/** adds coefficient to linear constraint (if it is not zero) */
JNIEXPORT
void JNISCIPCONSLINEAR(addCoefLinear)(
JNIEnv* env, /**< JNI environment variable */
jobject jobj, /**< JNI class pointer */
jlong jscip, /**< SCIP data structure */
jlong jcons, /**< constraint data */
jlong jvar, /**< variable of constraint entry */
jdouble val /**< coefficient of constraint entry */
)
{
SCIP* scip;
SCIP_CONS* cons;
SCIP_VAR* var;
/* convert JNI pointer into C pointer */
scip = (SCIP*) (size_t) jscip;
assert(scip != NULL);
/* convert JNI pointer into C pointer */
cons = (SCIP_CONS*) (size_t) jcons;
assert( cons != NULL);
/* convert JNI pointer into C pointer */
var = (SCIP_VAR*) (size_t) jvar;
assert(var != NULL);
JNISCIP_CALL( SCIPaddCoefLinear(scip, cons, var, (SCIP_Real) val) );
}
/** creates and captures a linear constraint
* in its most basic version, i. e., all constraint flags are set to their basic value as explained for the
* method SCIPcreateConsLinear(); all flags can be set via SCIPsetConsFLAGNAME-methods in scip.h
*
* @see SCIPcreateConsLinear() for information about the basic constraint flag configuration
*
* @note the constraint gets captured, hence at one point you have to release it using the method SCIPreleaseCons()
*/
JNIEXPORT
jlong JNISCIPCONSLINEAR(createConsBasicLinear)(
JNIEnv* env, /**< JNI environment variable */
jobject jobj, /**< JNI class pointer */
jlong jscip, /**< SCIP data structure */
jstring jname, /**< name of constraint */
jint jnvars, /**< number of nonzeros in the constraint */
jlongArray jvars, /**< array with variables of constraint entries */
jdoubleArray jvals, /**< array with coefficients of constraint entries */
jdouble jlhs, /**< left hand side of constraint */
jdouble jrhs /**< right hand side of constraint */
)
{
SCIP* scip;
SCIP_CONS* cons;
const char* name;
int nvars;
/* convert JNI pointer into C pointer */
scip = (SCIP*) (size_t) jscip;
assert(scip != NULL);
/* convert JNI string into C const char* */
name = (*env)->GetStringUTFChars(env, jname, NULL);
if( name == NULL )
SCIPABORT();
/* create linear constraint with zero variables */
JNISCIP_CALL( SCIPcreateConsBasicLinear(scip, &cons, name, 0, NULL, NULL, (SCIP_Real) jlhs, (SCIP_Real) jrhs) );
/* convert JNI integer into integer */
nvars = (int)jnvars;
if( nvars > 0 )
{
jlong* vars;
jdouble* vals;
int v;
JNISCIP_CALL( SCIPallocBufferArray(scip, &vars, nvars) );
JNISCIP_CALL( SCIPallocBufferArray(scip, &vals, nvars) );
(*env)->GetLongArrayRegion(env, jvars, 0, nvars, vars);
(*env)->GetDoubleArrayRegion(env, jvals, 0, nvars, vals);
for( v = 0; v < nvars; ++v )
{
JNISCIP_CALL( SCIPaddCoefLinear(scip, cons, (SCIP_VAR*)(size_t)vars[v], (SCIP_Real)vals[v]));
}
SCIPfreeBufferArray(scip, &vals);
SCIPfreeBufferArray(scip, &vars);
}
/* relase string object */
(*env)->ReleaseStringUTFChars(env, jname, name);
return (jlong)(size_t)cons;
}
/** creates and captures a linear constraint
*
* @note the constraint gets captured, hence at one point you have to release it using the method {@link releaseCons()}
*/
JNIEXPORT
jlong JNISCIPCONSLINEAR(createConsLinear)(
JNIEnv* env, /**< JNI environment variable */
jobject jobj, /**< JNI class pointer */
jlong jscip, /**< SCIP data structure */
jstring jname, /**< name of constraint */
jint jnvars, /**< number of nonzeros in the constraint */
jlongArray jvars, /**< array with variables of constraint entries */
jdoubleArray jvals, /**< array with coefficients of constraint entries */
jdouble jlhs, /**< left hand side of constraint */
jdouble jrhs, /**< right hand side of constraint */
jboolean initial, /**< should the LP relaxation of constraint be in the initial LP?
* Usually set to TRUE. Set to FALSE for 'lazy constraints'. */
jboolean separate, /**< should the constraint be separated during LP processing?
* Usually set to TRUE. */
jboolean enforce, /**< should the constraint be enforced during node processing?
* TRUE for model constraints, FALSE for additional, redundant constraints. */
jboolean check, /**< should the constraint be checked for feasibility?
* TRUE for model constraints, FALSE for additional, redundant constraints. */
jboolean propagate, /**< should the constraint be propagated during node processing?
* Usually set to TRUE. */
jboolean local, /**< is constraint only valid locally?
* Usually set to FALSE. Has to be set to TRUE, e.g., for branching constraints. */
jboolean modifiable, /**< is constraint modifiable (subject to column generation)?
* Usually set to FALSE. In column generation applications, set to TRUE if pricing
* adds coefficients to this constraint. */
jboolean dynamic, /**< is constraint subject to aging?
* Usually set to FALSE. Set to TRUE for own cuts which
* are seperated as constraints. */
jboolean removable, /**< should the relaxation be removed from the LP due to aging or cleanup?
* Usually set to FALSE. Set to TRUE for 'lazy constraints' and 'user cuts'. */
jboolean stickingatnode /**< should the constraint always be kept at the node where it was added, even
* if it may be moved to a more global node?
* Usually set to FALSE. Set to TRUE to for constraints that represent node data. */
)
{
SCIP* scip;
SCIP_CONS* cons;
const char* name;
int nvars;
/* convert JNI pointer into C pointer */
scip = (SCIP*) (size_t) jscip;
assert(scip != NULL);
/* convert JNI string into C const char* */
name = (*env)->GetStringUTFChars(env, jname, NULL);
if( name == NULL )
SCIPABORT();
/* create linear constraint with zero variables */
JNISCIP_CALL( SCIPcreateConsLinear(scip, &cons, name, 0, NULL, NULL, (SCIP_Real) jlhs, (SCIP_Real) jrhs,
(SCIP_Bool) initial, (SCIP_Bool) separate, (SCIP_Bool) enforce, (SCIP_Bool) check, (SCIP_Bool) propagate,
(SCIP_Bool) local, (SCIP_Bool) modifiable, (SCIP_Bool) dynamic, (SCIP_Bool) removable, (SCIP_Bool) stickingatnode) );
/* convert JNI integer into integer */
nvars = (int)jnvars;
if( nvars > 0 )
{
jlong* vars;
jdouble* vals;
int v;
JNISCIP_CALL( SCIPallocBufferArray(scip, &vars, nvars) );
JNISCIP_CALL( SCIPallocBufferArray(scip, &vals, nvars) );
(*env)->GetLongArrayRegion(env, jvars, 0, nvars, vars);
(*env)->GetDoubleArrayRegion(env, jvals, 0, nvars, vals);
for( v = 0; v < nvars; ++v )
{
JNISCIP_CALL( SCIPaddCoefLinear(scip, cons, (SCIP_VAR*)(size_t)vars[v], (SCIP_Real)vals[v]));
}
SCIPfreeBufferArray(scip, &vals);
SCIPfreeBufferArray(scip, &vars);
}
/* relase string object */
(*env)->ReleaseStringUTFChars(env, jname, name);
return (jlong)(size_t)cons;
}
/** method for either Farkas or Redcost pricing */
static
SCIP_RETCODE pricing(
SCIP* scip, /**< SCIP data structure */
SCIP_PRICER* pricer, /**< pricer */
SCIP_Real* lowerbound, /**< lowerbound pointer */
SCIP_Bool farkas /**< TRUE: Farkas pricing; FALSE: Redcost pricing */
)
{
SCIP_PRICERDATA* pricerdata; /* the data of the pricer */
SCIP_PROBDATA* probdata;
GRAPH* graph;
SCIP_VAR* var;
PATH* path;
SCIP_Real* edgecosts; /* edgecosts of the current subproblem */
char varname[SCIP_MAXSTRLEN];
SCIP_Real newlowerbound = -SCIPinfinity(scip);
SCIP_Real redcost; /* reduced cost */
int tail;
int e;
int t;
int i;
assert(scip != NULL);
assert(pricer != NULL);
/* get pricer data */
pricerdata = SCIPpricerGetData(pricer);
assert(pricerdata != NULL);
/* get problem data */
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
SCIPdebugMessage("solstat=%d\n", SCIPgetLPSolstat(scip));
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
newlowerbound = SCIPgetSolTransObj(scip, NULL);
SCIPdebug( SCIP_CALL( SCIPprintSol(scip, NULL, NULL, FALSE) ) );
# if 0
if ( pricerdata->lowerbound <= 4 )
{
char label[SCIP_MAXSTRLEN];
(void)SCIPsnprintf(label, SCIP_MAXSTRLEN, "X%g.gml", pricerdata->lowerbound);
SCIP_CALL( SCIPprobdataPrintGraph(scip, label , NULL, TRUE) );
pricerdata->lowerbound++;
}
#endif
/* get the graph*/
graph = SCIPprobdataGetGraph(probdata);
/* get dual solutions and save them in mi and pi */
for( t = 0; t < pricerdata->realnterms; ++t )
{
if( farkas )
{
pricerdata->mi[t] = SCIPgetDualfarkasLinear(scip, pricerdata->pathcons[t]);
}
else
{
pricerdata->mi[t] = SCIPgetDualsolLinear(scip, pricerdata->pathcons[t]);
assert(!SCIPisNegative(scip, pricerdata->mi[t]));
}
}
for( e = 0; e < pricerdata->nedges; ++e )
{
if( !pricerdata->bigt )
{
for( t = 0; t < pricerdata->realnterms; ++t )
{
if( farkas )
{
pricerdata->pi[t * pricerdata->nedges + e] = SCIPgetDualfarkasLinear(
scip, pricerdata->edgecons[t * pricerdata->nedges + e]);
}
else
{
pricerdata->pi[t * pricerdata->nedges + e] = SCIPgetDualsolLinear(
scip, pricerdata->edgecons[t * pricerdata->nedges + e]);
}
}
}
else
{
if( farkas )
{
pricerdata->pi[e] = SCIPgetDualfarkasLinear(
scip, pricerdata->edgecons[e]);
}
else
{
pricerdata->pi[e] = SCIPgetDualsolLinear(
scip, pricerdata->edgecons[e]);
}
}
}
SCIP_CALL( SCIPallocMemoryArray(scip, &path, graph->knots) );
SCIP_CALL( SCIPallocMemoryArray(scip, &edgecosts, pricerdata->nedges) );
if( pricerdata->bigt )
{
for( e = 0; e < pricerdata->nedges; ++e )
{
edgecosts[e] = (-pricerdata->pi[e]);
}
}
/* find shortest r-t (r root, t terminal) paths and create corresponding variables iff reduced cost < 0 */
for( t = 0; t < pricerdata->realnterms; ++t )
{
for( e = 0; e < pricerdata->nedges; ++e )
{
if( !pricerdata->bigt )
{
edgecosts[e] = (-pricerdata->pi[t * pricerdata->nedges + e]);
}
assert(!SCIPisNegative(scip, edgecosts[e]));
}
for( i = 0; i < graph->knots; i++ )
graph->mark[i] = 1;
graph_path_exec(scip, graph, FSP_MODE, pricerdata->root, edgecosts, path);
/* compute reduced cost of shortest path to terminal t */
redcost = 0.0;
tail = pricerdata->realterms[t];
while( tail != pricerdata->root )
{
redcost += edgecosts[path[tail].edge];
tail = graph->tail[path[tail].edge];
}
redcost -= pricerdata->mi[t];
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
{
newlowerbound += redcost;
}
/* check if reduced cost < 0 */
if( SCIPisNegative(scip, redcost) )
{
/* create variable to the shortest path (having reduced cost < 0) */
var = NULL;
sprintf(varname, "PathVar%d_%d", t, pricerdata->ncreatedvars[t]);
++(pricerdata->ncreatedvars[t]);
SCIP_CALL( SCIPcreateVarBasic(scip, &var, varname, 0.0, SCIPinfinity(scip), 0.0, SCIP_VARTYPE_CONTINUOUS) );
SCIP_CALL( SCIPaddPricedVar(scip, var, -redcost) );
tail = pricerdata->realterms[t];
while( tail != pricerdata->root )
{
/* add variable to constraints */
if( !pricerdata->bigt )
{
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->edgecons[t * pricerdata->nedges + path[tail].edge], var, 1.0) );
}
else
{
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->edgecons[path[tail].edge], var, 1.0) );
}
tail = graph->tail[path[tail].edge];
}
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->pathcons[t], var, 1.0) );
}
}
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
*lowerbound = newlowerbound;
SCIPfreeMemoryArray(scip, &edgecosts);
SCIPfreeMemoryArray(scip, &path);
return SCIP_OKAY;
}
/** main procedure of the zeroobj heuristic, creates and solves a sub-SCIP */
SCIP_RETCODE SCIPapplyZeroobj(
SCIP* scip, /**< original SCIP data structure */
SCIP_HEUR* heur, /**< heuristic data structure */
SCIP_RESULT* result, /**< result data structure */
SCIP_Real minimprove, /**< factor by which zeroobj should at least improve the incumbent */
SCIP_Longint nnodes /**< node limit for the subproblem */
)
{
SCIP* subscip; /* the subproblem created by zeroobj */
SCIP_HASHMAP* varmapfw; /* mapping of SCIP variables to sub-SCIP variables */
SCIP_VAR** vars; /* original problem's variables */
SCIP_VAR** subvars; /* subproblem's variables */
SCIP_HEURDATA* heurdata; /* heuristic's private data structure */
SCIP_EVENTHDLR* eventhdlr; /* event handler for LP events */
SCIP_Real cutoff; /* objective cutoff for the subproblem */
SCIP_Real timelimit; /* time limit for zeroobj subproblem */
SCIP_Real memorylimit; /* memory limit for zeroobj subproblem */
SCIP_Real large;
int nvars; /* number of original problem's variables */
int i;
SCIP_Bool success;
SCIP_Bool valid;
SCIP_RETCODE retcode;
SCIP_SOL** subsols;
int nsubsols;
assert(scip != NULL);
assert(heur != NULL);
assert(result != NULL);
assert(nnodes >= 0);
assert(0.0 <= minimprove && minimprove <= 1.0);
*result = SCIP_DIDNOTRUN;
/* only call heuristic once at the root */
if( SCIPgetDepth(scip) <= 0 && SCIPheurGetNCalls(heur) > 0 )
return SCIP_OKAY;
/* get heuristic data */
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
/* only call the heuristic if we do not have an incumbent */
if( SCIPgetNSolsFound(scip) > 0 && heurdata->onlywithoutsol )
return SCIP_OKAY;
/* check whether there is enough time and memory left */
timelimit = 0.0;
memorylimit = 0.0;
SCIP_CALL( SCIPgetRealParam(scip, "limits/time", &timelimit) );
if( !SCIPisInfinity(scip, timelimit) )
timelimit -= SCIPgetSolvingTime(scip);
SCIP_CALL( SCIPgetRealParam(scip, "limits/memory", &memorylimit) );
/* substract the memory already used by the main SCIP and the estimated memory usage of external software */
if( !SCIPisInfinity(scip, memorylimit) )
{
memorylimit -= SCIPgetMemUsed(scip)/1048576.0;
memorylimit -= SCIPgetMemExternEstim(scip)/1048576.0;
}
/* abort if no time is left or not enough memory to create a copy of SCIP, including external memory usage */
if( timelimit <= 0.0 || memorylimit <= 2.0*SCIPgetMemExternEstim(scip)/1048576.0 )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
/* get variable data */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
/* initialize the subproblem */
SCIP_CALL( SCIPcreate(&subscip) );
/* create the variable mapping hash map */
SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), SCIPcalcHashtableSize(5 * nvars)) );
SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
/* different methods to create sub-problem: either copy LP relaxation or the CIP with all constraints */
valid = FALSE;
/* copy complete SCIP instance */
SCIP_CALL( SCIPcopy(scip, subscip, varmapfw, NULL, "zeroobj", TRUE, FALSE, TRUE, &valid) );
SCIPdebugMessage("Copying the SCIP instance was %s complete.\n", valid ? "" : "not ");
/* create event handler for LP events */
eventhdlr = NULL;
SCIP_CALL( SCIPincludeEventhdlrBasic(subscip, &eventhdlr, EVENTHDLR_NAME, EVENTHDLR_DESC, eventExecZeroobj, NULL) );
if( eventhdlr == NULL )
{
SCIPerrorMessage("event handler for "HEUR_NAME" heuristic not found.\n");
return SCIP_PLUGINNOTFOUND;
}
/* determine large value to set variables to */
large = SCIPinfinity(scip);
if( !SCIPisInfinity(scip, 0.1 / SCIPfeastol(scip)) )
large = 0.1 / SCIPfeastol(scip);
/* get variable image and change to 0.0 in sub-SCIP */
for( i = 0; i < nvars; i++ )
{
SCIP_Real adjustedbound;
SCIP_Real lb;
SCIP_Real ub;
SCIP_Real inf;
subvars[i] = (SCIP_VAR*) SCIPhashmapGetImage(varmapfw, vars[i]);
SCIP_CALL( SCIPchgVarObj(subscip, subvars[i], 0.0) );
lb = SCIPvarGetLbGlobal(subvars[i]);
ub = SCIPvarGetUbGlobal(subvars[i]);
inf = SCIPinfinity(subscip);
/* adjust infinite bounds in order to avoid that variables with non-zero objective
* get fixed to infinite value in zeroobj subproblem
*/
if( SCIPisInfinity(subscip, ub ) )
{
adjustedbound = MAX(large, lb+large);
adjustedbound = MIN(adjustedbound, inf);
SCIP_CALL( SCIPchgVarUbGlobal(subscip, subvars[i], adjustedbound) );
}
if( SCIPisInfinity(subscip, -lb ) )
{
adjustedbound = MIN(-large, ub-large);
adjustedbound = MAX(adjustedbound, -inf);
SCIP_CALL( SCIPchgVarLbGlobal(subscip, subvars[i], adjustedbound) );
}
}
/* free hash map */
SCIPhashmapFree(&varmapfw);
/* do not abort subproblem on CTRL-C */
SCIP_CALL( SCIPsetBoolParam(subscip, "misc/catchctrlc", FALSE) );
/* disable output to console */
SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 0) );
/* set limits for the subproblem */
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nnodes) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/time", timelimit) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/memory", memorylimit) );
SCIP_CALL( SCIPsetIntParam(subscip, "limits/solutions", 1) );
/* forbid recursive call of heuristics and separators solving sub-SCIPs */
SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );
/* disable expensive techniques that merely work on the dual bound */
/* disable cutting plane separation */
SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );
/* disable expensive presolving */
SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );
if( !SCIPisParamFixed(subscip, "presolving/maxrounds") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "presolving/maxrounds", 50) );
}
/* use best dfs node selection */
if( SCIPfindNodesel(subscip, "dfs") != NULL && !SCIPisParamFixed(subscip, "nodeselection/dfs/stdpriority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/dfs/stdpriority", INT_MAX/4) );
}
/* use inference branching */
if( SCIPfindBranchrule(subscip, "inference") != NULL && !SCIPisParamFixed(subscip, "branching/inference/priority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "branching/leastinf/priority", INT_MAX/4) );
}
/* employ a limit on the number of enforcement rounds in the quadratic constraint handler; this fixes the issue that
* sometimes the quadratic constraint handler needs hundreds or thousands of enforcement rounds to determine the
* feasibility status of a single node without fractional branching candidates by separation (namely for uflquad
* instances); however, the solution status of the sub-SCIP might get corrupted by this; hence no deductions shall be
* made for the original SCIP
*/
if( SCIPfindConshdlr(subscip, "quadratic") != NULL && !SCIPisParamFixed(subscip, "constraints/quadratic/enfolplimit") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "constraints/quadratic/enfolplimit", 10) );
}
/* disable feaspump and fracdiving */
if( !SCIPisParamFixed(subscip, "heuristics/feaspump/freq") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "heuristics/feaspump/freq", -1) );
}
if( !SCIPisParamFixed(subscip, "heuristics/fracdiving/freq") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "heuristics/fracdiving/freq", -1) );
}
/* restrict LP iterations */
SCIP_CALL( SCIPsetLongintParam(subscip, "lp/iterlim", 2*heurdata->maxlpiters / MAX(1,nnodes)) );
SCIP_CALL( SCIPsetLongintParam(subscip, "lp/rootiterlim", heurdata->maxlpiters) );
#ifdef SCIP_DEBUG
/* for debugging zeroobj, enable MIP output */
SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 5) );
SCIP_CALL( SCIPsetIntParam(subscip, "display/freq", 100000000) );
#endif
/* if there is already a solution, add an objective cutoff */
if( SCIPgetNSols(scip) > 0 )
{
SCIP_Real upperbound;
SCIP_CONS* origobjcons;
#ifndef NDEBUG
int nobjvars;
nobjvars = 0;
#endif
cutoff = SCIPinfinity(scip);
assert( !SCIPisInfinity(scip,SCIPgetUpperbound(scip)) );
upperbound = SCIPgetUpperbound(scip) - SCIPsumepsilon(scip);
if( !SCIPisInfinity(scip,-1.0*SCIPgetLowerbound(scip)) )
{
cutoff = (1-minimprove)*SCIPgetUpperbound(scip) + minimprove*SCIPgetLowerbound(scip);
}
else
{
if( SCIPgetUpperbound(scip) >= 0 )
cutoff = ( 1 - minimprove ) * SCIPgetUpperbound ( scip );
else
cutoff = ( 1 + minimprove ) * SCIPgetUpperbound ( scip );
}
cutoff = MIN(upperbound, cutoff);
SCIP_CALL( SCIPcreateConsLinear(subscip, &origobjcons, "objbound_of_origscip", 0, NULL, NULL, -SCIPinfinity(subscip), cutoff,
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE) );
for( i = 0; i < nvars; ++i)
{
if( !SCIPisFeasZero(subscip, SCIPvarGetObj(vars[i])) )
{
SCIP_CALL( SCIPaddCoefLinear(subscip, origobjcons, subvars[i], SCIPvarGetObj(vars[i])) );
#ifndef NDEBUG
nobjvars++;
#endif
}
}
SCIP_CALL( SCIPaddCons(subscip, origobjcons) );
SCIP_CALL( SCIPreleaseCons(subscip, &origobjcons) );
assert(nobjvars == SCIPgetNObjVars(scip));
}
/* catch LP events of sub-SCIP */
SCIP_CALL( SCIPtransformProb(subscip) );
SCIP_CALL( SCIPcatchEvent(subscip, SCIP_EVENTTYPE_NODESOLVED, eventhdlr, (SCIP_EVENTDATA*) heurdata, NULL) );
SCIPdebugMessage("solving subproblem: nnodes=%"SCIP_LONGINT_FORMAT"\n", nnodes);
retcode = SCIPsolve(subscip);
/* drop LP events of sub-SCIP */
SCIP_CALL( SCIPdropEvent(subscip, SCIP_EVENTTYPE_NODESOLVED, eventhdlr, (SCIP_EVENTDATA*) heurdata, -1) );
/* errors in solving the subproblem should not kill the overall solving process;
* hence, the return code is caught and a warning is printed, only in debug mode, SCIP will stop.
*/
if( retcode != SCIP_OKAY )
{
#ifndef NDEBUG
SCIP_CALL( retcode );
#endif
SCIPwarningMessage(scip, "Error while solving subproblem in zeroobj heuristic; sub-SCIP terminated with code <%d>\n",retcode);
}
/* check, whether a solution was found;
* due to numerics, it might happen that not all solutions are feasible -> try all solutions until one was accepted
*/
nsubsols = SCIPgetNSols(subscip);
subsols = SCIPgetSols(subscip);
success = FALSE;
for( i = 0; i < nsubsols && (!success || heurdata->addallsols); ++i )
{
SCIP_CALL( createNewSol(scip, subscip, subvars, heur, subsols[i], &success) );
if( success )
*result = SCIP_FOUNDSOL;
}
#ifdef SCIP_DEBUG
SCIP_CALL( SCIPprintStatistics(subscip, NULL) );
#endif
/* free subproblem */
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/* standard "main" method for mex interface */
void mexFunction(
int nlhs, /* number of expected outputs */
mxArray* plhs[], /* array of pointers to output arguments */
int nrhs, /* number of inputs */
const mxArray* prhs[] /* array of pointers to input arguments */
)
{
SCIP* scip;
SCIP_VAR** vars;
SCIP_Real* objs;
SCIP_Real* lhss;
SCIP_Real* rhss;
SCIP_Real* lbs;
SCIP_Real* ubs;
SCIP_Real* matrix;
SCIP_Real* bestsol;
SCIP_Real* objval;
char* vartypes;
char objsense[SCIP_MAXSTRLEN];
int nvars;
int nconss;
int stringsize;
int i;
if( SCIPmajorVersion() < 2 )
{
mexErrMsgTxt("SCIP versions less than 2.0 are not supported\n");
return;
}
/* initialize SCIP */
SCIP_CALL_ABORT( SCIPcreate(&scip) );
/* output SCIP information */
SCIPprintVersion(scip, NULL);
/* include default SCIP plugins */
SCIP_CALL_ABORT( SCIPincludeDefaultPlugins(scip) );
if( nlhs != 2 || nrhs != 8 )
mexErrMsgTxt("invalid number of parameters. Call as [bestsol, objval] = matscip(matrix, lhs, rhs, obj, lb, ub, vartype, objsense)\n");
if( mxIsSparse(prhs[0]) )
mexErrMsgTxt("sparse matrices are not supported yet"); /* ???????? of course this has to change */
/* get linear constraint coefficient matrix */
matrix = mxGetPr(prhs[0]);
if( matrix == NULL )
mexErrMsgTxt("matrix must not be NULL");
if( mxGetNumberOfDimensions(prhs[0]) != 2 )
mexErrMsgTxt("matrix must have exactly two dimensions");
/* get dimensions of matrix */
nconss = mxGetM(prhs[0]);
nvars = mxGetN(prhs[0]);
assert(nconss > 0);
assert(nvars > 0);
/* get left hand sides of linear constraints */
lhss = mxGetPr(prhs[1]);
if( mxGetM(prhs[1]) != nconss )
mexErrMsgTxt("dimension of left hand side vector does not match matrix dimension");
assert(lhss != NULL);
/* get right hand sides of linear constraints */
rhss = mxGetPr(prhs[2]);
if( mxGetM(prhs[2]) != nconss )
mexErrMsgTxt("dimension of right hand side vector does not match matrix dimension");
assert(rhss != NULL);
/* get objective coefficients */
objs = mxGetPr(prhs[3]);
if( mxGetM(prhs[3]) != nvars )
mexErrMsgTxt("dimension of objective coefficient vector does not match matrix dimension");
/* get lower bounds of variables */
lbs = mxGetPr(prhs[4]);
if( mxGetM(prhs[4]) != nvars )
mexErrMsgTxt("dimension of lower bound vector does not match matrix dimension");
/* get upper bounds of variables */
ubs = mxGetPr(prhs[5]);
if( mxGetM(prhs[5]) != nvars )
mexErrMsgTxt("dimension of upper bound vector does not match matrix dimension");
/* allocate memory for variable type characters */
SCIP_CALL_ABORT( SCIPallocMemoryArray(scip, &vartypes, nvars+1) );
/* get variable types */
if( mxGetString(prhs[6], vartypes, nvars+1) != 0 )
mexErrMsgTxt("Error when parsing variable types, maybe a wrong vector dimension?");
/* get objective sense */
stringsize = mxGetNumberOfElements(prhs[7]);
if( stringsize != 3 )
mexErrMsgTxt("objective sense must be a three character word: \"max\" or \"min\"");
if( mxGetString(prhs[7], objsense, stringsize+1) != 0)
mexErrMsgTxt("Error when parsing objective sense string");
if( strcmp(objsense,"max") != 0 && strcmp(objsense,"min") != 0 )
mexErrMsgTxt("objective sense must be either \"max\" or \"min\"");
/* get output parameters */
plhs[0] = mxCreateDoubleMatrix(nvars, 1, mxREAL);
bestsol = mxGetPr(plhs[0]);
plhs[1] = mxCreateDoubleScalar(mxREAL);
objval = mxGetPr(plhs[1]);
/* create SCIP problem */
SCIP_CALL_ABORT( SCIPcreateProb(scip, "mex_prob", NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
/* allocate memory for variable array */
SCIP_CALL_ABORT( SCIPallocMemoryArray(scip, &vars, nvars) );
/* create variables */
for( i = 0; i < nvars; ++i)
{
SCIP_VARTYPE vartype;
char varname[SCIP_MAXSTRLEN];
/* convert vartype character to SCIP vartype */
if( vartypes[i] == 'i' )
vartype = SCIP_VARTYPE_INTEGER;
else if( vartypes[i] == 'b' )
vartype = SCIP_VARTYPE_BINARY;
else if( vartypes[i] == 'c' )
vartype = SCIP_VARTYPE_CONTINUOUS;
else
mexErrMsgTxt("unkown variable type");
/* variables get canonic names x_i */
(void) SCIPsnprintf(varname, SCIP_MAXSTRLEN, "x_%d", i);
/* create variable object and add it to SCIP */
SCIP_CALL_ABORT( SCIPcreateVar(scip, &vars[i], varname, lbs[i], ubs[i], objs[i],
vartype, TRUE, FALSE, NULL, NULL, NULL, NULL, NULL) );
assert(vars[i] != NULL);
SCIP_CALL_ABORT( SCIPaddVar(scip, vars[i]) );
}
/* create linear constraints */
for( i = 0; i < nconss; ++i )
{
SCIP_CONS* cons;
char consname[SCIP_MAXSTRLEN];
int j;
/* constraints get canonic names cons_i */
(void) SCIPsnprintf(consname, SCIP_MAXSTRLEN, "cons_%d", i);
/* create empty linear constraint */
SCIP_CALL_ABORT( SCIPcreateConsLinear(scip, &cons, consname, 0, NULL, NULL, lhss[i], rhss[i],
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE) );
/* add non-zero coefficients to linear constraint */
for( j = 0; j < nvars; ++j )
{
if( !SCIPisFeasZero(scip, matrix[i+j*nconss]) )
{
SCIP_CALL_ABORT( SCIPaddCoefLinear(scip, cons, vars[j], matrix[i+j*nconss]) );
}
}
/* add constraint to SCIP and release it */
SCIP_CALL_ABORT( SCIPaddCons(scip, cons) );
SCIP_CALL_ABORT( SCIPreleaseCons(scip, &cons) );
}
/* set objective sense in SCIP */
if( strcmp(objsense,"max") == 0)
{
SCIP_CALL_ABORT( SCIPsetObjsense(scip, SCIP_OBJSENSE_MAXIMIZE) );
}
else if( strcmp(objsense,"min") == 0)
{
SCIP_CALL_ABORT( SCIPsetObjsense(scip, SCIP_OBJSENSE_MINIMIZE) );
}
else
/* this should have been caught earlier when parsing objsense */
mexErrMsgTxt("unkown objective sense");
/* solve SCIP problem */
SCIP_CALL_ABORT( SCIPsolve(scip) );
/* if SCIP found a solution, pass it back into MATLAB output parameters */
if( SCIPgetNSols > 0 )
{
SCIP_SOL* scipbestsol;
/* get incumbent solution vector */
scipbestsol = SCIPgetBestSol(scip);
assert(scipbestsol != NULL);
/* get objective value of incumbent solution */
*objval = SCIPgetSolOrigObj(scip, scipbestsol);
assert(!SCIPisInfinity(scip, REALABS(*objval)));
/* copy solution values into output vector */
for( i = 0; i < nvars; ++i )
bestsol[i] = SCIPgetSolVal(scip,scipbestsol,vars[i]);
}
/* release variables */
for( i = 0; i < nvars; ++i )
{
SCIP_CALL_ABORT( SCIPreleaseVar(scip, &vars[i]) );
}
/* free memory for variable arrays */
SCIPfreeMemoryArray(scip, &vartypes);
SCIPfreeMemoryArray(scip, &vars);
/* deinitialize SCIP */
SCIP_CALL_ABORT( SCIPfree(&scip) );
/* check for memory leaks */
BMScheckEmptyMemory();
return;
}
/** reduced cost pricing method of variable pricer for feasible LPs */
static
SCIP_DECL_PRICERREDCOST(pricerRedcostBinpacking) { /*lint --e{715}*/
SCIP* subscip;
SCIP_PRICERDATA* pricerdata;
SCIP_CONS** conss;
SCIP_VAR** vars;
int* ids;
SCIP_Bool addvar;
SCIP_SOL** sols;
int nsols;
int s;
int nitems;
SCIP_Longint* values;
SCIP_Longint* weights;
SCIP_Longint* capacities;
int nbins;
int b;
SCIP_Real timelimit;
SCIP_Real memorylimit;
SCIP_Real dualHallBound;
assert(scip != NULL);
assert(pricer != NULL);
(*result) = SCIP_DIDNOTRUN;
/* get the pricer data */
pricerdata = SCIPpricerGetData(pricer);
assert(pricerdata != NULL);
capacities = pricerdata->capacities;
conss = pricerdata->conss;
ids = pricerdata->ids;
values = pricerdata->values;
weights = pricerdata->weights;
nitems = pricerdata->nitems;
nbins = pricerdata->nbins;
dualHallBound = 0.0;
// run pricing problem for each bin
for (b = 0; b < nbins; ++b) {
// assert(SCIPgetDualsolLinear(scip, conss[nitems+b])<= 0);
// TODO edit if correct objsense
dualHallBound -= SCIPgetDualsolLinear(scip, conss[nitems+b]);
if (b < nbins-1 && capacities[b+1] == capacities[b])
continue;
/* get the remaining time and memory limit */
SCIP_CALL(SCIPgetRealParam(scip, "limits/time", &timelimit));
if (!SCIPisInfinity(scip, timelimit))
timelimit -= SCIPgetSolvingTime(scip);
SCIP_CALL(SCIPgetRealParam(scip, "limits/memory", &memorylimit));
if (!SCIPisInfinity(scip, memorylimit))
memorylimit -= SCIPgetMemUsed(scip) / 1048576.0;
/* initialize SCIP */
SCIP_CALL(SCIPcreate(&subscip));
SCIP_CALL(SCIPincludeDefaultPlugins(subscip));
/* free sub SCIP */
SCIP_CALL(SCIPcreateProb(subscip, "pricing", NULL, NULL, NULL, NULL, NULL, NULL, NULL));
SCIP_CALL(SCIPsetObjsense(subscip, SCIP_OBJSENSE_MAXIMIZE));
/* do not abort subproblem on CTRL-C */
SCIP_CALL(SCIPsetBoolParam(subscip, "misc/catchctrlc", FALSE));
/* disable output to console */
SCIP_CALL(SCIPsetIntParam(subscip, "display/verblevel", 0));
/* set time and memory limit */
SCIP_CALL(SCIPsetRealParam(subscip, "limits/time", timelimit));
SCIP_CALL(SCIPsetRealParam(subscip, "limits/memory", memorylimit));
SCIP_CALL(SCIPallocMemoryArray(subscip, &vars, nitems));
/* initialization local pricing problem */
SCIP_CALL(initPricing(scip, pricerdata, subscip, vars, b));
SCIPdebugMessage("solve pricer problem\n");
/* solve sub SCIP */
SCIP_CALL(SCIPsolve(subscip));
sols = SCIPgetSols(subscip);
nsols = SCIPgetNSols(subscip);
addvar = FALSE;
/* loop over all solutions and create the corresponding column to master if the reduced cost are negative for master,
* that is the objective value i greater than 1.0
*/
for (s = 0; s < nsols; ++s) {
SCIP_Bool feasible;
SCIP_SOL* sol;
/* the soultion should be sorted w.r.t. the objective function value */
assert(s == 0 || SCIPisFeasGE(subscip, SCIPgetSolOrigObj(subscip, sols[s - 1]), SCIPgetSolOrigObj(subscip, sols[s])));
sol = sols[s];
assert(sol != NULL);
/* check if solution is feasible in original sub SCIP */
SCIP_CALL(SCIPcheckSolOrig(subscip, sol, &feasible, FALSE, FALSE));
if (!feasible) {
SCIPwarningMessage("solution in pricing problem (capacity <%d>) is infeasible\n", capacities[b]);
continue;
}
/* check if the solution has a value greater than 1.0 */
// First subscip?
if (SCIPisFeasGT(scip, SCIPgetSolOrigObj(subscip, sol), dualHallBound)) {
SCIP_VAR* var;
SCIP_VARDATA* vardata;
int* consids;
char strtmp[SCIP_MAXSTRLEN];
char name[SCIP_MAXSTRLEN];
int nconss;
int o;
int v;
SCIP_Longint totalvalue;
SCIP_Longint totalweight;
SCIPdebug(SCIP_CALL(SCIPprintSol(subscip, sol, NULL, FALSE)));
nconss = 0;
totalvalue = 0.0;
totalweight = 0.0;
(void) SCIPsnprintf(name, SCIP_MAXSTRLEN, "items");
SCIP_CALL(SCIPallocBufferArray(scip, &consids, nitems));
/* check which variables are fixed -> which item belongs to this packing */
for (o = 0, v = 0; o < nitems; ++o) {
if (!SCIPconsIsEnabled(conss[o]))
continue;
assert(SCIPgetNFixedonesSetppc(scip, conss[o]) == 0);
if (SCIPgetSolVal(subscip, sol, vars[v]) > 0.5) {
(void) SCIPsnprintf(strtmp, SCIP_MAXSTRLEN, "_%d", ids[o]);
strcat(name, strtmp);
consids[nconss] = o;
totalvalue+= values[o];
totalweight += weights[o];
nconss++;
} else
assert(SCIPisFeasEQ(subscip, SCIPgetSolVal(subscip, sol, vars[v]), 0.0));
v++;
}
SCIP_CALL(SCIPvardataCreateBinpacking(scip, &vardata, consids, nconss));
/* create variable for a new column with objective function coefficient 0.0 */
SCIP_CALL(SCIPcreateVarBinpacking(scip, &var, name, -totalvalue, FALSE, TRUE, vardata));
/* add the new variable to the pricer store */
SCIP_CALL(SCIPaddPricedVar(scip, var, 1.0));
addvar = TRUE;
/* change the upper bound of the binary variable to lazy since the upper bound is already enforced due to
* the objective function the set covering constraint; The reason for doing is that, is to avoid the bound
* of x <= 1 in the LP relaxation since this bound constraint would produce a dual variable which might have
* a positive reduced cost
*/
SCIP_CALL(SCIPchgVarUbLazy(scip, var, 1.0));
/* check which variable are fixed -> which orders belong to this packing */
for (v = 0; v < nconss; v++) {
assert(SCIPconsIsEnabled(conss[consids[v]]));
SCIP_CALL(SCIPaddCoefSetppc(scip, conss[consids[v]], var));
}
/* add variable to hall constraints */
for (v = 0; v <= b; v++) {
SCIP_CALL(SCIPaddCoefLinear(scip, conss[nitems+v], var, 1.0));
if (totalweight <= capacities[v] && v < b && capacities[b] != capacities[b+1])
break;
}
SCIPdebug(SCIPprintVar(scip, var, NULL));
SCIP_CALL(SCIPreleaseVar(scip, &var));
SCIPfreeBufferArray(scip, &consids);
} else
break;
}
/* free pricer MIP */
SCIPfreeMemoryArray(subscip, &vars);
if (addvar || SCIPgetStatus(subscip) == SCIP_STATUS_OPTIMAL)
(*result) = SCIP_SUCCESS;
/* free sub SCIP */
SCIP_CALL(SCIPfree(&subscip));
}
return SCIP_OKAY;
}
/** branching execution method for fractional LP solutions */
static
SCIP_DECL_BRANCHEXECLP(branchExeclpStp)
{ /*lint --e{715}*/
SCIP_PROBDATA* probdata;
SCIP_CONS* consin;
SCIP_CONS* consout;
SCIP_NODE* vertexin;
SCIP_NODE* vertexout;
SCIP_VAR** edgevars;
SCIP_Real estimatein;
SCIP_Real estimateout;
GRAPH* g;
int e;
int branchvertex;
assert(branchrule != NULL);
assert(strcmp(SCIPbranchruleGetName(branchrule), BRANCHRULE_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
SCIPdebugMessage("Execlp method of Stp branching\n ");
estimatein = SCIPgetUpperbound(scip);
estimateout = SCIPgetUpperbound(scip);
*result = SCIP_DIDNOTRUN;
/* get problem data */
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
/* get graph */
g = SCIPprobdataGetGraph(probdata);
assert(g != NULL);
/* get vertex to branch on */
SCIP_CALL( selectBranchingVertex(scip, &branchvertex) );
if( branchvertex == UNKNOWN )
{
SCIPdebugMessage("Branching did not run \n");
return SCIP_OKAY;
}
edgevars = SCIPprobdataGetEdgeVars(scip);
/* create constraints */
SCIP_CALL( SCIPcreateConsLinear(scip, &consin, "consin", 0,
NULL, NULL, 1.0, 1.0,
TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE) );
SCIP_CALL( SCIPcreateConsLinear(scip, &consout, "consout", 0,
NULL, NULL, 0.0, 0.0,
TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE) );
for( e = g->inpbeg[branchvertex]; e != EAT_LAST; e = g->ieat[e] )
{
SCIP_CALL( SCIPaddCoefLinear(scip, consin, edgevars[e], 1.0) );
SCIP_CALL( SCIPaddCoefLinear(scip, consout, edgevars[e], 1.0) );
SCIP_CALL( SCIPaddCoefLinear(scip, consout, edgevars[flipedge(e)], 1.0) );
}
/* create the child nodes */
SCIP_CALL( SCIPcreateChild(scip, &vertexin, 1.0, estimatein) );
SCIP_CALL( SCIPcreateChild(scip, &vertexout, 1.0, estimateout) );
assert(vertexin != NULL);
assert(vertexout != NULL);
SCIP_CALL( SCIPaddConsNode(scip, vertexin, consin, NULL) );
SCIP_CALL( SCIPaddConsNode(scip, vertexout, consout, NULL) );
/* relase constraints */
SCIP_CALL( SCIPreleaseCons(scip, &consin) );
SCIP_CALL( SCIPreleaseCons(scip, &consout) );
SCIPdebugMessage("Branched on stp vertex %d \n", branchvertex);
*result = SCIP_BRANCHED;
return SCIP_OKAY;
}
