Benders' decomposition cut method

Detailed Description

methods and files provided by the default Benders' decomposition cut method of SCIP

A detailed description what a Benders' decomposition cut method does and how to add a Benders' decomposition cut method to SCIP can be found here.

Modules
	Inclusion methods
	methods to include specific Benders' decomposition cut methods into SCIP

Functions
SCIP_RETCODE	SCIPgenerateAndApplyBendersOptCut (SCIP *masterprob, SCIP *subproblem, SCIP_BENDERS *benders, SCIP_BENDERSCUT *benderscut, SCIP_SOL *sol, int probnumber, char *cutname, SCIP_Real objective, SCIP_Real *primalvals, SCIP_Real *consdualvals, SCIP_Real *varlbdualvals, SCIP_Real *varubdualvals, SCIP_HASHMAP *row2idx, SCIP_HASHMAP *var2idx, SCIP_BENDERSENFOTYPE type, SCIP_Bool addcut, SCIP_Bool feasibilitycut, SCIP_RESULT *result)
SCIP_RETCODE	SCIPaddNlRowGradientBenderscutOpt (SCIP *masterprob, SCIP *subproblem, SCIP_BENDERS *benders, SCIP_NLROW *nlrow, SCIP_Real mult, SCIP_Real *primalvals, SCIP_HASHMAP *var2idx, SCIP_Real *dirderiv, SCIP_VAR ***vars, SCIP_Real **vals, int *nvars, int *varssize)

Files
file	benderscut_feas.h
	Standard feasibility cuts for Benders' decomposition.
file	benderscut_feasalt.h
	Alternative feasibility cuts for Benders' decomposition.
file	benderscut_int.h
	Generates a Laporte and Louveaux Benders' decomposition integer cut.
file	benderscut_nogood.h
	Generates a no-good cut for solutions that are integer infeasible.
file	benderscut_opt.h
	Generates a standard Benders' decomposition optimality cut.

Function Documentation

SCIPgenerateAndApplyBendersOptCut()

SCIP_RETCODE SCIPgenerateAndApplyBendersOptCut	(	SCIP *	masterprob,
		SCIP *	subproblem,
		SCIP_BENDERS *	benders,
		SCIP_BENDERSCUT *	benderscut,
		SCIP_SOL *	sol,
		int	probnumber,
		char *	cutname,
		SCIP_Real	objective,
		SCIP_Real *	primalvals,
		SCIP_Real *	consdualvals,
		SCIP_Real *	varlbdualvals,
		SCIP_Real *	varubdualvals,
		SCIP_HASHMAP *	row2idx,
		SCIP_HASHMAP *	var2idx,
		SCIP_BENDERSENFOTYPE	type,
		SCIP_Bool	addcut,
		SCIP_Bool	feasibilitycut,
		SCIP_RESULT *	result
	)

Generates a classical Benders' optimality cut using the dual solutions from the subproblem or the input arrays. If the dual solutions are input as arrays, then a mapping between the array indices and the rows/variables is required. This method can also be used to generate a feasiblity, is a problem to minimise the infeasibilities has been solved to generate the dual solutions

Generates a classical Benders' optimality cut using the dual solutions from the subproblem or the input arrays. If the dual solutions are input as arrays, then a mapping between the array indices and the rows/variables is required. As a cut strengthening approach, when an optimality cut is being generated (i.e. not for feasibility cuts) a MIR procedure is performed on the row. This procedure attempts to find a stronger constraint, if this doesn't happen, then the original constraint is added to SCIP.

This method can also be used to generate a feasibility cut, if a problem to minimise the infeasibilities has been solved to generate the dual solutions

Parameters

masterprob	the SCIP instance of the master problem
subproblem	the SCIP instance of the pricing problem
benders	the benders' decomposition
benderscut	the benders' decomposition cut method
sol	primal CIP solution
probnumber	the number of the pricing problem
cutname	the name for the cut to be generated
objective	the objective function of the subproblem
primalvals	the primal solutions for the NLP, can be NULL
consdualvals	dual variables for the constraints, can be NULL
varlbdualvals	the dual variables for the variable lower bounds, can be NULL
varubdualvals	the dual variables for the variable upper bounds, can be NULL
row2idx	mapping between the row in the subproblem to the index in the dual array, can be NULL
var2idx	mapping from variable of the subproblem to the index in the dual arrays, can be NULL
type	the enforcement type calling this function
addcut	should the Benders' cut be added as a cut or constraint
feasibilitycut	is this called for the generation of a feasibility cut
result	the result from solving the subproblems

Definition at line 836 of file benderscut_opt.c.

References addAuxiliaryVariableToCut(), checkSetupTolerances(), computeMIRForOptimalityCut(), computeStandardLPOptimalityCut(), computeStandardNLPOptimalityCut(), FALSE, NULL, SCIP_BENDERSENFOTYPE_CHECK, SCIP_BENDERSENFOTYPE_LP, SCIP_BENDERSENFOTYPE_PSEUDO, SCIP_BENDERSENFOTYPE_RELAX, SCIP_Bool, SCIP_CALL, SCIP_CONSADDED, SCIP_DIDNOTFIND, SCIP_FEASIBLE, SCIP_OKAY, SCIP_Real, SCIP_SEPARATED, SCIP_STAGE_INITSOLVE, SCIPABORT, SCIPaddCoefLinear(), SCIPaddCons(), SCIPaddPoolCut(), SCIPaddRow(), SCIPaddVarsToRow(), SCIPaddVarToRow(), SCIPallocBufferArray, SCIPallocClearBufferArray, SCIPbenderscutGetData(), SCIPbendersInStrengthenRound(), SCIPcheckBendersSubproblemOptimality(), SCIPcreateConsBasicLinear(), SCIPcreateEmptyRowConshdlr(), SCIPdebugMsg, SCIPdebugPrintCons, SCIPfeastol(), SCIPfindConshdlr(), SCIPfreeBufferArray, SCIPgetActivityLinear(), SCIPgetLhsLinear(), SCIPgetNFixedVars(), SCIPgetNNlpis(), SCIPgetNVars(), SCIPgetRowSolActivity(), SCIPgetSolVal(), SCIPgetStage(), SCIPgetSubscipDepth(), SCIPgetVars(), SCIPinfinity(), SCIPisFeasEQ(), SCIPisNLPConstructed(), SCIPreleaseCons(), SCIPreleaseRow(), SCIProwGetLhs(), SCIPsetConsDynamic(), SCIPsetConsRemovable(), SCIPstoreBendersCut(), SCIPvarGetName(), and TRUE.

Referenced by generateAndApplyBendersCuts(), and SCIP_DECL_BENDERSCUTEXEC().

SCIPaddNlRowGradientBenderscutOpt()

SCIP_RETCODE SCIPaddNlRowGradientBenderscutOpt	(	SCIP *	masterprob,
		SCIP *	subproblem,
		SCIP_BENDERS *	benders,
		SCIP_NLROW *	nlrow,
		SCIP_Real	mult,
		SCIP_Real *	primalvals,
		SCIP_HASHMAP *	var2idx,
		SCIP_Real *	dirderiv,
		SCIP_VAR ***	vars,
		SCIP_Real **	vals,
		int *	nvars,
		int *	varssize
	)

adds the gradient of a nonlinear row in the current NLP solution of a subproblem to a linear row or constraint in the master problem

Only computes gradient w.r.t. master problem variables. Computes also the directional derivative, that is, mult times gradient times solution.

Parameters

masterprob	the SCIP instance of the master problem
subproblem	the SCIP instance of the subproblem
benders	the benders' decomposition structure
nlrow	nonlinear row
mult	multiplier
primalvals	the primal solutions for the NLP, can be NULL
var2idx	mapping from variable of the subproblem to the index in the dual arrays, can be NULL
dirderiv	storage to add directional derivative
vars	pointer to array of variables in the generated cut with non-zero coefficient
vals	pointer to array of coefficients of the variables in the generated cut
nvars	the number of variables in the cut
varssize	the number of variables in the array

Definition at line 1166 of file benderscut_opt.c.

References addVariableToArray(), FALSE, getNlpVarSol(), NULL, SCIP_CALL, SCIP_EXPRITER_DFS, SCIP_INVALID, SCIP_OKAY, SCIP_Real, SCIPcreateExpriter(), SCIPcreateNLPSol(), SCIPcreateSol(), SCIPevalExprGradient(), SCIPexprGetDerivative(), SCIPexpriterGetNext(), SCIPexpriterInit(), SCIPexpriterIsEnd(), SCIPfreeExpriter(), SCIPfreeSol(), SCIPgetBendersMasterVar(), SCIPgetVarExprVar(), SCIPhashmapEntryGetImageInt(), SCIPhashmapEntryGetOrigin(), SCIPhashmapGetEntry(), SCIPhashmapGetNEntries(), SCIPisExprVar(), SCIPnlrowGetExpr(), SCIPnlrowGetLinearCoefs(), SCIPnlrowGetLinearVars(), SCIPnlrowGetNLinearVars(), and SCIPsetSolVal().

Referenced by computeStandardNLPFeasibilityCut(), and computeStandardNLPOptimalityCut().