required to start the subproblem
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 500]
heuristics/vbounds/minnodes = 500

# contingent of sub problem nodes in relation to the number of nodes of the original problem
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
heuristics/vbounds/nodesquot = 0.1

# factor by which vbounds heuristic should at least improve the incumbent
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
heuristics/vbounds/minimprove = 0.01

# maximum number of propagation rounds during probing (-1 infinity)
# [type: int, advanced: TRUE, range: [-1,536870911], default: 2]
heuristics/vbounds/maxproprounds = 2

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/vbounds/copycuts = TRUE

# should more variables be fixed based on variable locks if the fixing rate was not reached?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
heuristics/vbounds/uselockfixings = FALSE

# maximum number of backtracks during the fixing process
# [type: int, advanced: TRUE, range: [-1,536870911], default: 10]
heuristics/vbounds/maxbacktracks = 10

# which variants of the vbounds heuristic that try to stay feasible should be called? (0: off, 1: w/o looking at obj, 2: only fix to best bound, 4: only fix to worst bound
# [type: int, advanced: TRUE, range: [0,7], default: 6]
heuristics/vbounds/feasvariant = 6

# which tightening variants of the vbounds heuristic should be called? (0: off, 1: w/o looking at obj, 2: only fix to best bound, 4: only fix to worst bound
# [type: int, advanced: TRUE, range: [0,7], default: 7]
heuristics/vbounds/tightenvariant = 7

# priority of heuristic <veclendiving>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1003100]
heuristics/veclendiving/priority = -1003100

# frequency for calling primal heuristic <veclendiving> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
heuristics/veclendiving/freq = 10

# frequency offset for calling primal heuristic <veclendiving>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 4]
heuristics/veclendiving/freqofs = 4

# maximal depth level to call primal heuristic <veclendiving> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/veclendiving/maxdepth = -1

# minimal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 0]
heuristics/veclendiving/minreldepth = 0

# maximal relative depth to start diving
# [type: real, advanced: TRUE, range: [0,1], default: 1]
heuristics/veclendiving/maxreldepth = 1

# maximal fraction of diving LP iterations compared to node LP iterations
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.05]
heuristics/veclendiving/maxlpiterquot = 0.05

# additional number of allowed LP iterations
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1000]
heuristics/veclendiving/maxlpiterofs = 1000

# maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.8]
heuristics/veclendiving/maxdiveubquot = 0.8

# maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/veclendiving/maxdiveavgquot = 0

# maximal UBQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
heuristics/veclendiving/maxdiveubquotnosol = 0.1

# maximal AVGQUOT when no solution was found yet (0.0: no limit)
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
heuristics/veclendiving/maxdiveavgquotnosol = 0

# use one level of backtracking if infeasibility is encountered?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
heuristics/veclendiving/backtrack = TRUE

# percentage of immediate domain changes during probing to trigger LP resolve
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.15]
heuristics/veclendiving/lpresolvedomchgquot = 0.15

# LP solve frequency for diving heuristics (0: only after enough domain changes have been found)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 0]
heuristics/veclendiving/lpsolvefreq = 0

# should only LP branching candidates be considered instead of the slower but more general constraint handler diving variable selection?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
heuristics/veclendiving/onlylpbranchcands = FALSE

# priority of heuristic <zirounding>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -500]
heuristics/zirounding/priority = -500

# frequency for calling primal heuristic <zirounding> (-1: never, 0: only at depth freqofs)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
heuristics/zirounding/freq = 1

# frequency offset for calling primal heuristic <zirounding>
# [type: int, advanced: FALSE, range: [0,1073741822], default: 0]
heuristics/zirounding/freqofs = 0

# maximal depth level to call primal heuristic <zirounding> (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
heuristics/zirounding/maxdepth = -1

# determines maximum number of rounding loops
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 2]
heuristics/zirounding/maxroundingloops = 2

# flag to determine if Zirounding is deactivated after a certain percentage of unsuccessful calls
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
heuristics/zirounding/stopziround = TRUE

# if percentage of found solutions falls below this parameter, Zirounding will be deactivated
# [type: real, advanced: TRUE, range: [0,1], default: 0.02]
heuristics/zirounding/stoppercentage = 0.02

# determines the minimum number of calls before percentage-based deactivation of Zirounding is applied
# [type: int, advanced: TRUE, range: [1,2147483647], default: 1000]
heuristics/zirounding/minstopncalls = 1000

# priority of propagator <dualfix>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 8000000]
propagating/dualfix/priority = 8000000

# frequency for calling propagator <dualfix> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
propagating/dualfix/freq = 0

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/dualfix/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 1]
propagating/dualfix/timingmask = 1

# presolving priority of propagator <dualfix>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 8000000]
propagating/dualfix/presolpriority = 8000000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/dualfix/maxprerounds = -1

# timing mask of the presolving method of propagator <dualfix> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 4]
propagating/dualfix/presoltiming = 4

# priority of propagator <genvbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 3000000]
propagating/genvbounds/priority = 3000000

# frequency for calling propagator <genvbounds> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/genvbounds/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/genvbounds/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 15]
propagating/genvbounds/timingmask = 15

# presolving priority of propagator <genvbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -2000000]
propagating/genvbounds/presolpriority = -2000000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/genvbounds/maxprerounds = -1

# timing mask of the presolving method of propagator <genvbounds> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 4]
propagating/genvbounds/presoltiming = 4

# apply global propagation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/genvbounds/global = TRUE

# apply genvbounds in root node if no new incumbent was found?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/genvbounds/propinrootnode = TRUE

# sort genvbounds and wait for bound change events?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/genvbounds/sort = TRUE

# should genvbounds be transformed to (linear) constraints?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/genvbounds/propasconss = FALSE

# priority of propagator <obbt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000000]
propagating/obbt/priority = -1000000

# frequency for calling propagator <obbt> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
propagating/obbt/freq = 0

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/delay = TRUE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 4]
propagating/obbt/timingmask = 4

# presolving priority of propagator <obbt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
propagating/obbt/presolpriority = 0

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/obbt/maxprerounds = -1

# timing mask of the presolving method of propagator <obbt> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 28]
propagating/obbt/presoltiming = 28

# should obbt try to provide genvbounds if possible?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/creategenvbounds = TRUE

# should coefficients in filtering be normalized w.r.t. the domains sizes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/normalize = TRUE

# try to filter bounds in so-called filter rounds by solving auxiliary LPs?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/obbt/applyfilterrounds = FALSE

# try to filter bounds with the LP solution after each solve?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/applytrivialfilter = TRUE

# should we try to generate genvbounds during trivial and aggressive filtering?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/genvbdsduringfilter = TRUE

# try to create genvbounds during separation process?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/genvbdsduringsepa = TRUE

# minimal number of filtered bounds to apply another filter round
# [type: int, advanced: TRUE, range: [1,2147483647], default: 2]
propagating/obbt/minfilter = 2

# multiple of root node LP iterations used as total LP iteration limit for obbt (<= 0: no limit )
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 10]
propagating/obbt/itlimitfactor = 10

# multiple of OBBT LP limit used as total LP iteration limit for solving bilinear inequality LPs (< 0 for no limit)
# [type: real, advanced: FALSE, range: [-1.79769313486232e+308,1.79769313486232e+308], default: 3]
propagating/obbt/itlimitfactorbilin = 3

# minimum absolute value of nonconvex eigenvalues for a bilinear term
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.1]
propagating/obbt/minnonconvexity = 0.1

# minimum LP iteration limit
# [type: longint, advanced: FALSE, range: [0,9223372036854775807], default: 5000]
propagating/obbt/minitlimit = 5000

# feasibility tolerance for reduced costs used in obbt; this value is used if SCIP's dual feastol is greater
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1e-09]
propagating/obbt/dualfeastol = 1e-09

# maximum condition limit used in LP solver (-1.0: no limit)
# [type: real, advanced: FALSE, range: [-1,1.79769313486232e+308], default: -1]
propagating/obbt/conditionlimit = -1

# minimal relative improve for strengthening bounds
# [type: real, advanced: FALSE, range: [0,1], default: 0.001]
propagating/obbt/boundstreps = 0.001

# threshold whether upper bounds of vars of indicator conss are considered or tightened
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1000000]
propagating/obbt/indicatorthreshold = 1000000

# only apply obbt on non-convex variables
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/onlynonconvexvars = TRUE

# apply obbt on variables of indicator constraints? (independent of convexity)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/obbt/indicators = FALSE

# should integral bounds be tightened during the probing mode?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/tightintboundsprobing = TRUE

# should continuous bounds be tightened during the probing mode?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/obbt/tightcontboundsprobing = FALSE

# solve auxiliary LPs in order to find valid inequalities for bilinear terms?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/obbt/createbilinineqs = TRUE

# create linear constraints from inequalities for bilinear terms?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/obbt/createlincons = FALSE

# select the type of ordering algorithm which should be used (0: no special ordering, 1: greedy, 2: greedy reverse)
# [type: int, advanced: TRUE, range: [0,2], default: 1]
propagating/obbt/orderingalgo = 1

# should the obbt LP solution be separated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/obbt/separatesol = FALSE

# minimum number of iteration spend to separate an obbt LP solution
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
propagating/obbt/sepaminiter = 0

# maximum number of iteration spend to separate an obbt LP solution
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10]
propagating/obbt/sepamaxiter = 10

# trigger a propagation round after that many bound tightenings (0: no propagation)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
propagating/obbt/propagatefreq = 0

# priority of propagator <nlobbt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1100000]
propagating/nlobbt/priority = -1100000

# frequency for calling propagator <nlobbt> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
propagating/nlobbt/freq = -1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/nlobbt/delay = TRUE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 4]
propagating/nlobbt/timingmask = 4

# presolving priority of propagator <nlobbt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
propagating/nlobbt/presolpriority = 0

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/nlobbt/maxprerounds = -1

# timing mask of the presolving method of propagator <nlobbt> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 28]
propagating/nlobbt/presoltiming = 28

# factor for NLP feasibility tolerance
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
propagating/nlobbt/feastolfac = 0.01

# factor for NLP relative objective tolerance
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
propagating/nlobbt/relobjtolfac = 0.01

# (#convex nlrows)/(#nonconvex nlrows) threshold to apply propagator
# [type: real, advanced: TRUE, range: [0,1e+20], default: 0.2]
propagating/nlobbt/minnonconvexfrac = 0.2

# minimum (#convex nlrows)/(#linear nlrows) threshold to apply propagator
# [type: real, advanced: TRUE, range: [0,1e+20], default: 0.02]
propagating/nlobbt/minlinearfrac = 0.02

# should non-initial LP rows be used?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
propagating/nlobbt/addlprows = TRUE

# iteration limit of NLP solver; 0 for no limit
# [type: int, advanced: TRUE, range: [0,2147483647], default: 500]
propagating/nlobbt/nlpiterlimit = 500

# time limit of NLP solver; 0.0 for no limit
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
propagating/nlobbt/nlptimelimit = 0

# verbosity level of NLP solver
# [type: int, advanced: TRUE, range: [0,5], default: 0]
propagating/nlobbt/nlpverblevel = 0

# LP iteration limit for nlobbt will be this factor times total LP iterations in root node
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
propagating/nlobbt/itlimitfactor = 2

# priority of propagator <probing>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
propagating/probing/priority = -100000

# frequency for calling propagator <probing> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
propagating/probing/freq = -1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/probing/delay = TRUE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 4]
propagating/probing/timingmask = 4

# presolving priority of propagator <probing>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
propagating/probing/presolpriority = -100000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/probing/maxprerounds = -1

# timing mask of the presolving method of propagator <probing> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 16]
propagating/probing/presoltiming = 16

# maximal number of runs, probing participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
propagating/probing/maxruns = 1

# maximal number of propagation rounds in probing subproblems (-1: no limit, 0: auto)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
propagating/probing/proprounds = -1

# maximal number of fixings found, until probing is interrupted (0: don't iterrupt)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 25]
propagating/probing/maxfixings = 25

# maximal number of successive probings without fixings, until probing is aborted (0: don't abort)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1000]
propagating/probing/maxuseless = 1000

# maximal number of successive probings without fixings, bound changes, and implications, until probing is aborted (0: don't abort)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
propagating/probing/maxtotaluseless = 50

# maximal number of probings without fixings, until probing is aborted (0: don't abort)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
propagating/probing/maxsumuseless = 0

# maximal depth until propagation is executed(-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
propagating/probing/maxdepth = -1

# priority of propagator <pseudoobj>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 3000000]
propagating/pseudoobj/priority = 3000000

# frequency for calling propagator <pseudoobj> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/pseudoobj/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/pseudoobj/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 7]
propagating/pseudoobj/timingmask = 7

# presolving priority of propagator <pseudoobj>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 6000000]
propagating/pseudoobj/presolpriority = 6000000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/pseudoobj/maxprerounds = -1

# timing mask of the presolving method of propagator <pseudoobj> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 4]
propagating/pseudoobj/presoltiming = 4

# minimal number of successive non-binary variable propagations without a bound reduction before aborted
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
propagating/pseudoobj/minuseless = 100

# maximal fraction of non-binary variables with non-zero objective without a bound reduction before aborted
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
propagating/pseudoobj/maxvarsfrac = 0.1

# whether to propagate all non-binary variables when we are propagating the root node
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/pseudoobj/propfullinroot = TRUE

# propagate new cutoff bound directly globally
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/pseudoobj/propcutoffbound = TRUE

# should the propagator be forced even if active pricer are present?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/pseudoobj/force = FALSE

# number of variables added after the propagator is reinitialized?
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1000]
propagating/pseudoobj/maxnewvars = 1000

# use implications to strengthen the propagation of binary variable (increasing the objective change)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/pseudoobj/propuseimplics = TRUE

# use implications to strengthen the resolve propagation of binary variable (increasing the objective change)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/pseudoobj/respropuseimplics = TRUE

# maximum number of binary variables the implications are used if turned on (-1: unlimited)?
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 50000]
propagating/pseudoobj/maximplvars = 50000

# priority of propagator <redcost>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 1000000]
propagating/redcost/priority = 1000000

# frequency for calling propagator <redcost> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/redcost/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/redcost/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 6]
propagating/redcost/timingmask = 6

# presolving priority of propagator <redcost>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
propagating/redcost/presolpriority = 0

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/redcost/maxprerounds = -1

# timing mask of the presolving method of propagator <redcost> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 28]
propagating/redcost/presoltiming = 28

# should reduced cost fixing be also applied to continuous variables?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
propagating/redcost/continuous = FALSE

# should implications be used to strength the reduced cost for binary variables?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
propagating/redcost/useimplics = FALSE

# should the propagator be forced even if active pricer are present?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/redcost/force = FALSE

# priority of propagator <rootredcost>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10000000]
propagating/rootredcost/priority = 10000000

# frequency for calling propagator <rootredcost> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/rootredcost/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/rootredcost/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 5]
propagating/rootredcost/timingmask = 5

# presolving priority of propagator <rootredcost>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
propagating/rootredcost/presolpriority = 0

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/rootredcost/maxprerounds = -1

# timing mask of the presolving method of propagator <rootredcost> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 28]
propagating/rootredcost/presoltiming = 28

# should only binary variables be propagated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/rootredcost/onlybinary = FALSE

# should the propagator be forced even if active pricer are present?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/rootredcost/force = FALSE

# priority of propagator <symmetry>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000000]
propagating/symmetry/priority = -1000000

# frequency for calling propagator <symmetry> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/symmetry/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 1]
propagating/symmetry/timingmask = 1

# presolving priority of propagator <symmetry>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -10000000]
propagating/symmetry/presolpriority = -10000000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/symmetry/maxprerounds = -1

# timing mask of the presolving method of propagator <symmetry> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 16]
propagating/symmetry/presoltiming = 16

# is statistics table <symmetry> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/symmetry/active = TRUE

# limit on the number of generators that should be produced within symmetry detection (0 = no limit)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1500]
propagating/symmetry/maxgenerators = 1500

# Should all symmetries be checked after computation?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/checksymmetries = FALSE

# Should the number of variables affected by some symmetry be displayed?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/displaynorbitvars = FALSE

# Double equations to positive/negative version?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/doubleequations = FALSE

# Should the symmetry breaking constraints be added to the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/conssaddlp = TRUE

# Add inequalities for symresacks for each generator?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/addsymresacks = TRUE

# Should we check whether the components of the symmetry group can be handled by double lex matrices?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/detectdoublelex = TRUE

# Should we check whether the components of the symmetry group can be handled by orbitopes?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/detectorbitopes = TRUE

# Should we try to detect symmetric subgroups of the symmetry group on binary variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/detectsubgroups = TRUE

# Should we add weak SBCs for enclosing orbit of symmetric subgroups?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/addweaksbcs = TRUE

# timing of adding constraints (0 = before presolving, 1 = during presolving, 2 = after presolving) [disabled parameter]
# [type: int, advanced: TRUE, range: [0,2], default: 2]
propagating/symmetry/addconsstiming = 2

# timing of symmetry computation (0 = before presolving, 1 = during presolving, 2 = at first call) [disabled parameter]
# [type: int, advanced: TRUE, range: [0,2], default: 2]
propagating/symmetry/ofsymcomptiming = 2

# run orbital fixing during presolving? (disabled)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/performpresolving = FALSE

# recompute symmetries after a restart has occurred? (0 = never)
# [type: int, advanced: TRUE, range: [0,0], default: 0]
propagating/symmetry/recomputerestart = 0

# Should non-affected variables be removed from permutation to save memory?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/compresssymmetries = TRUE

# Compression is used if percentage of moved vars is at most the threshold.
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
propagating/symmetry/compressthreshold = 0.5

# Should the number of conss a variable is contained in be exploited in symmetry detection?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/usecolumnsparsity = FALSE

# maximum number of constraints up to which subgroup structures are detected
# [type: int, advanced: TRUE, range: [0,2147483647], default: 500000]
propagating/symmetry/maxnconsssubgroup = 500000

# whether dynamified symmetry handling constraint methods should be used
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/usedynamicprop = TRUE

# Should strong SBCs for enclosing orbit of symmetric subgroups be added if orbitopes are not used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/addstrongsbcs = FALSE

# rule to select the orbit in Schreier Sims inequalities (variable in 0: minimum size orbit; 1: maximum size orbit; 2: orbit with most variables in conflict with leader)
# [type: int, advanced: TRUE, range: [0,2], default: 1]
propagating/symmetry/ssttiebreakrule = 1

# rule to select the leader in an orbit (0: first var; 1: last var; 2: var having most conflicting vars in orbit)
# [type: int, advanced: TRUE, range: [0,2], default: 0]
propagating/symmetry/sstleaderrule = 0

# bitset encoding which variable types can be leaders (1: binary; 2: integer; 4: impl. int; 8: continuous);if multiple types are allowed, take the one with most affected vars
# [type: int, advanced: TRUE, range: [1,15], default: 14]
propagating/symmetry/sstleadervartype = 14

# Should Schreier Sims constraints be added if we use a conflict based rule?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/addconflictcuts = TRUE

# Should Schreier Sims constraints be added?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/sstaddcuts = TRUE

# Should Schreier Sims constraints be added if a symmetry component contains variables of different types?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/sstmixedcomponents = TRUE

# Whether all non-binary variables shall be not affected by symmetries if OF is active? (disabled)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/symfixnonbinaryvars = FALSE

# Is only symmetry on binary variables used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/symmetry/enforcecomputesymmetry = FALSE

# Shall orbitopes with less rows be preferred in detection?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/symmetry/preferlessrows = TRUE

# Type of symmetries that shall be computed?
# [type: int, advanced: TRUE, range: [0,1], default: 0]
propagating/symmetry/symtype = 0

# timing of symmetry computation and handling (0 = before presolving, 1 = during presolving, 2 = after presolving)
# [type: int, advanced: TRUE, range: [0,2], default: 2]
propagating/symmetry/symtiming = 2

# The column ordering variant, respects enum SCIP_ColumnOrdering.
# [type: int, advanced: TRUE, range: [0,4], default: 4]
propagating/symmetry/orbitopalreduction/columnordering = 4

# priority of propagator <vbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 3000000]
propagating/vbounds/priority = 3000000

# frequency for calling propagator <vbounds> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 1]
propagating/vbounds/freq = 1

# should propagator be delayed, if other propagators found reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/vbounds/delay = FALSE

# timing when propagator should be called (1:BEFORELP, 2:DURINGLPLOOP, 4:AFTERLPLOOP, 15:ALWAYS))
# [type: int, advanced: TRUE, range: [1,15], default: 5]
propagating/vbounds/timingmask = 5

# presolving priority of propagator <vbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -90000]
propagating/vbounds/presolpriority = -90000

# maximal number of presolving rounds the propagator participates in (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
propagating/vbounds/maxprerounds = -1

# timing mask of the presolving method of propagator <vbounds> (4:FAST, 8:MEDIUM, 16:EXHAUSTIVE, 32:FINAL)
# [type: int, advanced: TRUE, range: [2,60], default: 24]
propagating/vbounds/presoltiming = 24

# should bound widening be used to initialize conflict analysis?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
propagating/vbounds/usebdwidening = TRUE

# should implications be propagated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/vbounds/useimplics = FALSE

# should cliques be propagated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/vbounds/usecliques = FALSE

# should vbounds be propagated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
propagating/vbounds/usevbounds = TRUE

# should the bounds be topologically sorted in advance?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
propagating/vbounds/dotoposort = TRUE

# should cliques be regarded for the topological sort?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
propagating/vbounds/sortcliques = FALSE

# should cycles in the variable bound graph be identified?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
propagating/vbounds/detectcycles = FALSE

# minimum percentage of new cliques to trigger another clique table analysis
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
propagating/vbounds/minnewcliques = 0.1

# maximum number of cliques per variable to run clique table analysis in medium presolving
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 50]
propagating/vbounds/maxcliquesmedium = 50

# maximum number of cliques per variable to run clique table analysis in exhaustive presolving
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 100]
propagating/vbounds/maxcliquesexhaustive = 100

# priority of separator <cgmip>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000]
separating/cgmip/priority = -1000

# frequency for calling separator <cgmip> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/cgmip/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <cgmip> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/cgmip/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/delay = FALSE

# base for exponential increase of frequency at which separator <cgmip> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/cgmip/expbackoff = 4

# maximal number of cgmip separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
separating/cgmip/maxrounds = 5

# maximal number of cgmip separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 50]
separating/cgmip/maxroundsroot = 50

# maximal depth at which the separator is applied (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/cgmip/maxdepth = -1

# Use decision tree to turn separation on/off?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/decisiontree = FALSE

# time limit for sub-MIP
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1e+20]
separating/cgmip/timelimit = 1e+20

# memory limit for sub-MIP
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1e+20]
separating/cgmip/memorylimit = 1e+20

# minimum number of nodes considered for sub-MIP (-1: unlimited)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: 500]
separating/cgmip/minnodelimit = 500

# maximum number of nodes considered for sub-MIP (-1: unlimited)
# [type: longint, advanced: FALSE, range: [-1,9223372036854775807], default: 5000]
separating/cgmip/maxnodelimit = 5000

# bounds on the values of the coefficients in the CG-cut
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1000]
separating/cgmip/cutcoefbnd = 1000

# Use only active rows to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/onlyactiverows = FALSE

# maximal age of rows to consider if onlyactiverows is false
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/cgmip/maxrowage = -1

# Separate only rank 1 inequalities w.r.t. CG-MIP separator?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/onlyrankone = FALSE

# Generate cuts for problems with only integer variables?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/onlyintvars = FALSE

# Convert some integral variables to be continuous to reduce the size of the sub-MIP?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/contconvert = FALSE

# fraction of integral variables converted to be continuous (if contconvert)
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
separating/cgmip/contconvfrac = 0.1

# minimum number of integral variables before some are converted to be continuous
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
separating/cgmip/contconvmin = 100

# Convert some integral variables attaining fractional values to have integral value?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/intconvert = FALSE

# fraction of frac. integral variables converted to have integral value (if intconvert)
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
separating/cgmip/intconvfrac = 0.1

# minimum number of integral variables before some are converted to have integral value
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
separating/cgmip/intconvmin = 100

# Skip the upper bounds on the multipliers in the sub-MIP?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/skipmultbounds = TRUE

# Should the objective of the sub-MIP minimize the l1-norm of the multipliers?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/objlone = FALSE

# weight used for the row combination coefficient in the sub-MIP objective
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.001]
separating/cgmip/objweight = 0.001

# Weight each row by its size?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/objweightsize = TRUE

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/dynamiccuts = TRUE

# use CMIR-generator (otherwise add cut directly)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/usecmir = TRUE

# use strong CG-function to strengthen cut?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/usestrongcg = FALSE

# tell CMIR-generator which bounds to used in rounding?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/cmirownbounds = FALSE

# use cutpool to store CG-cuts even if the are not efficient?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/usecutpool = TRUE

# only separate cuts that are tight for the best feasible solution?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/primalseparation = TRUE

# terminate separation if a violated (but possibly sub-optimal) cut has been found?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/earlyterm = TRUE

# add constraint to subscip that only allows violated cuts (otherwise add obj. limit)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/addviolationcons = FALSE

# add constraint handler to filter out violated cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/addviolconshdlr = FALSE

# should the violation constraint handler use the norm of a cut to check for feasibility?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/conshdlrusenorm = TRUE

# Use upper bound on objective function (via primal solution)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/useobjub = FALSE

# Use lower bound on objective function (via primal solution)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/useobjlb = FALSE

# Should the settings for the sub-MIP be optimized for speed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/cgmip/subscipfast = TRUE

# Should information about the sub-MIP and cuts be displayed?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/output = FALSE

# Try to generate primal solutions from Gomory cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/cgmip/genprimalsols = FALSE

# priority of separator <clique>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -5000]
separating/clique/priority = -5000

# frequency for calling separator <clique> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
separating/clique/freq = 0

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <clique> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/clique/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/clique/delay = FALSE

# base for exponential increase of frequency at which separator <clique> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/clique/expbackoff = 4

# factor for scaling weights
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1000]
separating/clique/scaleval = 1000

# maximal number of nodes in branch and bound tree (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10000]
separating/clique/maxtreenodes = 10000

# frequency for premature backtracking up to tree level 1 (0: no backtracking)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1000]
separating/clique/backtrackfreq = 1000

# maximal number of clique cuts separated per separation round (-1: no limit)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/clique/maxsepacuts = 10

# maximal number of zero-valued variables extending the clique (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 1000]
separating/clique/maxzeroextensions = 1000

# maximal memory size of dense clique table (in kb)
# [type: real, advanced: TRUE, range: [0,2097151.99902344], default: 20000]
separating/clique/cliquetablemem = 20000

# minimal density of cliques to use a dense clique table
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/clique/cliquedensity = 0

# priority of separator <closecuts>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 1000000]
separating/closecuts/priority = 1000000

# frequency for calling separator <closecuts> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/closecuts/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <closecuts> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/closecuts/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/closecuts/delay = FALSE

# base for exponential increase of frequency at which separator <closecuts> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/closecuts/expbackoff = 4

# generate close cuts w.r.t. relative interior point (best solution otherwise)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/closecuts/separelint = TRUE

# convex combination value for close cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
separating/closecuts/sepacombvalue = 0.3

# threshold on number of generated cuts below which the ordinary separation is started
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 50]
separating/closecuts/closethres = 50

# include an objective cutoff when computing the relative interior?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/closecuts/inclobjcutoff = FALSE

# recompute relative interior point in each separation call?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/closecuts/recomputerelint = FALSE

# turn off separation in current node after unsuccessful calls (-1 never turn off)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
separating/closecuts/maxunsuccessful = 0

# factor for maximal LP iterations in relative interior computation compared to node LP iterations (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: 10]
separating/closecuts/maxlpiterfactor = 10

# priority of separator <flowcover>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
separating/flowcover/priority = -100000

# frequency for calling separator <flowcover> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/flowcover/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <flowcover> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/flowcover/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/flowcover/delay = FALSE

# base for exponential increase of frequency at which separator <flowcover> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/flowcover/expbackoff = 4

# priority of separator <cmir>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
separating/cmir/priority = -100000

# frequency for calling separator <cmir> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/cmir/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <cmir> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/cmir/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/cmir/delay = FALSE

# base for exponential increase of frequency at which separator <cmir> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/cmir/expbackoff = 4

# priority of separator <knapsackcover>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
separating/knapsackcover/priority = -100000

# frequency for calling separator <knapsackcover> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/knapsackcover/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <knapsackcover> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/knapsackcover/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/knapsackcover/delay = FALSE

# base for exponential increase of frequency at which separator <knapsackcover> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/knapsackcover/expbackoff = 4

# priority of separator <aggregation>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -3000]
separating/aggregation/priority = -3000

# frequency for calling separator <aggregation> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/aggregation/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <aggregation> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/aggregation/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/aggregation/delay = FALSE

# base for exponential increase of frequency at which separator <aggregation> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/aggregation/expbackoff = 4

# maximal number of cmir separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/aggregation/maxrounds = -1

# maximal number of cmir separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/aggregation/maxroundsroot = -1

# maximal number of rows to start aggregation with per separation round (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 200]
separating/aggregation/maxtries = 200

# maximal number of rows to start aggregation with per separation round in the root node (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/aggregation/maxtriesroot = -1

# maximal number of consecutive unsuccessful aggregation tries (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 20]
separating/aggregation/maxfails = 20

# maximal number of consecutive unsuccessful aggregation tries in the root node (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 100]
separating/aggregation/maxfailsroot = 100

# maximal number of aggregations for each row per separation round
# [type: int, advanced: TRUE, range: [0,2147483647], default: 3]
separating/aggregation/maxaggrs = 3

# maximal number of aggregations for each row per separation round in the root node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 6]
separating/aggregation/maxaggrsroot = 6

# maximal number of cmir cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
separating/aggregation/maxsepacuts = 100

# maximal number of cmir cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 500]
separating/aggregation/maxsepacutsroot = 500

# maximal slack of rows to be used in aggregation
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
separating/aggregation/maxslack = 0

# maximal slack of rows to be used in aggregation in the root node
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.1]
separating/aggregation/maxslackroot = 0.1

# weight of row density in the aggregation scoring of the rows
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.0001]
separating/aggregation/densityscore = 0.0001

# weight of slack in the aggregation scoring of the rows
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.001]
separating/aggregation/slackscore = 0.001

# maximal density of aggregated row
# [type: real, advanced: TRUE, range: [0,1], default: 0.2]
separating/aggregation/maxaggdensity = 0.2

# maximal density of row to be used in aggregation
# [type: real, advanced: TRUE, range: [0,1], default: 0.05]
separating/aggregation/maxrowdensity = 0.05

# additional number of variables allowed in row on top of density
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
separating/aggregation/densityoffset = 100

# maximal row aggregation factor
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 10000]
separating/aggregation/maxrowfac = 10000

# maximal number of different deltas to try (-1: unlimited)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/aggregation/maxtestdelta = -1

# tolerance for bound distances used to select continuous variable in current aggregated constraint to be eliminated
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.01]
separating/aggregation/aggrtol = 0.01

# should negative values also be tested in scaling?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/aggregation/trynegscaling = TRUE

# should an additional variable be complemented if f0 = 0?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/aggregation/fixintegralrhs = TRUE

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/aggregation/dynamiccuts = TRUE

# priority of separator <convexproj>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
separating/convexproj/priority = 0

# frequency for calling separator <convexproj> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/convexproj/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <convexproj> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/convexproj/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/convexproj/delay = TRUE

# base for exponential increase of frequency at which separator <convexproj> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/convexproj/expbackoff = 4

# maximal depth at which the separator is applied (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/convexproj/maxdepth = -1

# iteration limit of NLP solver; 0 for no limit
# [type: int, advanced: TRUE, range: [0,2147483647], default: 250]
separating/convexproj/nlpiterlimit = 250

# priority of separator <disjunctive>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10]
separating/disjunctive/priority = 10

# frequency for calling separator <disjunctive> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
separating/disjunctive/freq = 0

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <disjunctive> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/disjunctive/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/disjunctive/delay = TRUE

# base for exponential increase of frequency at which separator <disjunctive> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/disjunctive/expbackoff = 4

# strengthen cut if integer variables are present.
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/disjunctive/strengthen = TRUE

# node depth of separating bipartite disjunctive cuts (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/disjunctive/maxdepth = -1

# maximal number of separation rounds per iteration in a branching node (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 25]
separating/disjunctive/maxrounds = 25

# maximal number of separation rounds in the root node (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 100]
separating/disjunctive/maxroundsroot = 100

# maximal number of cuts investigated per iteration in a branching node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
separating/disjunctive/maxinvcuts = 50

# maximal number of cuts investigated per iteration in the root node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 250]
separating/disjunctive/maxinvcutsroot = 250

# delay separation if number of conflict graph edges is larger than predefined value (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 100000]
separating/disjunctive/maxconfsdelay = 100000

# maximal rank of a disj. cut that could not be scaled to integral coefficients (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 20]
separating/disjunctive/maxrank = 20

# maximal rank of a disj. cut that could be scaled to integral coefficients (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/disjunctive/maxrankintegral = -1

# maximal valid range max(|weights|)/min(|weights|) of row weights
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1000]
separating/disjunctive/maxweightrange = 1000

# priority of separator <eccuts>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -13000]
separating/eccuts/priority = -13000

# frequency for calling separator <eccuts> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/eccuts/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <eccuts> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/eccuts/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/eccuts/delay = FALSE

# base for exponential increase of frequency at which separator <eccuts> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/eccuts/expbackoff = 4

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/eccuts/dynamiccuts = TRUE

# maximal number of eccuts separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/eccuts/maxrounds = 10

# maximal number of eccuts separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 250]
separating/eccuts/maxroundsroot = 250

# maximal depth at which the separator is applied (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/eccuts/maxdepth = -1

# maximal number of edge-concave cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
separating/eccuts/maxsepacuts = 10

# maximal number of edge-concave cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 50]
separating/eccuts/maxsepacutsroot = 50

# maximal coef. range of a cut (max coef. divided by min coef.) in order to be added to LP relaxation
# [type: real, advanced: FALSE, range: [0,1e+20], default: 10000000]
separating/eccuts/cutmaxrange = 10000000

# minimal violation of an edge-concave cut to be separated
# [type: real, advanced: FALSE, range: [0,0.5], default: 0.3]
separating/eccuts/minviolation = 0.3

# search for edge-concave aggregations of at least this size
# [type: int, advanced: TRUE, range: [3,5], default: 3]
separating/eccuts/minaggrsize = 3

# search for edge-concave aggregations of at most this size
# [type: int, advanced: TRUE, range: [3,5], default: 4]
separating/eccuts/maxaggrsize = 4

# maximum number of bilinear terms allowed to be in a quadratic constraint
# [type: int, advanced: TRUE, range: [0,2147483647], default: 500]
separating/eccuts/maxbilinterms = 500

# maximum number of unsuccessful rounds in the edge-concave aggregation search
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5]
separating/eccuts/maxstallrounds = 5

# priority of separator <gauge>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
separating/gauge/priority = 0

# frequency for calling separator <gauge> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/gauge/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <gauge> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/gauge/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/gauge/delay = FALSE

# base for exponential increase of frequency at which separator <gauge> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/gauge/expbackoff = 4

# iteration limit of NLP solver; 0 for no limit
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1000]
separating/gauge/nlpiterlimit = 1000

# priority of separator <gomory>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1000]
separating/gomory/priority = -1000

# frequency for calling separator <gomory> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/gomory/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <gomory> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/gomory/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/gomory/delay = FALSE

# base for exponential increase of frequency at which separator <gomory> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/gomory/expbackoff = 4

# priority of separator <strongcg>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
separating/strongcg/priority = -100000

# frequency for calling separator <strongcg> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/strongcg/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <strongcg> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/strongcg/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/strongcg/delay = FALSE

# base for exponential increase of frequency at which separator <strongcg> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/strongcg/expbackoff = 4

# priority of separator <gomorymi>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100000]
separating/gomorymi/priority = -100000

# frequency for calling separator <gomorymi> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/gomorymi/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <gomorymi> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/gomorymi/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/gomorymi/delay = FALSE

# base for exponential increase of frequency at which separator <gomorymi> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/gomorymi/expbackoff = 4

# maximal number of gomory separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
separating/gomory/maxrounds = 5

# maximal number of gomory separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/gomory/maxroundsroot = 10

# maximal number of gomory cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 50]
separating/gomory/maxsepacuts = 50

# maximal number of gomory cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 200]
separating/gomory/maxsepacutsroot = 200

# maximal rank of a gomory cut that could not be scaled to integral coefficients (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/gomory/maxrank = -1

# maximal rank of a gomory cut that could be scaled to integral coefficients (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/gomory/maxrankintegral = -1

# minimal integrality violation of a basis variable in order to try Gomory cut
# [type: real, advanced: FALSE, range: [0.0001,0.5], default: 0.01]
separating/gomory/away = 0.01

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/dynamiccuts = TRUE

# try to scale cuts to integral coefficients
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/gomory/makeintegral = FALSE

# if conversion to integral coefficients failed still consider the cut
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/forcecuts = TRUE

# separate rows with integral slack
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/separaterows = TRUE

# should cuts be added to the delayed cut pool?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/gomory/delayedcuts = FALSE

# choose side types of row (lhs/rhs) based on basis information?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/sidetypebasis = TRUE

# try to generate strengthened Chvatal-Gomory cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/trystrongcg = TRUE

# Should both Gomory and strong CG cuts be generated (otherwise take best)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/gomory/genbothgomscg = TRUE

# priority of separator <impliedbounds>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -50]
separating/impliedbounds/priority = -50

# frequency for calling separator <impliedbounds> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/impliedbounds/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <impliedbounds> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/impliedbounds/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/impliedbounds/delay = FALSE

# base for exponential increase of frequency at which separator <impliedbounds> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/impliedbounds/expbackoff = 4

# should violated inequalities for cliques with 2 variables be separated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/impliedbounds/usetwosizecliques = TRUE

# priority of separator <interminor>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
separating/interminor/priority = 0

# frequency for calling separator <interminor> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/interminor/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <interminor> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/interminor/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/interminor/delay = FALSE

# base for exponential increase of frequency at which separator <interminor> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/interminor/expbackoff = 4

# whether to use strengthened intersection cuts to separate minors
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/interminor/usestrengthening = FALSE

# whether to also enforce nonegativity bounds of principle minors
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/interminor/usebounds = FALSE

# minimum required violation of a cut
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.0001]
separating/interminor/mincutviol = 0.0001

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/interminor/maxrounds = 10

# maximal number of separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/interminor/maxroundsroot = -1

# priority of separator <intobj>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -100]
separating/intobj/priority = -100

# frequency for calling separator <intobj> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/intobj/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <intobj> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/intobj/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/intobj/delay = FALSE

# base for exponential increase of frequency at which separator <intobj> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/intobj/expbackoff = 4

# priority of separator <lagromory>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -8000]
separating/lagromory/priority = -8000

# frequency for calling separator <lagromory> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/lagromory/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <lagromory> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/lagromory/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/lagromory/delay = FALSE

# base for exponential increase of frequency at which separator <lagromory> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/lagromory/expbackoff = 4

# minimal integrality violation of a basis variable to try separation
# [type: real, advanced: FALSE, range: [0,1], default: 0.01]
separating/lagromory/away = 0.01

# factor w.r.t. root node LP iterations for maximal separating LP iterations in the root node (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
separating/lagromory/rootlpiterlimitfactor = -1

# factor w.r.t. root node LP iterations for maximal separating LP iterations in the tree (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
separating/lagromory/totallpiterlimitfactor = -1

# factor w.r.t. root node LP iterations for maximal separating LP iterations per separation round (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: -1]
separating/lagromory/perroundlpiterlimitfactor = -1

# factor w.r.t. number of integer columns for number of cuts separated per separation round in root node
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1]
separating/lagromory/perroundcutsfactorroot = 1

# factor w.r.t. number of integer columns for number of cuts separated per separation round at a non-root node
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.5]
separating/lagromory/perroundcutsfactor = 0.5

# factor w.r.t. number of integer columns for total number of cuts separated
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 50]
separating/lagromory/totalcutsfactor = 50

# initial value of the mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [0,100], default: 0.01]
separating/lagromory/muparaminit = 0.01

# lower bound of the mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/lagromory/muparamlb = 0

# upper bound of the mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [1,10], default: 2]
separating/lagromory/muparamub = 2

# factor of mu while backtracking the mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
separating/lagromory/mubacktrackfactor = 0.5

# factor of mu parameter (factor for step length) for larger increment
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 10]
separating/lagromory/muslab1factor = 10

# factor of mu parameter (factor for step length) for smaller increment
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 2]
separating/lagromory/muslab2factor = 2

# factor of mu parameter (factor for step length) for reduction
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.5]
separating/lagromory/muslab3factor = 0.5

# factor of delta deciding larger increment of mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [0,1], default: 0.001]
separating/lagromory/deltaslab1ub = 0.001

# factor of delta deciding smaller increment of mu parameter (factor for step length)
# [type: real, advanced: TRUE, range: [0,1], default: 0.01]
separating/lagromory/deltaslab2ub = 0.01

# factor for positive upper bound used as an estimate for the optimal Lagrangian dual value
# [type: real, advanced: TRUE, range: [1,100], default: 2]
separating/lagromory/ubparamposfactor = 2

# factor for negative upper bound used as an estimate for the optimal Lagrangian dual value
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
separating/lagromory/ubparamnegfactor = 0.5

# factor w.r.t. root node LP iterations for iteration limit of each separating LP (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: 0.2]
separating/lagromory/perrootlpiterfactor = 0.2

# factor w.r.t. non-root node LP iterations for iteration limit of each separating LP (negative for no limit)
# [type: real, advanced: TRUE, range: [-1,1.79769313486232e+308], default: 0.1]
separating/lagromory/perlpiterfactor = 0.1

# fraction of generated cuts per explored basis to accept from separator
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/lagromory/cutsfilterfactor = 1

# initial radius of the ball used in stabilization of Lagrangian multipliers
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
separating/lagromory/radiusinit = 0.5

# maximum radius of the ball used in stabilization of Lagrangian multipliers
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 20]
separating/lagromory/radiusmax = 20

# minimum radius of the ball used in stabilization of Lagrangian multipliers
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 1e-06]
separating/lagromory/radiusmin = 1e-06

# a constant for stablity center based stabilization of Lagrangian multipliers
# [type: real, advanced: TRUE, range: [2,1.79769313486232e+308], default: 2]
separating/lagromory/constant = 2

# multiplier to evaluate cut violation score used for updating ball radius
# [type: real, advanced: TRUE, range: [0,1], default: 0.98]
separating/lagromory/radiusupdateweight = 0.98

# minimum dual degeneracy rate for separator execution
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
separating/lagromory/dualdegeneracyratethreshold = 0.5

# minimum variable-constraint ratio on optimal face for separator execution
# [type: real, advanced: FALSE, range: [1,1.79769313486232e+308], default: 1]
separating/lagromory/varconsratiothreshold = 1

# is the mu parameter (factor for step length) constant?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/muparamconst = TRUE

# separate rows with integral slack?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/separaterows = TRUE

# sort fractional integer columnsbased on fractionality?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/sortcutoffsol = TRUE

# choose side types of row (lhs/rhs) based on basis information?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/sidetypebasis = TRUE

# should generated cuts be removed from LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/dynamiccuts = TRUE

# try to scale all cuts to integral coefficients?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/lagromory/makeintegral = FALSE

# force cuts to be added to the LP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/lagromory/forcecuts = FALSE

# should cuts be added to the delayed cut pool
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/lagromory/delayedcuts = FALSE

# should locally valid cuts be generated?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/lagromory/allowlocal = FALSE

# aggregate all generated cuts using the Lagrangian multipliers?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/lagromory/aggregatecuts = TRUE

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
separating/lagromory/maxrounds = 1

# maximal number of separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
separating/lagromory/maxroundsroot = 1

# maximal number of separating LP iterations per separation round (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 50000]
separating/lagromory/perroundnmaxlpiters = 50000

# maximal number of cuts separated per Lagromory LP in the non-root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
separating/lagromory/nmaxcutsperlp = 10

# maximal number of cuts separated per Lagromory LP in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 50]
separating/lagromory/nmaxcutsperlproot = 50

# maximal number of main loop iterations of the relax-and-cut algorithm
# [type: int, advanced: TRUE, range: [0,2147483647], default: 4]
separating/lagromory/nmaxmainiters = 4

# maximal number of subgradient loop iterations of the relax-and-cut algorithm
# [type: int, advanced: TRUE, range: [0,2147483647], default: 6]
separating/lagromory/nmaxsubgradientiters = 6

# frequency of subgradient iterations for generating cuts
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
separating/lagromory/cutgenfreq = 1

# frequency of subgradient iterations for adding cuts to objective function
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
separating/lagromory/cutaddfreq = 1

# maximal number of iterations for rolling average of Lagrangian value
# [type: int, advanced: TRUE, range: [0,2147483647], default: 2]
separating/lagromory/nmaxlagrangianvalsforavg = 2

# consecutive number of iterations used to determine if mu needs to be backtracked
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10]
separating/lagromory/nmaxconsecitersformuupdate = 10

# the ball into which the Lagrangian multipliers are projected for stabilization (0: no projection, 1: L1-norm ball projection, 2: L2-norm ball projection, 3: L_inf-norm ball projection)
# [type: int, advanced: TRUE, range: [0,3], default: 2]
separating/lagromory/projectiontype = 2

# type of stability center for taking weighted average of Lagrangian multipliers for stabilization (0: no weighted stabilization, 1: best Lagrangian multipliers)
# [type: int, advanced: TRUE, range: [0,1], default: 1]
separating/lagromory/stabilitycentertype = 1

# priority of the optimal face for separator execution (0: low priority, 1: medium priority, 2: high priority)
# [type: int, advanced: TRUE, range: [0,2], default: 2]
separating/lagromory/optimalfacepriority = 2

# minimum restart round for separator execution (0: from beginning of the instance solving, >= n with n >= 1: from restart round n)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
separating/lagromory/minrestart = 1

# priority of separator <mcf>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -10000]
separating/mcf/priority = -10000

# frequency for calling separator <mcf> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
separating/mcf/freq = 0

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <mcf> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/mcf/maxbounddist = 0

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/mcf/delay = FALSE

# base for exponential increase of frequency at which separator <mcf> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/mcf/expbackoff = 4

# number of clusters to generate in the shrunken network -- default separation
# [type: int, advanced: TRUE, range: [2,32], default: 5]
separating/mcf/nclusters = 5

# maximal valid range max(|weights|)/min(|weights|) of row weights
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 1000000]
separating/mcf/maxweightrange = 1000000

# maximal number of different deltas to try (-1: unlimited)  -- default separation
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 20]
separating/mcf/maxtestdelta = 20

# should negative values also be tested in scaling?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/mcf/trynegscaling = FALSE

# should an additional variable be complemented if f0 = 0?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/fixintegralrhs = TRUE

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/dynamiccuts = TRUE

# model type of network (0: auto, 1:directed, 2:undirected)
# [type: int, advanced: TRUE, range: [0,2], default: 0]
separating/mcf/modeltype = 0

# maximal number of mcf cuts separated per separation round
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
separating/mcf/maxsepacuts = 100

# maximal number of mcf cuts separated per separation round in the root node  -- default separation
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 200]
separating/mcf/maxsepacutsroot = 200

# maximum inconsistency ratio for separation at all
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.02]
separating/mcf/maxinconsistencyratio = 0.02

# maximum inconsistency ratio of arcs not to be deleted
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.5]
separating/mcf/maxarcinconsistencyratio = 0.5

# should we separate only if the cuts shores are connected?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/checkcutshoreconnectivity = TRUE

# should we separate inequalities based on single-node cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/separatesinglenodecuts = TRUE

# should we separate flowcutset inequalities on the network cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/separateflowcutset = TRUE

# should we separate knapsack cover inequalities on the network cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/mcf/separateknapsack = TRUE

# priority of separator <minor>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
separating/minor/priority = 0

# frequency for calling separator <minor> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/minor/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <minor> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/minor/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/minor/delay = FALSE

# base for exponential increase of frequency at which separator <minor> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/minor/expbackoff = 4

# constant for the maximum number of minors, i.e., max(const, fac * # quadratic terms)
# [type: int, advanced: FALSE, range: [0,2147483647], default: 3000]
separating/minor/maxminorsconst = 3000

# factor for the maximum number of minors, i.e., max(const, fac * # quadratic terms)
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 10]
separating/minor/maxminorsfac = 10

# minimum required violation of a cut
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 0.0001]
separating/minor/mincutviol = 0.0001

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/minor/maxrounds = 10

# maximal number of separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/minor/maxroundsroot = -1

# whether to ignore circle packing constraints during minor detection
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/minor/ignorepackingconss = TRUE

# priority of separator <mixing>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -50]
separating/mixing/priority = -50

# frequency for calling separator <mixing> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/mixing/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <mixing> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/mixing/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/mixing/delay = FALSE

# base for exponential increase of frequency at which separator <mixing> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/mixing/expbackoff = 4

# Should local bounds be used?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/mixing/uselocalbounds = FALSE

# Should general integer variables be used to generate cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/mixing/iscutsonints = FALSE

# maximal number of mixing separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/mixing/maxrounds = -1

# maximal number of mixing separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/mixing/maxroundsroot = -1

# maximal number of consecutive unsuccessful iterations
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/mixing/maxnunsuccessful = 10

# priority of separator <oddcycle>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -15000]
separating/oddcycle/priority = -15000

# frequency for calling separator <oddcycle> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
separating/oddcycle/freq = -1

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <oddcycle> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/oddcycle/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/oddcycle/delay = FALSE

# base for exponential increase of frequency at which separator <oddcycle> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/oddcycle/expbackoff = 4

# Should the search method by Groetschel, Lovasz, Schrijver be used? Otherwise use levelgraph method by Hoffman, Padberg.
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/usegls = TRUE

# Should odd cycle cuts be lifted?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/oddcycle/liftoddcycles = FALSE

# maximal number of oddcycle cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 5000]
separating/oddcycle/maxsepacuts = 5000

# maximal number of oddcycle cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 5000]
separating/oddcycle/maxsepacutsroot = 5000

# maximal number of oddcycle separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/oddcycle/maxrounds = 10

# maximal number of oddcycle separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/oddcycle/maxroundsroot = 10

# factor for scaling of the arc-weights
# [type: int, advanced: TRUE, range: [1,2147483647], default: 1000]
separating/oddcycle/scalingfactor = 1000

# add links between a variable and its negated
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/addselfarcs = TRUE

# try to repair violated cycles with double appearance of a variable
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/repaircycles = TRUE

# separate triangles found as 3-cycles or repaired larger cycles
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/includetriangles = TRUE

# Even if a variable is already covered by a cut, still try it as start node for a cycle search?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/oddcycle/multiplecuts = FALSE

# Even if a variable is already covered by a cut, still allow another cut to cover it too?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/allowmultiplecuts = TRUE

# Choose lifting candidate by coef*lpvalue or only by coef?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/oddcycle/lpliftcoef = FALSE

# Calculate lifting coefficient of every candidate in every step (or only if its chosen)?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/recalcliftcoef = TRUE

# use sorted variable array (unsorted(0), maxlp(1), minlp(2), maxfrac(3), minfrac(4))
# [type: int, advanced: TRUE, range: [0,4], default: 3]
separating/oddcycle/sortswitch = 3

# sort level of the root neighbors by fractionality (maxfrac)
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/oddcycle/sortrootneighbors = TRUE

# percentage of variables to try the chosen method on [0-100]
# [type: int, advanced: TRUE, range: [0,100], default: 0]
separating/oddcycle/percenttestvars = 0

# offset of variables to try the chosen method on (additional to the percentage of testvars)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
separating/oddcycle/offsettestvars = 100

# percentage of nodes allowed in the same level of the level graph [0-100]
# [type: int, advanced: TRUE, range: [0,100], default: 100]
separating/oddcycle/maxpernodeslevel = 100

# offset of nodes allowed in the same level of the level graph (additional to the percentage of levelnodes)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10]
separating/oddcycle/offsetnodeslevel = 10

# maximal number of levels in level graph
# [type: int, advanced: TRUE, range: [0,2147483647], default: 20]
separating/oddcycle/maxnlevels = 20

# maximal number of oddcycle cuts generated per chosen variable as root of the level graph
# [type: int, advanced: TRUE, range: [0,2147483647], default: 1]
separating/oddcycle/maxcutsroot = 1

# maximal number of oddcycle cuts generated in every level of the level graph
# [type: int, advanced: TRUE, range: [0,2147483647], default: 50]
separating/oddcycle/maxcutslevel = 50

# minimal weight on an edge (in level graph or bipartite graph)
# [type: int, advanced: TRUE, range: [0,2147483647], default: 0]
separating/oddcycle/maxreference = 0

# number of unsuccessful calls at current node
# [type: int, advanced: TRUE, range: [0,2147483647], default: 3]
separating/oddcycle/maxunsucessfull = 3

# maximal number of other cuts s.t. separation is applied (-1 for direct call)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: -1]
separating/oddcycle/cutthreshold = -1

# priority of separator <rapidlearning>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -1200000]
separating/rapidlearning/priority = -1200000

# frequency for calling separator <rapidlearning> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 5]
separating/rapidlearning/freq = 5

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <rapidlearning> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/rapidlearning/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/delay = FALSE

# base for exponential increase of frequency at which separator <rapidlearning> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/rapidlearning/expbackoff = 4

# should the found conflicts be applied in the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/applyconflicts = TRUE

# should the found global bound deductions be applied in the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/applybdchgs = TRUE

# should the inference values be used as initialization in the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/applyinfervals = TRUE

# should the inference values only be used when rapidlearning found other reductions?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/reducedinfer = FALSE

# should the incumbent solution be copied to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/applyprimalsol = TRUE

# should a solved status be copied to the original SCIP?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/applysolved = TRUE

# should local LP degeneracy be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/checkdegeneracy = TRUE

# should the progress on the dual bound be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/checkdualbound = FALSE

# should the ratio of leaves proven to be infeasible and exceeding the cutoff bound be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/checkleaves = FALSE

# check whether rapid learning should be executed
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/checkexec = TRUE

# should the (local) objective function be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/checkobj = FALSE

# should the number of solutions found so far be checked?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/checknsols = TRUE

# should rapid learning be applied when there are continuous variables?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rapidlearning/contvars = FALSE

# maximal portion of continuous variables to apply rapid learning
# [type: real, advanced: TRUE, range: [0,1], default: 0.3]
separating/rapidlearning/contvarsquot = 0.3

# maximal fraction of LP iterations compared to node LP iterations
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.2]
separating/rapidlearning/lpiterquot = 0.2

# minimal degeneracy threshold to allow local rapid learning
# [type: real, advanced: TRUE, range: [0,1], default: 0.7]
separating/rapidlearning/mindegeneracy = 0.7

# minimal threshold of inf/obj leaves to allow local rapid learning
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 10]
separating/rapidlearning/mininflpratio = 10

# minimal ratio of unfixed variables in relation to basis size to allow local rapid learning
# [type: real, advanced: TRUE, range: [1,1.79769313486232e+308], default: 2]
separating/rapidlearning/minvarconsratio = 2

# maximum problem size (variables) for which rapid learning will be called
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10000]
separating/rapidlearning/maxnvars = 10000

# maximum problem size (constraints) for which rapid learning will be called
# [type: int, advanced: TRUE, range: [0,2147483647], default: 10000]
separating/rapidlearning/maxnconss = 10000

# maximum number of overall calls
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
separating/rapidlearning/maxcalls = 100

# maximum number of nodes considered in rapid learning run
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5000]
separating/rapidlearning/maxnodes = 5000

# minimum number of nodes considered in rapid learning run
# [type: int, advanced: TRUE, range: [0,2147483647], default: 500]
separating/rapidlearning/minnodes = 500

# number of nodes that should be processed before rapid learning is executed locally based on the progress of the dualbound
# [type: longint, advanced: TRUE, range: [0,9223372036854775807], default: 100]
separating/rapidlearning/nwaitingnodes = 100

# should all active cuts from cutpool be copied to constraints in subproblem?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
separating/rapidlearning/copycuts = TRUE

# priority of separator <rlt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10]
separating/rlt/priority = 10

# frequency for calling separator <rlt> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 0]
separating/rlt/freq = 0

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <rlt> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/rlt/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/delay = FALSE

# base for exponential increase of frequency at which separator <rlt> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/rlt/expbackoff = 4

# maximal number of rlt-cuts that are added per round (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: -1]
separating/rlt/maxncuts = -1

# maximal number of unknown bilinear terms a row is still used with (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 0]
separating/rlt/maxunknownterms = 0

# maximal number of variables used to compute rlt cuts (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 100]
separating/rlt/maxusedvars = 100

# maximal number of separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
separating/rlt/maxrounds = 1

# maximal number of separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 10]
separating/rlt/maxroundsroot = 10

# if set to true, only equality rows are used for rlt cuts
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/onlyeqrows = FALSE

# if set to true, only continuous rows are used for rlt cuts
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/onlycontrows = FALSE

# if set to true, only original rows and variables are used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/rlt/onlyoriginal = TRUE

# if set to true, rlt is also used in sub-scips
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/useinsubscip = FALSE

# if set to true, projected rows are checked first
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/useprojection = FALSE

# if set to true, hidden products are detected and separated by McCormick cuts
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/detecthidden = FALSE

# whether RLT cuts (TRUE) or only McCormick inequalities (FALSE) should be added for hidden products
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
separating/rlt/hiddenrlt = FALSE

# if set to true, globally valid RLT cuts are added to the global cut pool
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/rlt/addtopool = TRUE

# threshold for score of cut relative to best score to be considered good, so that less strict filtering is applied
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/rlt/goodscore = 1

# threshold for score of cut relative to best score to be discarded
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
separating/rlt/badscore = 0.5

# weight of objective parallelism in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/rlt/objparalweight = 0

# weight of efficacy in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/rlt/efficacyweight = 1

# weight of directed cutoff distance in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/rlt/dircutoffdistweight = 0

# maximum parallelism for good cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
separating/rlt/goodmaxparall = 0.1

# maximum parallelism for non-good cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
separating/rlt/maxparall = 0.1

# priority of separator <zerohalf>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: -6000]
separating/zerohalf/priority = -6000

# frequency for calling separator <zerohalf> (-1: never, 0: only in root node)
# [type: int, advanced: FALSE, range: [-1,1073741822], default: 10]
separating/zerohalf/freq = 10

# maximal relative distance from current node's dual bound to primal bound compared to best node's dual bound for applying separator <zerohalf> (0.0: only on current best node, 1.0: on all nodes)
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/zerohalf/maxbounddist = 1

# should separator be delayed, if other separators found cuts?
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
separating/zerohalf/delay = FALSE

# base for exponential increase of frequency at which separator <zerohalf> is called (1: call at each multiple of frequency)
# [type: int, advanced: TRUE, range: [1,100], default: 4]
separating/zerohalf/expbackoff = 4

# maximal number of zerohalf separation rounds per node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 5]
separating/zerohalf/maxrounds = 5

# maximal number of zerohalf separation rounds in the root node (-1: unlimited)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 20]
separating/zerohalf/maxroundsroot = 20

# maximal number of zerohalf cuts separated per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 20]
separating/zerohalf/maxsepacuts = 20

# initial seed used for random tie-breaking in cut selection
# [type: int, advanced: FALSE, range: [0,2147483647], default: 24301]
separating/zerohalf/initseed = 24301

# maximal number of zerohalf cuts separated per separation round in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 100]
separating/zerohalf/maxsepacutsroot = 100

# maximal number of zerohalf cuts considered per separation round
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2000]
separating/zerohalf/maxcutcands = 2000

# maximal slack of rows to be used in aggregation
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
separating/zerohalf/maxslack = 0

# maximal slack of rows to be used in aggregation in the root node
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0]
separating/zerohalf/maxslackroot = 0

# threshold for score of cut relative to best score to be considered good, so that less strict filtering is applied
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/zerohalf/goodscore = 1

# threshold for score of cut relative to best score to be discarded
# [type: real, advanced: TRUE, range: [0,1], default: 0.5]
separating/zerohalf/badscore = 0.5

# weight of objective parallelism in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/zerohalf/objparalweight = 0

# weight of efficacy in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 1]
separating/zerohalf/efficacyweight = 1

# weight of directed cutoff distance in cut score calculation
# [type: real, advanced: TRUE, range: [0,1], default: 0]
separating/zerohalf/dircutoffdistweight = 0

# maximum parallelism for good cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
separating/zerohalf/goodmaxparall = 0.1

# maximum parallelism for non-good cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.1]
separating/zerohalf/maxparall = 0.1

# minimal violation to generate zerohalfcut for
# [type: real, advanced: TRUE, range: [0,1.79769313486232e+308], default: 0.1]
separating/zerohalf/minviol = 0.1

# should generated cuts be removed from the LP if they are no longer tight?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
separating/zerohalf/dynamiccuts = TRUE

# maximal density of row to be used in aggregation
# [type: real, advanced: TRUE, range: [0,1], default: 0.05]
separating/zerohalf/maxrowdensity = 0.05

# additional number of variables allowed in row on top of density
# [type: int, advanced: TRUE, range: [0,2147483647], default: 100]
separating/zerohalf/densityoffset = 100

# display activation status of display column <solfound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/solfound/active = 1

# display activation status of display column <concsolfound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/concsolfound/active = 1

# display activation status of display column <time> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/time/active = 1

# display activation status of display column <nnodes> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nnodes/active = 1

# display activation status of display column <nodesleft> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nodesleft/active = 1

# display activation status of display column <nobjleaves> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nobjleaves/active = 1

# display activation status of display column <ninfeasleaves> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/ninfeasleaves/active = 1

# display activation status of display column <lpiterations> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/lpiterations/active = 1

# display activation status of display column <lpavgiterations> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/lpavgiterations/active = 1

# display activation status of display column <lpcond> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/lpcond/active = 1

# display activation status of display column <memused> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/memused/active = 1

# display activation status of display column <concmemused> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/concmemused/active = 1

# display activation status of display column <memtotal> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/memtotal/active = 1

# display activation status of display column <depth> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/depth/active = 1

# display activation status of display column <maxdepth> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/maxdepth/active = 1

# display activation status of display column <plungedepth> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/plungedepth/active = 1

# display activation status of display column <nfrac> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nfrac/active = 1

# display activation status of display column <nexternbranchcands> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nexternbranchcands/active = 1

# display activation status of display column <vars> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/vars/active = 1

# display activation status of display column <conss> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/conss/active = 1

# display activation status of display column <curconss> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/curconss/active = 1

# display activation status of display column <curcols> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/curcols/active = 1

# display activation status of display column <currows> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/currows/active = 1

# display activation status of display column <cuts> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/cuts/active = 1

# display activation status of display column <separounds> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/separounds/active = 1

# display activation status of display column <poolsize> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/poolsize/active = 1

# display activation status of display column <conflicts> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/conflicts/active = 1

# display activation status of display column <strongbranchs> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/strongbranchs/active = 1

# display activation status of display column <pseudoobj> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/pseudoobj/active = 1

# display activation status of display column <lpobj> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/lpobj/active = 1

# display activation status of display column <curdualbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/curdualbound/active = 1

# display activation status of display column <estimate> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/estimate/active = 1

# display activation status of display column <avgdualbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/avgdualbound/active = 1

# display activation status of display column <dualbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/dualbound/active = 1

# display activation status of display column <primalbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/primalbound/active = 1

# display activation status of display column <concdualbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/concdualbound/active = 1

# display activation status of display column <concprimalbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/concprimalbound/active = 1

# display activation status of display column <cutoffbound> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/cutoffbound/active = 1

# display activation status of display column <gap> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/gap/active = 1

# display activation status of display column <concgap> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/concgap/active = 1

# display activation status of display column <primalgap> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 0]
display/primalgap/active = 0

# display activation status of display column <nsols> (0: off, 1: auto, 2:on)
# [type: int, advanced: FALSE, range: [0,2], default: 1]
display/nsols/active = 1

# is statistics table <status> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/status/active = TRUE

# is statistics table <timing> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/timing/active = TRUE

# is statistics table <origprob> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/origprob/active = TRUE

# is statistics table <presolvedprob> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/presolvedprob/active = TRUE

# is statistics table <presolver> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/presolver/active = TRUE

# is statistics table <constraint> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/constraint/active = TRUE

# is statistics table <constiming> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/constiming/active = TRUE

# is statistics table <propagator> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/propagator/active = TRUE

# is statistics table <conflict> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/conflict/active = TRUE

# is statistics table <separator> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/separator/active = TRUE

# is statistics table <cutsel> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/cutsel/active = TRUE

# is statistics table <pricer> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/pricer/active = TRUE

# is statistics table <branchrules> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/branchrules/active = TRUE

# is statistics table <heuristics> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/heuristics/active = TRUE

# is statistics table <compression> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/compression/active = TRUE

# is statistics table <benders> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/benders/active = TRUE

# is statistics table <exprhdlr> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/exprhdlr/active = TRUE

# is statistics table <lp> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/lp/active = TRUE

# is statistics table <nlp> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/nlp/active = TRUE

# is statistics table <nlpi> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/nlpi/active = TRUE

# is statistics table <relaxator> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/relaxator/active = TRUE

# is statistics table <tree> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/tree/active = TRUE

# is statistics table <root> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/root/active = TRUE

# is statistics table <solution> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/solution/active = TRUE

# is statistics table <concurrentsolver> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
table/concurrentsolver/active = TRUE

# soft time limit which should be applied after first solution was found (-1.0: disabled)
# [type: real, advanced: FALSE, range: [-1,1.79769313486232e+308], default: -1]
limits/softtime = -1

# the preferred number concurrent solvers of type <scip> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 1]
concurrent/scip/prefprio = 1

# the preferred number concurrent solvers of type <scip-default> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-default/prefprio = 0

# the preferred number concurrent solvers of type <scip-cpsolver> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-cpsolver/prefprio = 0

# the preferred number concurrent solvers of type <scip-easycip> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-easycip/prefprio = 0

# the preferred number concurrent solvers of type <scip-feas> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-feas/prefprio = 0

# the preferred number concurrent solvers of type <scip-hardlp> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-hardlp/prefprio = 0

# the preferred number concurrent solvers of type <scip-opti> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-opti/prefprio = 0

# the preferred number concurrent solvers of type <scip-counter> with respect to the number of threads
# [type: real, advanced: FALSE, range: [0,1], default: 0]
concurrent/scip-counter/prefprio = 0

# priority of Benders' decomposition <default>
# [type: int, advanced: FALSE, range: [-536870912,536870911], default: 0]
benders/default/priority = 0

# should Benders' cuts be generated for LP solutions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/cutlp = TRUE

# should Benders' cuts be generated for pseudo solutions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/cutpseudo = TRUE

# should Benders' cuts be generated for relaxation solutions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/cutrelax = TRUE

# should Benders' cuts from LNS heuristics be transferred to the main SCIP instance?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/transfercuts = FALSE

# should Benders' decomposition be used in LNS heurisics?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/lnscheck = TRUE

# maximum depth at which the LNS check is performed (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,1073741822], default: -1]
benders/default/lnsmaxdepth = -1

# the maximum number of Benders' decomposition calls in LNS heuristics (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 10]
benders/default/lnsmaxcalls = 10

# the maximum number of root node Benders' decomposition calls in LNS heuristics (-1: no limit)
# [type: int, advanced: TRUE, range: [-1,2147483647], default: 0]
benders/default/lnsmaxcallsroot = 0

# should the transferred cuts be added as constraints?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/cutsasconss = TRUE

# fraction of subproblems that are solved in each iteration
# [type: real, advanced: FALSE, range: [0,1], default: 1]
benders/default/subprobfrac = 1

# should the auxiliary variable bound be updated by solving the subproblem?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/updateauxvarbound = FALSE

# if the subproblem objective is integer, then define the auxiliary variables as implicit integers?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/auxvarsimplint = FALSE

# should Benders' cuts be generated while checking solutions?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/cutcheck = TRUE

# the convex combination multiplier for the cut strengthening
# [type: real, advanced: FALSE, range: [0,1], default: 0.5]
benders/default/cutstrengthenmult = 0.5

# the maximum number of cut strengthening without improvement
# [type: int, advanced: TRUE, range: [0,2147483647], default: 5]
benders/default/noimprovelimit = 5

# the constant use to perturb the cut strengthening core point
# [type: real, advanced: FALSE, range: [0,1], default: 1e-06]
benders/default/corepointperturb = 1e-06

# should the core point cut strengthening be employed (only applied to fractional solutions or continuous subproblems)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/cutstrengthenenabled = FALSE

# where should the strengthening interior point be sourced from ('l'p relaxation, 'f'irst solution, 'i'ncumbent solution, 'r'elative interior point, vector of 'o'nes, vector of 'z'eros)
# [type: char, advanced: FALSE, range: {lfiroz}, default: r]
benders/default/cutstrengthenintpoint = r

# the number of threads to use when solving the subproblems
# [type: int, advanced: TRUE, range: [1,2147483647], default: 1]
benders/default/numthreads = 1

# should a feasibility phase be executed during the root node, i.e. adding slack variables to constraints to ensure feasibility
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/execfeasphase = FALSE

# the initial objective coefficient of the slack variables in the subproblem
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1000000]
benders/default/slackvarcoef = 1000000

# the maximal objective coefficient of the slack variables in the subproblem
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1000000000]
benders/default/maxslackvarcoef = 1000000000

# should the constraints of the subproblems be checked for convexity?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/checkconsconvexity = TRUE

# iteration limit for NLP solver
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10000]
benders/default/nlpiterlimit = 10000

# priority of Benders' cut <feas>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10000]
benders/default/benderscut/feas/priority = 10000

# is this Benders' decomposition cut method used to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/feas/enabled = TRUE

# priority of Benders' cut <feasalt>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 10001]
benders/default/benderscut/feasalt/priority = 10001

# is this Benders' decomposition cut method used to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/feasalt/enabled = TRUE

# priority of Benders' cut <integer>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 0]
benders/default/benderscut/integer/priority = 0

# is this Benders' decomposition cut method used to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/integer/enabled = TRUE

# the constant term of the integer Benders' cuts.
# [type: real, advanced: FALSE, range: [-1e+20,1e+20], default: -10000]
benders/default/benderscut/integer/cutsconstant = -10000

# should cuts be generated and added to the cutpool instead of global constraints directly added to the problem.
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/benderscut/integer/addcuts = FALSE

# priority of Benders' cut <nogood>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 500]
benders/default/benderscut/nogood/priority = 500

# is this Benders' decomposition cut method used to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/nogood/enabled = TRUE

# should cuts be generated and added to the cutpool instead of global constraints directly added to the problem.
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/benderscut/nogood/addcuts = FALSE

# priority of Benders' cut <optimality>
# [type: int, advanced: TRUE, range: [-536870912,536870911], default: 5000]
benders/default/benderscut/optimality/priority = 5000

# is this Benders' decomposition cut method used to generate cuts?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/optimality/enabled = TRUE

# should cuts be generated and added to the cutpool instead of global constraints directly added to the problem.
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
benders/default/benderscut/optimality/addcuts = FALSE

# should the mixed integer rounding procedure be applied to cuts
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
benders/default/benderscut/optimality/mir = TRUE

# priority of cut selection rule <ensemble>
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 7000]
cutselection/ensemble/priority = 7000

# weight of normed-efficacy in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.75]
cutselection/ensemble/efficacyweight = 0.75

# weight of normed-directed cutoff distance in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/ensemble/dircutoffdistweight = 0

# weight of objective parallelism in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.25]
cutselection/ensemble/objparalweight = 0.25

# weight of integral support in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.45]
cutselection/ensemble/intsupportweight = 0.45

# weight of normed-expected obj improvement in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.1]
cutselection/ensemble/expimprovweight = 0.1

# minimum score s.t. a cut can be added
# [type: real, advanced: FALSE, range: [-1e+98,1e+98], default: 0]
cutselection/ensemble/minscore = 0

# weight of normed-pseudo-costs in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.75]
cutselection/ensemble/pscostweight = 0.75

# weight of normed-num-locks in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.25]
cutselection/ensemble/locksweight = 0.25

# weight of maximum sparsity reward in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.5]
cutselection/ensemble/maxsparsitybonus = 0.5

# weight of good numerics bonus (ratio of coefficients) in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/ensemble/goodnumericsbonus = 0

# max sparsity value for which a bonus is applied in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.2]
cutselection/ensemble/endsparsitybonus = 0.2

# threshold for when two cuts are considered parallel to each other
# [type: real, advanced: FALSE, range: [0,1], default: 0.95]
cutselection/ensemble/maxparal = 0.95

# penalty for weaker of two parallel cuts if penalising parallel cuts
# [type: real, advanced: TRUE, range: [0,1e+98], default: 0.25]
cutselection/ensemble/paralpenalty = 0.25

# max allowed cut density if filtering dense cuts
# [type: real, advanced: TRUE, range: [0,1], default: 0.425]
cutselection/ensemble/maxcutdensity = 0.425

# max non-zeros per round applied cuts (root). multiple num LP cols.
# [type: real, advanced: FALSE, range: [0,1e+98], default: 4.5]
cutselection/ensemble/maxnonzerorootround = 4.5

# max non-zeros per round applied cuts (tree). multiple num LP cols.
# [type: real, advanced: FALSE, range: [0,1e+98], default: 9.5]
cutselection/ensemble/maxnonzerotreeround = 9.5

# should cuts be filtered so no two parallel cuts are added
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
cutselection/ensemble/filterparalcuts = FALSE

# should two parallel cuts be penalised instead of outright filtered
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
cutselection/ensemble/penaliseparalcuts = TRUE

# should cuts over a given density threshold be filtered
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
cutselection/ensemble/filterdensecuts = TRUE

# should the number of locks be penalised instead of rewarded
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
cutselection/ensemble/penaliselocks = TRUE

# should objective parallelism be penalised instead of rewarded
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
cutselection/ensemble/penaliseobjparal = TRUE

# max coefficient ratio for which numeric bonus is applied.
# [type: int, advanced: TRUE, range: [1,1000000], default: 10000]
cutselection/ensemble/maxcoefratiobonus = 10000

# max number of cuts such that cut selector is applied.
# [type: int, advanced: TRUE, range: [1,1000000], default: 200]
cutselection/ensemble/maxcuts = 200

# max number of variables such that cut selector is applied.
# [type: int, advanced: TRUE, range: [1,1000000], default: 50000]
cutselection/ensemble/maxnumvars = 50000

# priority of cut selection rule <hybrid>
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 8000]
cutselection/hybrid/priority = 8000

# weight of efficacy in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 1]
cutselection/hybrid/efficacyweight = 1

# weight of directed cutoff distance in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/hybrid/dircutoffdistweight = 0

# weight of objective parallelism in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.1]
cutselection/hybrid/objparalweight = 0.1

# weight of integral support in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0.1]
cutselection/hybrid/intsupportweight = 0.1

# minimal orthogonality for a cut to enter the LP
# [type: real, advanced: FALSE, range: [0,1], default: 0.9]
cutselection/hybrid/minortho = 0.9

# minimal orthogonality for a cut to enter the LP in the root node
# [type: real, advanced: FALSE, range: [0,1], default: 0.9]
cutselection/hybrid/minorthoroot = 0.9

# priority of cut selection rule <dynamic>
# [type: int, advanced: FALSE, range: [-536870912,1073741823], default: 7000]
cutselection/dynamic/priority = 7000

# weight of efficacy in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 1]
cutselection/dynamic/efficacyweight = 1

# weight of directed cutoff distance in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/dynamic/dircutoffdistweight = 0

# weight of objective parallelism in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/dynamic/objparalweight = 0

# weight of integral support in cut score calculation
# [type: real, advanced: FALSE, range: [0,1e+98], default: 0]
cutselection/dynamic/intsupportweight = 0

# minimal efficacy gain for a cut to enter the LP
# [type: real, advanced: FALSE, range: [0,1], default: 0.01]
cutselection/dynamic/mingain = 0.01

# filtering strategy during cut selection
# [type: char, advanced: FALSE, range: {df}, default: d]
cutselection/dynamic/filtermode = d

# minimal orthogonality for a cut to enter the LP
# [type: real, advanced: FALSE, range: [0,1], default: 0.9]
cutselection/dynamic/minortho = 0.9

# maximum depth at which this cutselector is employed
# [type: int, advanced: FALSE, range: [-1,1073741822], default: -1]
cutselection/dynamic/maxdepth = -1

# minimal distance from zero to enforce for child in bound tightening
# [type: real, advanced: FALSE, range: [0,1], default: 1e-09]
expr/log/minzerodistance = 1e-09

# minimal distance from zero to enforce for child in bound tightening
# [type: real, advanced: FALSE, range: [0,1], default: 1e-09]
expr/pow/minzerodistance = 1e-09

# maximal exponent when to expand power of sum in simplify
# [type: int, advanced: FALSE, range: [1,2147483647], default: 2]
expr/pow/expandmaxexponent = 2

# whether a fractional exponent is distributed onto factors on power of product
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
expr/pow/distribfracexponent = FALSE

# whether to expand products of a sum and several factors in simplify
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
expr/prod/expandalways = FALSE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/default/enabled = TRUE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/convex/enabled = TRUE

# whether to run convexity detection when the root of an expression is a non-quadratic sum
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/convex/detectsum = FALSE

# whether to create extended formulations instead of looking for maximal convex expressions
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/convex/extendedform = TRUE

# maximal relative perturbation of non-differentiable reference point
# [type: real, advanced: FALSE, range: [0,1], default: 0.01]
nlhdlr/convex/maxperturb = 0.01

# whether to use convexity check on quadratics
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/convex/cvxquadratic = TRUE

# whether to use convexity check on signomials
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/convex/cvxsignomial = TRUE

# whether to use convexity check on product composition f(h)*h
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/convex/cvxprodcomp = TRUE

# whether to also handle trivial convex expressions
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/convex/handletrivial = FALSE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/concave/enabled = TRUE

# whether to run convexity detection when the root of an expression is a sum
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/concave/detectsum = FALSE

# whether to use convexity check on quadratics
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/concave/cvxquadratic = FALSE

# whether to use convexity check on signomials
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/concave/cvxsignomial = TRUE

# whether to use convexity check on product composition f(h)*h
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/concave/cvxprodcomp = TRUE

# whether to also handle trivial convex expressions
# [type: bool, advanced: TRUE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/concave/handletrivial = FALSE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/bilinear/enabled = TRUE

# whether to use the interval evaluation callback of the nlhdlr
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/bilinear/useinteval = TRUE

# whether to use the reverse propagation callback of the nlhdlr
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/bilinear/usereverseprop = TRUE

# maximum number of separation rounds in the root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 10]
nlhdlr/bilinear/maxseparoundsroot = 10

# maximum number of separation rounds in a local node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 1]
nlhdlr/bilinear/maxseparounds = 1

# maximum depth to apply separation
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2147483647]
nlhdlr/bilinear/maxsepadepth = 2147483647

# is statistics table <nlhdlr_bilinear> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
table/nlhdlr_bilinear/active = FALSE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/perspective/enabled = TRUE

# maximal number of propagation rounds in probing
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
nlhdlr/perspective/maxproprounds = 1

# minimal relative reduction in a variable's domain for applying probing
# [type: real, advanced: FALSE, range: [0,1], default: 0.1]
nlhdlr/perspective/mindomreduction = 0.1

# minimal violation w.r.t. auxiliary variables for applying probing
# [type: real, advanced: FALSE, range: [0,1.79769313486232e+308], default: 1e-05]
nlhdlr/perspective/minviolprobing = 1e-05

# whether to do probing only in separation
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/perspective/probingonlyinsepa = TRUE

# probing frequency (-1 - no probing, 0 - root node only)
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
nlhdlr/perspective/probingfreq = 1

# whether perspective cuts are added only for convex expressions
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/perspective/convexonly = FALSE

# whether variable semicontinuity is used to tighten variable bounds
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/perspective/tightenbounds = TRUE

# whether to adjust the reference point
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/perspective/adjrefpoint = TRUE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quadratic/enabled = TRUE

# whether to use intersection cuts for quadratic constraints to separate
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/quadratic/useintersectioncuts = FALSE

# whether the strengthening should be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/quadratic/usestrengthening = FALSE

# whether monoidal strengthening should be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quadratic/usemonoidal = TRUE

# whether the minimal representation of the S-free set should be used (instead of the gauge)
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quadratic/useminrep = TRUE

# use bounds of variables in quadratic as rays for intersection cuts
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/quadratic/useboundsasrays = FALSE

# limit for number of cuts generated consecutively
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2]
nlhdlr/quadratic/ncutslimit = 2

# limit for number of cuts generated at root node
# [type: int, advanced: FALSE, range: [0,2147483647], default: 20]
nlhdlr/quadratic/ncutslimitroot = 20

# maximal rank a slackvar can have
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2147483647]
nlhdlr/quadratic/maxrank = 2147483647

# minimal cut violation the generated cuts must fulfill to be added to the LP
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.0001]
nlhdlr/quadratic/mincutviolation = 0.0001

# minimal violation the constraint must fulfill such that a cut is generated
# [type: real, advanced: FALSE, range: [0,1e+20], default: 0.0001]
nlhdlr/quadratic/minviolation = 0.0001

# determines at which nodes cut is used (if it's -1, it's used only at the root node, if it's n >= 0, it's used at every multiple of n
# [type: int, advanced: FALSE, range: [-1,2147483647], default: 1]
nlhdlr/quadratic/atwhichnodes = 1

# limit for number of rays we do the strengthening for
# [type: int, advanced: FALSE, range: [0,2147483647], default: 2147483647]
nlhdlr/quadratic/nstrengthlimit = 2147483647

# should we try to sparisfy the intersection cut?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/quadratic/sparsifycuts = FALSE

# should cut be generated even with bad numerics when restricting to ray?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quadratic/ignorebadrayrestriction = TRUE

# should cut be added even when range / efficacy is large?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quadratic/ignorenhighre = TRUE

# for monoidal strengthening, should we track more statistics (more expensive)?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
nlhdlr/quadratic/trackmore = FALSE

# is statistics table <nlhdlr_quadratic> active
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: FALSE]
table/nlhdlr_quadratic/active = FALSE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/quotient/enabled = TRUE

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/signomial/enabled = TRUE

# maximum number of variables when underestimating a concave power function
# [type: int, advanced: TRUE, range: [2,14], default: 14]
nlhdlr/signomial/maxnundervars = 14

# minimum scale factor when scaling a cut
# [type: real, advanced: TRUE, range: [1e-06,1000000], default: 1e-05]
nlhdlr/signomial/mincutscale = 1e-05

# should this nonlinear handler be used
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/soc/enabled = TRUE

# Minimum efficacy which a cut needs in order to be added.
# [type: real, advanced: FALSE, range: [0,1e+20], default: 1e-05]
nlhdlr/soc/mincutefficacy = 1e-05

# Should Eigenvalue computations be done to detect complex cases in quadratic constraints?
# [type: bool, advanced: FALSE, range: {TRUE,FALSE}, default: TRUE]
nlhdlr/soc/compeigenvalues = TRUE