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Tutorial: the interactive shell If are using SCIP as a black box solver, here you will find some tips and tricks what you can do. First of all, we need a SCIP binary and an example problem file to work with. Therefore, you can either download the SCIP standard distribution (which includes problem files) and compile it on your own or you can download a precompiled binary and an example problem separately. SCIP can read files in LP, MPS, ZPL, WBO, FZN, PIP, OSiL, and other formats (see File Readers). If you want to download the source code of the SCIP standard distribution, we recommend to go to the SCIP download section, download the latest release (version 3.0 as of this writing), inflate the tarball (e.g., with "tar xzf scipoptsuite-[version].tgz"), and follow the instructions in the INSTALL file. The instance stein27, which will serve as an example in this tutorial, can be found under scipoptsuite-[version]/scip-[version]/check/instances/MIP/stein27.mps. If you want to download a precompiled binary, go to the SCIP download section and download an appropriate binary for your operating system. To follow this tutorial, we recommend downloading the instance stein27 from the MIPLIB 3.0 homepage. Now start your binary, without any arguments. This opens the interactive shell, which should look somehow like this: SCIP version 2.0.1 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: SoPlex 1.5.0]
Copyright (c) 2002-2014 Konrad-Zuse-Zentrum fuer Informationstechnik Berlin (ZIB)
External codes:
SoPlex 1.5.0 Linear Programming Solver developed at Zuse Institute Berlin (soplex.zib.de)
ZIMPL 3.1.0 Zuse Institute Mathematical Programming Language developed by T. Koch (zimpl.zib.de)
user parameter file <scip.set> not found - using default parameters
SCIP>
First of all "help" shows you a list of all available shell commands. Brackets indicate a submenu with further options. Okay, let's solve some MIPs... use "read <path/to/file>" to parse a problem file, "optimize" to solve it and "display solution" to show the nonzero variables of the best found solution. SCIP> read check/instances/MIP/stein27.mps
original problem has 27 variables (27 bin, 0 int, 0 impl, 0 cont) and 118 constraints
SCIP> optimize
feasible solution found by trivial heuristic, objective value 2.700000e+01
presolving:
(round 1) 0 del vars, 0 del conss, 0 chg bounds, 0 chg sides, 0 chg coeffs, 118 upgd conss, 0 impls, 0 clqs
presolving (2 rounds):
0 deleted vars, 0 deleted constraints, 0 tightened bounds, 0 added holes, 0 changed sides, 0 changed coefficients
0 implications, 0 cliques
presolved problem has 27 variables (27 bin, 0 int, 0 impl, 0 cont) and 118 constraints
1 constraints of type <knapsack>
117 constraints of type <logicor>
transformed objective value is always integral (scale: 1)
Presolving Time: 0.00
time | node | left |LP iter|LP it/n| mem |mdpt |frac |vars |cons |cols |rows |cuts |confs|strbr| dualbound | primalbound | gap
t 0.0s| 1 | 0 | 34 | - | 337k| 0 | 21 | 27 | 118 | 27 | 118 | 0 | 0 | 0 | 1.300000e+01 | 2.700000e+01 | 107.69%
R 0.0s| 1 | 0 | 34 | - | 338k| 0 | 21 | 27 | 118 | 27 | 118 | 0 | 0 | 0 | 1.300000e+01 | 2.600000e+01 | 100.00%
s 0.0s| 1 | 0 | 34 | - | 339k| 0 | 21 | 27 | 118 | 27 | 118 | 0 | 0 | 0 | 1.300000e+01 | 2.500000e+01 | 92.31%
0.0s| 1 | 0 | 44 | - | 392k| 0 | 21 | 27 | 118 | 27 | 120 | 2 | 0 | 0 | 1.300000e+01 | 2.500000e+01 | 92.31%
b 0.0s| 1 | 0 | 44 | - | 393k| 0 | 21 | 27 | 118 | 27 | 120 | 2 | 0 | 0 | 1.300000e+01 | 1.900000e+01 | 46.15%
...
0.1s| 1 | 2 | 107 | - | 920k| 0 | 24 | 27 | 118 | 27 | 131 | 13 | 0 | 24 | 1.300000e+01 | 1.900000e+01 | 46.15%
R 0.1s| 14 | 10 | 203 | 7.4 | 935k| 13 | - | 27 | 118 | 27 | 124 | 13 | 0 | 164 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.1s| 100 | 54 | 688 | 5.9 | 994k| 13 | 20 | 27 | 118 | 27 | 124 | 13 | 0 | 206 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.1s| 200 | 86 | 1195 | 5.5 |1012k| 13 | - | 27 | 119 | 27 | 124 | 13 | 1 | 207 | 1.300000e+01 | 1.800000e+01 | 38.46%
time | node | left |LP iter|LP it/n| mem |mdpt |frac |vars |cons |cols |rows |cuts |confs|strbr| dualbound | primalbound | gap
0.2s| 300 | 106 | 1686 | 5.3 |1024k| 13 | - | 27 | 119 | 27 | 124 | 13 | 1 | 207 | 1.350000e+01 | 1.800000e+01 | 33.33%
...
0.7s| 4100 | 50 | 18328 | 4.4 |1033k| 16 | 8 | 27 | 119 | 27 | 124 | 13 | 15 | 207 | 1.650000e+01 | 1.800000e+01 | 9.09%
SCIP Status : problem is solved [optimal solution found]
Solving Time (sec) : 0.73
Solving Nodes : 4192
Primal Bound : +1.80000000000000e+01 (283 solutions)
Dual Bound : +1.80000000000000e+01
Gap : 0.00 %
SCIP> display solution
objective value: 18
x0001 1 (obj:1)
x0003 1 (obj:1)
...
x0027 1 (obj:1)
SCIP>
What do we see here? After "optimize", SCIP first goes into presolving. Not much is happening for this instance, just the linear constraints get upgraded to more specific types. Each round of presolving will be displayed in a single line, with a short summary at the end. Here, there has only been one round with actual changes, the second round did not bring any further reductions. Thus, it is not displayed and presolving is stopped. Then, we see the actual solving process. The first three output lines indicate that new incumbent solutions were found by the primal heuristics with display characters "t", "R", and "s"; see, how the "primalbound" column goes down from 27 to 25. In the fourth line, two "cuts" are added. Up to here, we needed 44 "LP iter"ations (34 for the first LP and 10 more to resolve after adding cuts). Little later, the root node processing is finished. We see that there are now two open nodes in the "left" column. From now on, we will see an output line every hundredth node or whenever a new incumbent is found (e.g. at node 14 in the above output). After some more nodes, the "dualbound" starts moving, too. At one point, both will be the same, and the solving process terminates, showing us some wrap-up information. The exact performance varies amongst different architectures, operating systems, and so on. Do not be worried if your installation needs more or less time or nodes to solve. Also, this instance has more than 2000 different optimal solutions. The optimal objective value always has to be 18, but the solution vector may differ. If you are interested in this behavior, which is called "performance variability", you may have a look at the MIPLIB2010 paper. We might want to have some more information now. Which were the heuristics that found the solutions? What plugins were called during the solutions process and how much time did they spend? How did the instance that we were solving look? Information on certain plugin types (e.g., heuristics, branching rules, separators) we get by "display <plugin-type>", information on the solution process, we get by "display statistics", and "display problem" shows us the current instance. SCIP> display heuristics
primal heuristic c priority freq ofs description
---------------- - -------- ---- --- -----------
trivial t 10000 0 0 start heuristic which tries some trivial solutions
...
rounding R -1000 1 0 LP rounding heuristic with infeasibility recovering
shifting s -5000 10 0 LP rounding heuristic with infeasibility recovering also using continuous variables
...
SCIP> display statistics
...
gomory : 0.02 6 0 0 461 0
cgmip : 0.00 0 0 0 0 0
strongcg : 0.01 6 0 0 598 0
...
oneopt : 0.01 4 1
coefdiving : 0.02 57 0
...
primal LP : 0.00 0 0 0.00 -
dual LP : 0.20 4187 14351 3.43 71755.00
...
We see that rounding and shifting were the heuristics producing the solutions in the beginning. Rounding is called at every node, shifting only at every tenth level of the tree. The statistics are quite comprehensive, thus, we just explain a few lines here. We get information for all types of plugins and for the overall solving process. Besides others, we see that in six calls, the gomory cut separator and the strong Chvátal-Gomory separator each produced several hundred cuts (of which only a few entered the LP). The oneopt heuristic found one solution in 4 calls, whereas coefdiving failed all 57 times it was called. All the LPs have been solved with the dual simplex algorithm, which took about 0.2 seconds of the 0.7 seconds overall solving time. Now, we can start playing around with parameters. Rounding and shifting seem to be quite successful on this instance, wondering what happens if we disable them? Or what happens, if we are even more rigorous and disable all heuristics? Or if we do the opposite and use aggressive heuristics? SCIP> set
<branching> change parameters for branching rules
...
<heuristics> change parameters for primal heuristics
SCIP/set> heuristics
<actconsdiving> LP diving heuristic that chooses fixings w.r.t. the active constraints
...
<shifting> LP rounding heuristic with infeasibility recovering also using continuous variables
...
SCIP/set/heuristics> shifting
<advanced> advanced parameters
freq frequency for calling primal heuristic <shifting> (-1: never, 0: only at depth freqofs) [10]
freqofs frequency offset for calling primal heuristic <shifting> [0]
SCIP/set/heuristics/shifting> freq
current value: 10, new value [-1,2147483647]: -1
heuristics/shifting/freq = -1
SCIP> se he rou freq -1
heuristics/rounding/freq = -1
SCIP> re check/instances/MIP/stein27.mps
original problem has 27 variables (27 bin, 0 int, 0 impl, 0 cont) and 118 constraints
SCIP> o
feasible solution found by trivial heuristic, objective value 2.700000e+01
...
z 0.1s| 3 | 4 | 140 | 10.5 |1060k| 2 | 22 | 27 | 118 | 27 | 123 | 14 | 0 | 66 | 1.300000e+01 | 1.900000e+01 | 46.15%
z 0.1s| 6 | 7 | 176 | 11.4 |1063k| 5 | 18 | 27 | 118 | 27 | 123 | 14 | 0 | 118 | 1.300000e+01 | 1.900000e+01 | 46.15%
* 0.1s| 39 | 28 | 386 | 7.0 |1092k| 14 | - | 27 | 118 | 27 | 123 | 14 | 0 | 199 | 1.300000e+01 | 1.800000e+01 | 38.46%
...
SCIP Status : problem is solved [optimal solution found]
Solving Time (sec) : 0.75
Solving Nodes : 4253
Primal Bound : +1.80000000000000e+01 (287 solutions)
Dual Bound : +1.80000000000000e+01
Gap : 0.00 %
SCIP>
We can navigate through the menus step-by-step and get a list of available options and submenus. Thus, we select "set" to change settings, "heuristics" to change settings of primal heuristics, "shifting" for that particular heuristic. Then we see a list of parameters (and yet another submenu for advanced parameters), and disable this heuristic by setting its calling frequency to -1. If we already know the path to a certain setting, we can directly type it (as for the rounding heuristic in the above example). Note that we do not have to use the full names, but we may use short versions, as long as they are unique. To solve a problem a second time, we have to read it and start the optimization process again. reset parameters to their default values
SCIP> set heuristics emphasis
aggressive sets heuristics <aggressive>
fast sets heuristics <fast>
off turns <off> all heuristics
SCIP/set/heuristics/emphasis> aggr
heuristics/veclendiving/freq = 5
...
heuristics/crossover/minfixingrate = 0.5
SCIP> read check/instances/MIP/stein27.mps
original problem has 27 variables (27 bin, 0 int, 0 impl, 0 cont) and 118 constraints
SCIP> opt
...
D 0.1s| 1 | 0 | 107 | - | 971k| 0 | 24 | 27 | 122 | 27 | 131 | 13 | 4 | 0 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.1s| 1 | 0 | 107 | - | 971k| 0 | 24 | 27 | 122 | 27 | 131 | 13 | 4 | 0 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.1s| 1 | 0 | 119 | - |1111k| 0 | 24 | 27 | 122 | 27 | 132 | 14 | 4 | 0 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.1s| 1 | 2 | 119 | - |1112k| 0 | 24 | 27 | 122 | 27 | 132 | 14 | 4 | 24 | 1.300000e+01 | 1.800000e+01 | 38.46%
time | node | left |LP iter|LP it/n| mem |mdpt |frac |vars |cons |cols |rows |cuts |confs|strbr| dualbound | primalbound | gap
0.2s| 100 | 59 | 698 | 5.8 |1138k| 14 | 11 | 27 | 122 | 27 | 123 | 14 | 4 | 204 | 1.300000e+01 | 1.800000e+01 | 38.46%
0.2s| 200 | 91 | 1226 | 5.6 |1155k| 14 | - | 27 | 122 | 27 | 123 | 14 | 4 | 207 | 1.300000e+01 | 1.800000e+01 | 38.46%
^Cpressed CTRL-C 1 times (5 times for forcing termination)
SCIP Status : solving was interrupted [user interrupt]
Solving Time (sec) : 0.32
Solving Nodes : 216
Primal Bound : +1.80000000000000e+01 (283 solutions)
Dual Bound : +1.30000000000000e+01
Gap : 38.46 %
SCIP>
Okay, what happened here? First, we reset all parameters to their default values, using "set default". Next, we loaded some meta-parameter settings (also see the FAQ), to apply primal heuristics more aggressively. SCIP shows us, which single parameters it changed therefor. Now, the optimal solution is already found at the root node, by a heuristic which is deactivated by default. Then, after node 200, the user pressed CTRL-C which interrupts the solving process, We see that now in the short status report, primal and dual bound are different, thus, the problem is not solved yet. Nevertheless, we could access statistics, see the current incumbent solution, change parameters and so on. Entering "optimize" we continue the solving process from the point on at which it has been interrupted. SCIP can also write information to files. E.g., we could store the incumbent solution to a file, or output the problem instance in another file format (the LP format is much more human readable than the MPS format, for example). SCIP> write solution stein27.sol
written solution information to file <stein27.sol>
SCIP> write problem stein27.lp
written original problem to file <stein27.lp>
SCIP> q
...
We hope this tutorial gave you an overview of what is possible using the SCIP interactive shell. Please also read our Frequently Asked Questions (FAQ), in particular the section Using SCIP as a standalone MIP/MINLP-Solver. |