/** creates the zeroobj primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurZeroobj(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
heur = NULL;
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecZeroobj, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyZeroobj) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeZeroobj) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitZeroobj) );
/* add zeroobj primal heuristic parameters */
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/maxnodes",
"maximum number of nodes to regard in the subproblem",
&heurdata->maxnodes, TRUE,DEFAULT_MAXNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/nodesofs",
"number of nodes added to the contingent of the total nodes",
&heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/minnodes",
"minimum number of nodes required to start the subproblem",
&heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/maxlpiters",
"maximum number of LP iterations to be performed in the subproblem",
&heurdata->maxlpiters, TRUE, DEFAULT_MAXLPITERS, -1LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/nodesquot",
"contingent of sub problem nodes in relation to the number of nodes of the original problem",
&heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minimprove",
"factor by which zeroobj should at least improve the incumbent",
&heurdata->minimprove, TRUE, DEFAULT_MINIMPROVE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/addallsols",
"should all subproblem solutions be added to the original SCIP?",
&heurdata->addallsols, TRUE, DEFAULT_ADDALLSOLS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/onlywithoutsol",
"should heuristic only be executed if no primal solution was found, yet?",
&heurdata->onlywithoutsol, TRUE, DEFAULT_ONLYWITHOUTSOL, NULL, NULL) );
return SCIP_OKAY;
}
/** creates RENS primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurGcgrens(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
/* create heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeur(scip, HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP,
heurCopyGcgrens,
heurFreeGcgrens, heurInitGcgrens, heurExitGcgrens,
heurInitsolGcgrens, heurExitsolGcgrens, heurExecGcgrens,
heurdata) );
/* add rens primal heuristic parameters */
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minfixingrate",
"minimum percentage of integer variables that have to be fixable",
&heurdata->minfixingrate, FALSE, DEFAULT_MINFIXINGRATE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/maxnodes",
"maximum number of nodes to regard in the subproblem",
&heurdata->maxnodes, TRUE,DEFAULT_MAXNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/nodesofs",
"number of nodes added to the contingent of the total nodes",
&heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/minnodes",
"minimum number of nodes required to start the subproblem",
&heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/nodesquot",
"contingent of sub problem nodes in relation to the number of nodes of the original problem",
&heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minimprove",
"factor by which RENS should at least improve the incumbent",
&heurdata->minimprove, TRUE, DEFAULT_MINIMPROVE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/binarybounds",
"should general integers get binary bounds [floor(.),ceil(.)] ?",
&heurdata->binarybounds, TRUE, DEFAULT_BINARYBOUNDS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/uselprows",
"should subproblem be created out of the rows in the LP rows?",
&heurdata->uselprows, TRUE, DEFAULT_USELPROWS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/copycuts",
"if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?",
&heurdata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the pseudo cost branching rule and includes it in SCIP */
SCIP_RETCODE SCIPincludeBranchrulePscost(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_BRANCHRULEDATA* branchruledata;
SCIP_BRANCHRULE* branchrule;
/* create pscost branching rule data */
SCIP_CALL( SCIPallocMemory(scip, &branchruledata) );
/* include allfullstrong branching rule */
SCIP_CALL( SCIPincludeBranchruleBasic(scip, &branchrule, BRANCHRULE_NAME, BRANCHRULE_DESC, BRANCHRULE_PRIORITY,
BRANCHRULE_MAXDEPTH, BRANCHRULE_MAXBOUNDDIST, branchruledata) );
assert(branchrule != NULL);
/* set non-fundamental callbacks via specific setter functions*/
SCIP_CALL( SCIPsetBranchruleCopy(scip, branchrule, branchCopyPscost) );
SCIP_CALL( SCIPsetBranchruleFree(scip, branchrule, branchFreePscost) );
SCIP_CALL( SCIPsetBranchruleExecLp(scip, branchrule, branchExeclpPscost) );
SCIP_CALL( SCIPsetBranchruleExecExt(scip, branchrule, branchExecextPscost) );
SCIP_CALL( SCIPaddCharParam(scip, "branching/"BRANCHRULE_NAME"/strategy",
"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)",
&branchruledata->strategy, FALSE, BRANCHRULE_STRATEGY_DEFAULT, BRANCHRULE_STRATEGIES, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "branching/"BRANCHRULE_NAME"/minscoreweight",
"weight for minimum of scores of a branching candidate when building weighted sum of min/max/sum of scores",
&branchruledata->scoreminweight, TRUE, BRANCHRULE_SCOREMINWEIGHT_DEFAULT, -SCIPinfinity(scip), SCIPinfinity(scip), NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "branching/"BRANCHRULE_NAME"/maxscoreweight",
"weight for maximum of scores of a branching candidate when building weighted sum of min/max/sum of scores",
&branchruledata->scoremaxweight, TRUE, BRANCHRULE_SCOREMAXWEIGHT_DEFAULT, -SCIPinfinity(scip), SCIPinfinity(scip), NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "branching/"BRANCHRULE_NAME"/sumscoreweight",
"weight for sum of scores of a branching candidate when building weighted sum of min/max/sum of scores",
&branchruledata->scoresumweight, TRUE, BRANCHRULE_SCORESUMWEIGHT_DEFAULT, -SCIPinfinity(scip), SCIPinfinity(scip), NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "branching/"BRANCHRULE_NAME"/nchildren",
"number of children to create in n-ary branching",
&branchruledata->nchildren, FALSE, BRANCHRULE_NCHILDREN_DEFAULT, 2, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "branching/"BRANCHRULE_NAME"/narymaxdepth",
"maximal depth where to do n-ary branching, -1 to turn off",
&branchruledata->narymaxdepth, FALSE, BRANCHRULE_NARYMAXDEPTH_DEFAULT, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "branching/"BRANCHRULE_NAME"/naryminwidth",
"minimal domain width in children when doing n-ary branching, relative to global bounds",
&branchruledata->naryminwidth, FALSE, BRANCHRULE_NARYMINWIDTH_DEFAULT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "branching/"BRANCHRULE_NAME"/narywidthfactor",
"factor of domain width in n-ary branching when creating nodes with increasing distance from branching value",
&branchruledata->narywidthfactor, FALSE, BRANCHRULE_NARYWIDTHFAC_DEFAULT, 1.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the objpscostdiving heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurObjpscostdiving(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create Objpscostdiving primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecObjpscostdiving, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyObjpscostdiving) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeObjpscostdiving) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitObjpscostdiving) );
SCIP_CALL( SCIPsetHeurExit(scip, heur, heurExitObjpscostdiving) );
/* objpscostdiving heuristic parameters */
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/objpscostdiving/minreldepth",
"minimal relative depth to start diving",
&heurdata->minreldepth, TRUE, DEFAULT_MINRELDEPTH, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/objpscostdiving/maxreldepth",
"maximal relative depth to start diving",
&heurdata->maxreldepth, TRUE, DEFAULT_MAXRELDEPTH, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/objpscostdiving/maxlpiterquot",
"maximal fraction of diving LP iterations compared to total iteration number",
&heurdata->maxlpiterquot, FALSE, DEFAULT_MAXLPITERQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"heuristics/objpscostdiving/maxlpiterofs",
"additional number of allowed LP iterations",
&heurdata->maxlpiterofs, FALSE, DEFAULT_MAXLPITEROFS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"heuristics/objpscostdiving/maxsols",
"total number of feasible solutions found up to which heuristic is called (-1: no limit)",
&heurdata->maxsols, TRUE, DEFAULT_MAXSOLS, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/objpscostdiving/depthfac",
"maximal diving depth: number of binary/integer variables times depthfac",
&heurdata->depthfac, TRUE, DEFAULT_DEPTHFAC, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/objpscostdiving/depthfacnosol",
"maximal diving depth factor if no feasible solution was found yet",
&heurdata->depthfacnosol, TRUE, DEFAULT_DEPTHFACNOSOL, 0.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the guideddiving heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurGuideddiving(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create Guideddiving primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecGuideddiving, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyGuideddiving) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeGuideddiving) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitGuideddiving) );
SCIP_CALL( SCIPsetHeurExit(scip, heur, heurExitGuideddiving) );
/* guideddiving heuristic parameters */
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/guideddiving/minreldepth",
"minimal relative depth to start diving",
&heurdata->minreldepth, TRUE, DEFAULT_MINRELDEPTH, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/guideddiving/maxreldepth",
"maximal relative depth to start diving",
&heurdata->maxreldepth, TRUE, DEFAULT_MAXRELDEPTH, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/guideddiving/maxlpiterquot",
"maximal fraction of diving LP iterations compared to node LP iterations",
&heurdata->maxlpiterquot, FALSE, DEFAULT_MAXLPITERQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"heuristics/guideddiving/maxlpiterofs",
"additional number of allowed LP iterations",
&heurdata->maxlpiterofs, FALSE, DEFAULT_MAXLPITEROFS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/guideddiving/maxdiveubquot",
"maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where diving is performed (0.0: no limit)",
&heurdata->maxdiveubquot, TRUE, DEFAULT_MAXDIVEUBQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"heuristics/guideddiving/maxdiveavgquot",
"maximal quotient (curlowerbound - lowerbound)/(avglowerbound - lowerbound) where diving is performed (0.0: no limit)",
&heurdata->maxdiveavgquot, TRUE, DEFAULT_MAXDIVEAVGQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"heuristics/guideddiving/backtrack",
"use one level of backtracking if infeasibility is encountered?",
&heurdata->backtrack, FALSE, DEFAULT_BACKTRACK, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the inference history branching rule and includes it in SCIP */
SCIP_RETCODE SCIPincludeBranchruleInference(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_BRANCHRULEDATA* branchruledata;
SCIP_BRANCHRULE* branchrule;
/* create inference branching rule data */
SCIP_CALL( SCIPallocMemory(scip, &branchruledata) );
/* include branching rule */
SCIP_CALL( SCIPincludeBranchruleBasic(scip, &branchrule, BRANCHRULE_NAME, BRANCHRULE_DESC, BRANCHRULE_PRIORITY,
BRANCHRULE_MAXDEPTH, BRANCHRULE_MAXBOUNDDIST, branchruledata) );
assert(branchrule != NULL);
/* set non-fundamental callbacks via specific setter functions*/
SCIP_CALL( SCIPsetBranchruleCopy(scip, branchrule, branchCopyInference) );
SCIP_CALL( SCIPsetBranchruleFree(scip, branchrule, branchFreeInference) );
SCIP_CALL( SCIPsetBranchruleExecLp(scip, branchrule, branchExeclpInference) );
SCIP_CALL( SCIPsetBranchruleExecExt(scip, branchrule, branchExecextInference) );
SCIP_CALL( SCIPsetBranchruleExecPs(scip, branchrule, branchExecpsInference) );
/* inference branching rule parameters */
SCIP_CALL( SCIPaddRealParam(scip,
"branching/inference/conflictweight",
"weight in score calculations for conflict score",
&branchruledata->conflictweight, TRUE, DEFAULT_CONFLICTWEIGHT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"branching/inference/inferenceweight",
"weight in score calculations for inference score",
&branchruledata->inferenceweight, TRUE, DEFAULT_INFERENCEWEIGHT, SCIP_REAL_MIN, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"branching/inference/cutoffweight",
"weight in score calculations for cutoff score",
&branchruledata->cutoffweight, TRUE, DEFAULT_CUTOFFWEIGHT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"branching/inference/fractionals",
"should branching on LP solution be restricted to the fractional variables?",
&branchruledata->fractionals, TRUE, DEFAULT_FRACTIONALS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"branching/inference/useweightedsum",
"should a weighted sum of inference, conflict and cutoff weights be used?",
&branchruledata->useweightedsum, FALSE, DEFAULT_USEWEIGHTEDSUM, NULL, NULL) );
/* inference branching rule parameters */
SCIP_CALL( SCIPaddRealParam(scip,
"branching/inference/reliablescore",
"weight in score calculations for conflict score",
&branchruledata->reliablescore, TRUE, DEFAULT_RELIABLESCORE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** includes event handler for best solution found */
SCIP_RETCODE SCIPincludeEventHdlrSofttimelimit(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_EVENTHDLRDATA* eventhdlrdata;
SCIP_EVENTHDLR* eventhdlr;
SCIP_CALL( SCIPallocMemory(scip, &eventhdlrdata) );
eventhdlrdata->filterpos = -1;
eventhdlr = NULL;
/* create event handler for events on watched variables */
SCIP_CALL( SCIPincludeEventhdlrBasic(scip, &eventhdlr, EVENTHDLR_NAME, EVENTHDLR_DESC, eventExecSofttimelimit, eventhdlrdata) );
assert(eventhdlr != NULL);
SCIP_CALL( SCIPsetEventhdlrCopy(scip, eventhdlr, eventCopySofttimelimit) );
SCIP_CALL( SCIPsetEventhdlrFree(scip, eventhdlr, eventFreeSofttimelimit) );
SCIP_CALL( SCIPsetEventhdlrInit(scip, eventhdlr, eventInitSofttimelimit) );
SCIP_CALL( SCIPsetEventhdlrExit(scip, eventhdlr, eventExitSofttimelimit) );
SCIP_CALL( SCIPaddRealParam(scip, "limits/softtime",
"soft time limit which should be applied after first solution was found",
&eventhdlrdata->softtimelimit, FALSE, -1.0, -1.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the node selector for best first search and includes it in SCIP */
SCIP_RETCODE SCIPincludeNodeselBfs(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_NODESELDATA* nodeseldata;
/* allocate and initialize node selector data; this has to be freed in the destructor */
SCIP_CALL( SCIPallocMemory(scip, &nodeseldata) );
/* include node selector */
SCIP_CALL( SCIPincludeNodesel(scip, NODESEL_NAME, NODESEL_DESC, NODESEL_STDPRIORITY, NODESEL_MEMSAVEPRIORITY,
nodeselCopyBfs,
nodeselFreeBfs, nodeselInitBfs, nodeselExitBfs,
nodeselInitsolBfs, nodeselExitsolBfs, nodeselSelectBfs, nodeselCompBfs,
nodeseldata) );
/* add node selector parameters */
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/bfs/minplungedepth",
"minimal plunging depth, before new best node may be selected (-1 for dynamic setting)",
&nodeseldata->minplungedepth, TRUE, MINPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/bfs/maxplungedepth",
"maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)",
&nodeseldata->maxplungedepth, TRUE, MAXPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"nodeselection/bfs/maxplungequot",
"maximal quotient (curlowerbound - lowerbound)/(cutoffbound - lowerbound) where plunging is performed",
&nodeseldata->maxplungequot, TRUE, MAXPLUNGEQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the node selector for hybrid best estimate / best bound search and includes it in SCIP */
SCIP_RETCODE SCIPincludeNodeselHybridestim(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_NODESELDATA* nodeseldata;
SCIP_NODESEL* nodesel;
/* allocate and initialize node selector data; this has to be freed in the destructor */
SCIP_CALL( SCIPallocMemory(scip, &nodeseldata) );
/* include node selector */
SCIP_CALL( SCIPincludeNodeselBasic(scip, &nodesel, NODESEL_NAME, NODESEL_DESC, NODESEL_STDPRIORITY, NODESEL_MEMSAVEPRIORITY,
nodeselSelectHybridestim, nodeselCompHybridestim, nodeseldata) );
assert(nodesel != NULL);
SCIP_CALL( SCIPsetNodeselCopy(scip, nodesel, nodeselCopyHybridestim) );
SCIP_CALL( SCIPsetNodeselFree(scip, nodesel, nodeselFreeHybridestim) );
/* add node selector parameters */
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/hybridestim/minplungedepth",
"minimal plunging depth, before new best node may be selected (-1 for dynamic setting)",
&nodeseldata->minplungedepth, TRUE, MINPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/hybridestim/maxplungedepth",
"maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)",
&nodeseldata->maxplungedepth, TRUE, MAXPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"nodeselection/hybridestim/maxplungequot",
"maximal quotient (estimate - lowerbound)/(cutoffbound - lowerbound) where plunging is performed",
&nodeseldata->maxplungequot, TRUE, MAXPLUNGEQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/hybridestim/bestnodefreq",
"frequency at which the best node instead of the hybrid best estimate / best bound is selected (0: never)",
&nodeseldata->bestnodefreq, FALSE, BESTNODEFREQ, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"nodeselection/hybridestim/estimweight",
"weight of estimate value in node selection score (0: pure best bound search, 1: pure best estimate search)",
&nodeseldata->estimweight, TRUE, ESTIMWEIGHT, 0.0, 1.0, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the Strong CG cut separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaStrongcg(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_SEPADATA* sepadata;
SCIP_SEPA* sepa;
/* create separator data */
SCIP_CALL( SCIPallocMemory(scip, &sepadata) );
sepadata->lastncutsfound = 0;
/* include separator */
SCIP_CALL( SCIPincludeSepaBasic(scip, &sepa, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST,
SEPA_USESSUBSCIP, SEPA_DELAY,
sepaExeclpStrongcg, NULL,
sepadata) );
assert(sepa != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetSepaCopy(scip, sepa, sepaCopyStrongcg) );
SCIP_CALL( SCIPsetSepaFree(scip, sepa, sepaFreeStrongcg) );
/* add separator parameters */
SCIP_CALL( SCIPaddIntParam(scip,
"separating/strongcg/maxrounds",
"maximal number of strong CG separation rounds per node (-1: unlimited)",
&sepadata->maxrounds, FALSE, DEFAULT_MAXROUNDS, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/strongcg/maxroundsroot",
"maximal number of strong CG separation rounds in the root node (-1: unlimited)",
&sepadata->maxroundsroot, FALSE, DEFAULT_MAXROUNDSROOT, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/strongcg/maxsepacuts",
"maximal number of strong CG cuts separated per separation round",
&sepadata->maxsepacuts, FALSE, DEFAULT_MAXSEPACUTS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/strongcg/maxsepacutsroot",
"maximal number of strong CG cuts separated per separation round in the root node",
&sepadata->maxsepacutsroot, FALSE, DEFAULT_MAXSEPACUTSROOT, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"separating/strongcg/maxweightrange",
"maximal valid range max(|weights|)/min(|weights|) of row weights",
&sepadata->maxweightrange, TRUE, DEFAULT_MAXWEIGHTRANGE, 1.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/strongcg/dynamiccuts",
"should generated cuts be removed from the LP if they are no longer tight?",
&sepadata->dynamiccuts, FALSE, DEFAULT_DYNAMICCUTS, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the node selector for best estimate search and includes it in SCIP */
SCIP_RETCODE SCIPincludeNodeselEstimate(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_NODESELDATA* nodeseldata;
SCIP_NODESEL* nodesel;
/* allocate and initialize node selector data; this has to be freed in the destructor */
SCIP_CALL( SCIPallocMemory(scip, &nodeseldata) );
/* include node selector */
SCIP_CALL( SCIPincludeNodeselBasic(scip, &nodesel, NODESEL_NAME, NODESEL_DESC, NODESEL_STDPRIORITY, NODESEL_MEMSAVEPRIORITY,
nodeselSelectEstimate, nodeselCompEstimate, nodeseldata) );
assert(nodesel != NULL);
SCIP_CALL( SCIPsetNodeselCopy(scip, nodesel, nodeselCopyEstimate) );
SCIP_CALL( SCIPsetNodeselFree(scip, nodesel, nodeselFreeEstimate) );
/* add node selector parameters */
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/estimate/minplungedepth",
"minimal plunging depth, before new best node may be selected (-1 for dynamic setting)",
&nodeseldata->minplungedepth, TRUE, DEFAULT_MINPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/estimate/maxplungedepth",
"maximal plunging depth, before new best node is forced to be selected (-1 for dynamic setting)",
&nodeseldata->maxplungedepth, TRUE, DEFAULT_MAXPLUNGEDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"nodeselection/estimate/maxplungequot",
"maximal quotient (estimate - lowerbound)/(cutoffbound - lowerbound) where plunging is performed",
&nodeseldata->maxplungequot, TRUE, DEFAULT_MAXPLUNGEQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/estimate/bestnodefreq",
"frequency at which the best node instead of the best estimate is selected (0: never)",
&nodeseldata->bestnodefreq, FALSE, DEFAULT_BESTNODEFREQ, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"nodeselection/estimate/breadthfirstdepth",
"depth until breadth-fisrt search is applied",
&nodeseldata->breadthfirstdepth, FALSE, DEFAULT_BREADTHFIRSTDEPTH, -1, INT_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the zirounding primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurZirounding(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create zirounding primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecZirounding, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyZirounding) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeZirounding) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitZirounding) );
SCIP_CALL( SCIPsetHeurExit(scip, heur, heurExitZirounding) );
SCIP_CALL( SCIPsetHeurInitsol(scip, heur, heurInitsolZirounding) );
/* add zirounding primal heuristic parameters */
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/zirounding/maxroundingloops",
"determines maximum number of rounding loops",
&heurdata->maxroundingloops, TRUE, DEFAULT_MAXROUNDINGLOOPS, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/zirounding/stopziround",
"flag to determine if Zirounding is deactivated after a certain percentage of unsuccessful calls",
&heurdata->stopziround, TRUE, DEFAULT_STOPZIROUND, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,"heuristics/zirounding/stoppercentage",
"if percentage of found solutions falls below this parameter, Zirounding will be deactivated",
&heurdata->stoppercentage, TRUE, DEFAULT_STOPPERCENTAGE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/zirounding/minstopncalls",
"determines the minimum number of calls before percentage-based deactivation of Zirounding is applied",
&heurdata->minstopncalls, TRUE, DEFAULT_MINSTOPNCALLS, 1, INT_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the closecuts separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaClosecuts(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_SEPADATA* sepadata;
/* create closecuts separator data */
SCIP_CALL( SCIPallocMemory(scip, &sepadata) );
sepadata->sepasol = NULL;
sepadata->discardnode = -1;
sepadata->nunsuccessful = 0;
/* include separator */
SCIP_CALL( SCIPincludeSepa(scip, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST, SEPA_USESSUBSCIP, SEPA_DELAY,
sepaCopyClosecuts, sepaFreeClosecuts, sepaInitClosecuts, sepaExitClosecuts,
sepaInitsolClosecuts, sepaExitsolClosecuts, sepaExeclpClosecuts, sepaExecsolClosecuts,
sepadata) );
/* add closecuts separator parameters */
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/closecuts/separelint",
"generate close cuts w.r.t. relative interior point (best solution otherwise)?",
&sepadata->separelint, TRUE, SCIP_DEFAULT_SEPARELINT, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"separating/closecuts/sepacombvalue",
"convex combination value for close cuts",
&sepadata->sepacombvalue, TRUE, SCIP_DEFAULT_SEPACOMBVALUE, 0.0, 1.0,
NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/closecuts/separootonly",
"generate close cuts in the root only?",
&sepadata->separootonly, TRUE, SCIP_DEFAULT_SEPAROOTONLY, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/closecuts/closethres",
"threshold on number of generated cuts below which the ordinary separation is started",
&sepadata->sepathreshold, TRUE, SCIP_DEFAULT_SEPATHRESHOLD, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/closecuts/inclobjcutoff",
"include an objective cutoff when computing the relative interior?",
&sepadata->inclobjcutoff, TRUE, SCIP_DEFAULT_INCLOBJCUTOFF, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/closecuts/recomputerelint",
"recompute relative interior point in each separation call?",
&sepadata->recomputerelint, TRUE, SCIP_DEFAULT_RECOMPUTERELINT, NULL, NULL) );
SCIP_CALL( SCIPaddCharParam(scip,
"separating/closecuts/relintnormtype",
"type of norm to use when computing relative interior: 'o'ne norm, 's'upremum norm",
&sepadata->relintnormtype, TRUE, SCIP_DEFAULT_RELINTNORMTYPE, "os", NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/closecuts/maxunsuccessful",
"turn off separation in current node after unsuccessful calls (-1 never turn off)",
&sepadata->maxunsuccessful, TRUE, SCIP_DEFAULT_MAXUNSUCCESSFUL, -1, INT_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the crossover primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurCrossover(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create Crossover primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecCrossover, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyCrossover) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeCrossover) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitCrossover) );
SCIP_CALL( SCIPsetHeurExit(scip, heur, heurExitCrossover) );
/* add crossover primal heuristic parameters */
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/nodesofs",
"number of nodes added to the contingent of the total nodes",
&heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/maxnodes",
"maximum number of nodes to regard in the subproblem",
&heurdata->maxnodes, TRUE, DEFAULT_MAXNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/minnodes",
"minimum number of nodes required to start the subproblem",
&heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/nusedsols",
"number of solutions to be taken into account",
&heurdata->nusedsols, FALSE, DEFAULT_NUSEDSOLS, 2, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddLongintParam(scip, "heuristics/"HEUR_NAME"/nwaitingnodes",
"number of nodes without incumbent change that heuristic should wait",
&heurdata->nwaitingnodes, TRUE, DEFAULT_NWAITINGNODES, 0LL, SCIP_LONGINT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/nodesquot",
"contingent of sub problem nodes in relation to the number of nodes of the original problem",
&heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minfixingrate",
"minimum percentage of integer variables that have to be fixed",
&heurdata->minfixingrate, FALSE, DEFAULT_MINFIXINGRATE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minimprove",
"factor by which Crossover should at least improve the incumbent",
&heurdata->minimprove, TRUE, DEFAULT_MINIMPROVE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/randomization",
"should the choice which sols to take be randomized?",
&heurdata->randomization, TRUE, DEFAULT_RANDOMIZATION, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/dontwaitatroot",
"should the nwaitingnodes parameter be ignored at the root node?",
&heurdata->dontwaitatroot, TRUE, DEFAULT_DONTWAITATROOT, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/uselprows",
"should subproblem be created out of the rows in the LP rows?",
&heurdata->uselprows, TRUE, DEFAULT_USELPROWS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/copycuts",
"if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?",
&heurdata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/permute",
"should the subproblem be permuted to increase diversification?",
&heurdata->permute, TRUE, DEFAULT_PERMUTE, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the Gomory MIR cut separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaGomory(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_SEPADATA* sepadata;
SCIP_SEPA* sepa;
/* create separator data */
SCIP_CALL( SCIPallocMemory(scip, &sepadata) );
sepadata->lastncutsfound = 0;
/* include separator */
SCIP_CALL( SCIPincludeSepaBasic(scip, &sepa, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST,
SEPA_USESSUBSCIP, SEPA_DELAY,
sepaExeclpGomory, NULL,
sepadata) );
assert(sepa != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetSepaCopy(scip, sepa, sepaCopyGomory) );
SCIP_CALL( SCIPsetSepaFree(scip, sepa, sepaFreeGomory) );
/* add separator parameters */
SCIP_CALL( SCIPaddIntParam(scip,
"separating/gomory/maxrounds",
"maximal number of gomory separation rounds per node (-1: unlimited)",
&sepadata->maxrounds, FALSE, DEFAULT_MAXROUNDS, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/gomory/maxroundsroot",
"maximal number of gomory separation rounds in the root node (-1: unlimited)",
&sepadata->maxroundsroot, FALSE, DEFAULT_MAXROUNDSROOT, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/gomory/maxsepacuts",
"maximal number of gomory cuts separated per separation round",
&sepadata->maxsepacuts, FALSE, DEFAULT_MAXSEPACUTS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip,
"separating/gomory/maxsepacutsroot",
"maximal number of gomory cuts separated per separation round in the root node",
&sepadata->maxsepacutsroot, FALSE, DEFAULT_MAXSEPACUTSROOT, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"separating/gomory/away",
"minimal integrality violation of a basis variable in order to try Gomory cut",
&sepadata->away, FALSE, DEFAULT_AWAY, 0.0, 0.5, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip,
"separating/gomory/maxweightrange",
"maximal valid range max(|weights|)/min(|weights|) of row weights",
&sepadata->maxweightrange, TRUE, DEFAULT_MAXWEIGHTRANGE, 1.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/gomory/dynamiccuts",
"should generated cuts be removed from the LP if they are no longer tight?",
&sepadata->dynamiccuts, FALSE, DEFAULT_DYNAMICCUTS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/gomory/makeintegral",
"try to scale cuts to integral coefficients",
&sepadata->makeintegral, TRUE, DEFAULT_MAKEINTEGRAL, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/gomory/forcecuts",
"if conversion to integral coefficients failed still use the cut",
&sepadata->forcecuts, TRUE, DEFAULT_FORCECUTS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/gomory/separaterows",
"separate rows with integral slack",
&sepadata->separaterows, TRUE, DEFAULT_SEPARATEROWS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip,
"separating/gomory/delayedcuts",
"should cuts be added to the delayed cut pool?",
&sepadata->delayedcuts, TRUE, DEFAULT_DELAYEDCUTS, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the localbranching primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurLocalbranching(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create Localbranching primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecLocalbranching, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyLocalbranching) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeLocalbranching) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitLocalbranching) );
/* add localbranching primal heuristic parameters */
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/nodesofs",
"number of nodes added to the contingent of the total nodes",
&heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/neighborhoodsize",
"radius (using Manhattan metric) of the incumbent's neighborhood to be searched",
&heurdata->neighborhoodsize, FALSE, DEFAULT_NEIGHBORHOODSIZE, 1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/nodesquot",
"contingent of sub problem nodes in relation to the number of nodes of the original problem",
&heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/lplimfac",
"factor by which the limit on the number of LP depends on the node limit",
&heurdata->lplimfac, TRUE, DEFAULT_LPLIMFAC, 1.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/minnodes",
"minimum number of nodes required to start the subproblem",
&heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/maxnodes",
"maximum number of nodes to regard in the subproblem",
&heurdata->maxnodes, TRUE, DEFAULT_MAXNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/nwaitingnodes",
"number of nodes without incumbent change that heuristic should wait",
&heurdata->nwaitingnodes, TRUE, DEFAULT_NWAITINGNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minimprove",
"factor by which localbranching should at least improve the incumbent",
&heurdata->minimprove, TRUE, DEFAULT_MINIMPROVE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/uselprows",
"should subproblem be created out of the rows in the LP rows?",
&heurdata->uselprows, TRUE, DEFAULT_USELPROWS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/copycuts",
"if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?",
&heurdata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the disjunctive cut separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaDisjunctive(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_SEPADATA* sepadata = NULL;
SCIP_SEPA* sepa = NULL;
/* create separator data */
SCIP_CALL( SCIPallocMemory(scip, &sepadata) );
sepadata->conshdlr = NULL;
sepadata->lastncutsfound = 0;
/* include separator */
SCIP_CALL( SCIPincludeSepaBasic(scip, &sepa, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST,
SEPA_USESSUBSCIP, SEPA_DELAY, sepaExeclpDisjunctive, NULL, sepadata) );
assert( sepa != NULL );
/* set non fundamental callbacks via setter functions */
SCIP_CALL( SCIPsetSepaCopy(scip, sepa, sepaCopyDisjunctive) );
SCIP_CALL( SCIPsetSepaFree(scip, sepa, sepaFreeDisjunctive) );
SCIP_CALL( SCIPsetSepaInitsol(scip, sepa, sepaInitsolDisjunctive) );
/* add separator parameters */
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/strengthen",
"strengthen cut if integer variables are present.",
&sepadata->strengthen, TRUE, DEFAULT_STRENGTHEN, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxdepth",
"node depth of separating bipartite disjunctive cuts (-1: no limit)",
&sepadata->maxdepth, TRUE, DEFAULT_MAXDEPTH, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxrounds",
"maximal number of separation rounds per iteration in a branching node (-1: no limit)",
&sepadata->maxrounds, TRUE, DEFAULT_MAXROUNDS, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxroundsroot",
"maximal number of separation rounds in the root node (-1: no limit)",
&sepadata->maxroundsroot, TRUE, DEFAULT_MAXROUNDSROOT, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxinvcuts",
"maximal number of cuts investigated per iteration in a branching node",
&sepadata->maxinvcuts, TRUE, DEFAULT_MAXINVCUTS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxinvcutsroot",
"maximal number of cuts investigated per iteration in the root node",
&sepadata->maxinvcutsroot, TRUE, DEFAULT_MAXINVCUTSROOT, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxconfsdelay",
"delay separation if number of conflict graph edges is larger than predefined value (-1: no limit)",
&sepadata->maxconfsdelay, TRUE, DEFAULT_MAXCONFSDELAY, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxrank",
"maximal rank of a disj. cut that could not be scaled to integral coefficients (-1: unlimited)",
&sepadata->maxrank, FALSE, DEFAULT_MAXRANK, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/" SEPA_NAME "/maxrankintegral",
"maximal rank of a disj. cut that could be scaled to integral coefficients (-1: unlimited)",
&sepadata->maxrankintegral, FALSE, DEFAULT_MAXRANKINTEGRAL, -1, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "separating/" SEPA_NAME "/maxweightrange",
"maximal valid range max(|weights|)/min(|weights|) of row weights",
&sepadata->maxweightrange, TRUE, DEFAULT_MAXWEIGHTRANGE, 1.0, SCIP_REAL_MAX, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the mutation primal heuristic and includes it in SCIP */
SCIP_RETCODE SCIPincludeHeurMutation(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_HEURDATA* heurdata;
SCIP_HEUR* heur;
/* create Mutation primal heuristic data */
SCIP_CALL( SCIPallocMemory(scip, &heurdata) );
/* include primal heuristic */
SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecMutation, heurdata) );
assert(heur != NULL);
/* set non-NULL pointers to callback methods */
SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyMutation) );
SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeMutation) );
SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitMutation) );
/* add mutation primal heuristic parameters */
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/nodesofs",
"number of nodes added to the contingent of the total nodes",
&heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/maxnodes",
"maximum number of nodes to regard in the subproblem",
&heurdata->maxnodes, TRUE, DEFAULT_MAXNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/minnodes",
"minimum number of nodes required to start the subproblem",
&heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "heuristics/"HEUR_NAME"/nwaitingnodes",
"number of nodes without incumbent change that heuristic should wait",
&heurdata->nwaitingnodes, TRUE, DEFAULT_NWAITINGNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/nodesquot",
"contingent of sub problem nodes in relation to the number of nodes of the original problem",
&heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minfixingrate",
"percentage of integer variables that have to be fixed",
&heurdata->minfixingrate, FALSE, DEFAULT_MINFIXINGRATE, SCIPsumepsilon(scip), 1.0-SCIPsumepsilon(scip), NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "heuristics/"HEUR_NAME"/minimprove",
"factor by which "HEUR_NAME" should at least improve the incumbent",
&heurdata->minimprove, TRUE, DEFAULT_MINIMPROVE, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/uselprows",
"should subproblem be created out of the rows in the LP rows?",
&heurdata->uselprows, TRUE, DEFAULT_USELPROWS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/"HEUR_NAME"/copycuts",
"if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?",
&heurdata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
return SCIP_OKAY;
}
/** creates the rapidlearning separator and includes it in SCIP */
SCIP_RETCODE SCIPincludeSepaRapidlearning(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_SEPADATA* sepadata;
/* create rapidlearning separator data */
SCIP_CALL( SCIPallocMemory(scip, &sepadata) );
/* include separator */
SCIP_CALL( SCIPincludeSepa(scip, SEPA_NAME, SEPA_DESC, SEPA_PRIORITY, SEPA_FREQ, SEPA_MAXBOUNDDIST,
SEPA_USESSUBSCIP, SEPA_DELAY,
sepaCopyRapidlearning, sepaFreeRapidlearning, sepaInitRapidlearning, sepaExitRapidlearning,
sepaInitsolRapidlearning, sepaExitsolRapidlearning,
sepaExeclpRapidlearning, sepaExecsolRapidlearning,
sepadata) );
/* add rapidlearning separator parameters */
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/applyconflicts",
"should the found conflicts be applied in the original SCIP?",
&sepadata->applyconflicts, TRUE, DEFAULT_APPLYCONFLICTS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/applybdchgs",
"should the found global bound deductions be applied in the original SCIP?",
&sepadata->applybdchgs, TRUE, DEFAULT_APPLYBDCHGS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/applyinfervals",
"should the inference values be used as initialization in the original SCIP?",
&sepadata->applyinfervals, TRUE, DEFAULT_APPLYINFERVALS, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/reducedinfer",
"should the inference values only be used when "SEPA_NAME" found other reductions?",
&sepadata->reducedinfer, TRUE, DEFAULT_REDUCEDINFER, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/applyprimalsol",
"should the incumbent solution be copied to the original SCIP?",
&sepadata->applyprimalsol, TRUE, DEFAULT_APPLYPRIMALSOL, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/applysolved",
"should a solved status be copied to the original SCIP?",
&sepadata->applysolved, TRUE, DEFAULT_APPLYSOLVED, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/contvars",
"should rapid learning be applied when there are continuous variables?",
&sepadata->contvars, TRUE, DEFAULT_CONTVARS, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "separating/"SEPA_NAME"/contvarsquot",
"maximal portion of continuous variables to apply rapid learning",
&sepadata->contvarsquot, TRUE, DEFAULT_CONTVARSQUOT, 0.0, 1.0, NULL, NULL) );
SCIP_CALL( SCIPaddRealParam(scip, "separating/"SEPA_NAME"/lpiterquot",
"maximal fraction of LP iterations compared to node LP iterations",
&sepadata->lpiterquot, TRUE, DEFAULT_LPITERQUOT, 0.0, SCIP_REAL_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/"SEPA_NAME"/maxnvars",
"maximum problem size (variables) for which rapid learning will be called",
&sepadata->maxnvars, TRUE, DEFAULT_MAXNVARS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/"SEPA_NAME"/maxnconss",
"maximum problem size (constraints) for which rapid learning will be called",
&sepadata->maxnconss, TRUE, DEFAULT_MAXNCONSS, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/"SEPA_NAME"/maxnodes",
"maximum number of nodes considered in rapid learning run",
&sepadata->maxnodes, TRUE, DEFAULT_MAXNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddIntParam(scip, "separating/"SEPA_NAME"/minnodes",
"minimum number of nodes considered in rapid learning run",
&sepadata->minnodes, TRUE, DEFAULT_MINNODES, 0, INT_MAX, NULL, NULL) );
SCIP_CALL( SCIPaddBoolParam(scip, "separating/"SEPA_NAME"/copycuts",
"should all active cuts from cutpool be copied to constraints in subproblem?",
&sepadata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
return SCIP_OKAY;
}
