/* check whether coef is the r-th row of the inverse basis matrix B^-1; this is
* the case if( coef * B ) is the r-th unit vector */
SCIP_RETCODE SCIPdebugCheckBInvRow(
SCIP* scip, /**< SCIP data structure */
int r, /**< row number */
SCIP_Real* coef /**< r-th row of the inverse basis matrix */
)
{
SCIP_Real vecval;
SCIP_Real matrixval;
int* basisind;
int nrows;
int idx;
int i;
int k;
assert(scip != NULL);
nrows = SCIPgetNLPRows(scip);
/* get basic indices for the basic matrix B */
SCIP_CALL( SCIPallocBufferArray(scip, &basisind, nrows) );
SCIP_CALL( SCIPgetLPBasisInd(scip, basisind) );
/* loop over the columns of B */
for( k = 0; k < nrows; ++k )
{
vecval = 0.0;
/* indices of basic columns and rows:
* - index i >= 0 corresponds to column i,
* - index i < 0 to row -i-1
*/
idx = basisind[k];
/* check if we have a slack variable; this is the case if idx < 0 */
if( idx >= 0 )
{
/* loop over the rows to compute the corresponding value in the unit vector */
for( i = 0; i < nrows; ++i )
{
SCIP_CALL( SCIPlpiGetCoef(scip->lp->lpi, i, idx, &matrixval) );
vecval += coef[i] * matrixval;
}
}
else
{
assert( idx < 0 );
/* retransform idx
* - index i >= 0 corresponds to column i,
* - index i < 0 to row -i-1
*/
idx = -idx - 1;
assert( idx >= 0 && idx < nrows );
/* since idx < 0 we are in the case of a slack variable, i.e., the corresponding column
is the idx-unit vector; note that some LP solver return a -idx-unit vector */
/* vecval = REALABS(coef[idx]);*/
vecval = coef[idx];
}
/* check if vecval fits to the r-th unit vector */
if( k == r && !SCIPisFeasEQ(scip, vecval, 1.0) )
{
/* we expected a 1.0 and found something different */
SCIPwarningMessage("checked SCIPgetLPBInvRow() found value <%g> expected 1.0\n", vecval);
}
else if( k != r && !SCIPisFeasZero(scip, vecval) )
{
/* we expected a 0.0 and found something different */
SCIPwarningMessage("checked SCIPgetLPBInvRow() found value <%g> expected 0.0\n", vecval);
}
}
SCIPfreeBufferArray(scip, &basisind);
return SCIP_OKAY;
}
/** initializes the pricing problem for the given capacity */
static
SCIP_RETCODE initPricing(
SCIP* scip, /**< SCIP data structure */
SCIP_PRICERDATA* pricerdata, /**< pricer data */
SCIP* subscip, /**< pricing SCIP data structure */
SCIP_VAR** vars /**< variable array for the items */
)
{
SCIP_CONS** conss;
SCIP_Longint* vals;
SCIP_CONS* cons;
SCIP_VAR* var;
SCIP_Longint* weights;
SCIP_Longint capacity;
SCIP_Real dual;
int nitems;
int nvars;
int c;
assert( SCIPgetStage(subscip) == SCIP_STAGE_PROBLEM );
assert(pricerdata != NULL);
nitems = pricerdata->nitems;
conss = pricerdata->conss;
weights = pricerdata->weights;
capacity = pricerdata->capacity;
nvars = 0;
SCIP_CALL( SCIPallocBufferArray(subscip, &vals, nitems) );
/* create for each order, which is not assigned yet, a variable with objective coefficient */
for( c = 0; c < nitems; ++c )
{
cons = conss[c];
/* check if each constraint is setppc constraint */
assert( !strncmp( SCIPconshdlrGetName( SCIPconsGetHdlr(cons) ), "setppc", 6) );
/* constraints which are (locally) disabled/redundant are not of
* interest since the corresponding job is assigned to a packing
*/
if( !SCIPconsIsEnabled(cons) )
continue;
if( SCIPgetNFixedonesSetppc(scip, cons) == 1 )
{
/* disable constraint locally */
SCIP_CALL( SCIPdelConsLocal(scip, cons) );
continue;
}
/* dual value in original SCIP */
dual = SCIPgetDualsolSetppc(scip, cons);
SCIP_CALL( SCIPcreateVarBasic(subscip, &var, SCIPconsGetName(cons), 0.0, 1.0, dual, SCIP_VARTYPE_BINARY) );
SCIP_CALL( SCIPaddVar(subscip, var) );
vals[nvars] = weights[c];
vars[nvars] = var;
nvars++;
/* release variable */
SCIP_CALL( SCIPreleaseVar(subscip, &var) );
}
/* create capacity constraint */
SCIP_CALL( SCIPcreateConsBasicKnapsack(subscip, &cons, "capacity", nvars, vars, vals,
capacity) );
SCIP_CALL( SCIPaddCons(subscip, cons) );
SCIP_CALL( SCIPreleaseCons(subscip, &cons) );
/* add constraint of the branching decisions */
SCIP_CALL( addBranchingDecisionConss(scip, subscip, vars, pricerdata->conshdlr) );
/* avoid to generate columns which are fixed to zero */
SCIP_CALL( addFixedVarsConss(scip, subscip, vars, conss, nitems) );
SCIPfreeBufferArray(subscip, &vals);
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecObjpscostdiving) /*lint --e{715}*/
{ /*lint --e{715}*/
SCIP_HEURDATA* heurdata;
SCIP_LPSOLSTAT lpsolstat;
SCIP_VAR* var;
SCIP_VAR** lpcands;
SCIP_Real* lpcandssol;
SCIP_Real* lpcandsfrac;
SCIP_Real primsol;
SCIP_Real frac;
SCIP_Real pscostquot;
SCIP_Real bestpscostquot;
SCIP_Real oldobj;
SCIP_Real newobj;
SCIP_Real objscale;
SCIP_Bool bestcandmayrounddown;
SCIP_Bool bestcandmayroundup;
SCIP_Bool bestcandroundup;
SCIP_Bool mayrounddown;
SCIP_Bool mayroundup;
SCIP_Bool roundup;
SCIP_Bool lperror;
SCIP_Longint ncalls;
SCIP_Longint nsolsfound;
SCIP_Longint nlpiterations;
SCIP_Longint maxnlpiterations;
int* roundings;
int nvars;
int varidx;
int nlpcands;
int startnlpcands;
int depth;
int maxdepth;
int maxdivedepth;
int divedepth;
int bestcand;
int c;
assert(heur != NULL);
assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
assert(SCIPhasCurrentNodeLP(scip));
*result = SCIP_DELAYED;
/* only call heuristic, if an optimal LP solution is at hand */
if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
return SCIP_OKAY;
/* only call heuristic, if the LP solution is basic (which allows fast resolve in diving) */
if( !SCIPisLPSolBasic(scip) )
return SCIP_OKAY;
/* don't dive two times at the same node */
if( SCIPgetLastDivenode(scip) == SCIPgetNNodes(scip) && SCIPgetDepth(scip) > 0 )
return SCIP_OKAY;
*result = SCIP_DIDNOTRUN;
/* get heuristic's data */
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
/* only apply heuristic, if only a few solutions have been found */
if( heurdata->maxsols >= 0 && SCIPgetNSolsFound(scip) >= heurdata->maxsols )
return SCIP_OKAY;
/* only try to dive, if we are in the correct part of the tree, given by minreldepth and maxreldepth */
depth = SCIPgetDepth(scip);
maxdepth = SCIPgetMaxDepth(scip);
maxdepth = MAX(maxdepth, 30);
if( depth < heurdata->minreldepth*maxdepth || depth > heurdata->maxreldepth*maxdepth )
return SCIP_OKAY;
/* calculate the maximal number of LP iterations until heuristic is aborted */
nlpiterations = SCIPgetNNodeLPIterations(scip);
ncalls = SCIPheurGetNCalls(heur);
nsolsfound = 10*SCIPheurGetNBestSolsFound(heur) + heurdata->nsuccess;
maxnlpiterations = (SCIP_Longint)((1.0 + 10.0*(nsolsfound+1.0)/(ncalls+1.0)) * heurdata->maxlpiterquot * nlpiterations);
maxnlpiterations += heurdata->maxlpiterofs;
/* don't try to dive, if we took too many LP iterations during diving */
if( heurdata->nlpiterations >= maxnlpiterations )
return SCIP_OKAY;
/* allow at least a certain number of LP iterations in this dive */
maxnlpiterations = MAX(maxnlpiterations, heurdata->nlpiterations + MINLPITER);
/* get fractional variables that should be integral */
SCIP_CALL( SCIPgetLPBranchCands(scip, &lpcands, &lpcandssol, &lpcandsfrac, &nlpcands, NULL) );
/* don't try to dive, if there are no fractional variables */
if( nlpcands == 0 )
return SCIP_OKAY;
/* calculate the maximal diving depth */
nvars = SCIPgetNBinVars(scip) + SCIPgetNIntVars(scip);
if( SCIPgetNSolsFound(scip) == 0 )
maxdivedepth = (int)(heurdata->depthfacnosol * nvars);
else
maxdivedepth = (int)(heurdata->depthfac * nvars);
maxdivedepth = MIN(maxdivedepth, 10*maxdepth);
*result = SCIP_DIDNOTFIND;
/* get temporary memory for remembering the current soft roundings */
SCIP_CALL( SCIPallocBufferArray(scip, &roundings, nvars) );
BMSclearMemoryArray(roundings, nvars);
/* start diving */
SCIP_CALL( SCIPstartDive(scip) );
SCIPdebugMessage("(node %"SCIP_LONGINT_FORMAT") executing objpscostdiving heuristic: depth=%d, %d fractionals, dualbound=%g, maxnlpiterations=%"SCIP_LONGINT_FORMAT", maxdivedepth=%d\n",
SCIPgetNNodes(scip), SCIPgetDepth(scip), nlpcands, SCIPgetDualbound(scip), maxnlpiterations, maxdivedepth);
/* dive as long we are in the given diving depth and iteration limits and fractional variables exist, but
* - if the last objective change was in a direction, that corresponds to a feasible rounding, we continue in any case
* - if possible, we dive at least with the depth 10
* - if the number of fractional variables decreased at least with 1 variable per 2 dive depths, we continue diving
*/
lperror = FALSE;
lpsolstat = SCIP_LPSOLSTAT_OPTIMAL;
divedepth = 0;
bestcandmayrounddown = FALSE;
bestcandmayroundup = FALSE;
startnlpcands = nlpcands;
while( !lperror && lpsolstat == SCIP_LPSOLSTAT_OPTIMAL && nlpcands > 0
&& (divedepth < 10
|| nlpcands <= startnlpcands - divedepth/2
|| (divedepth < maxdivedepth && nlpcands <= startnlpcands - divedepth/10
&& heurdata->nlpiterations < maxnlpiterations)) && !SCIPisStopped(scip) )
{
SCIP_RETCODE retcode;
divedepth++;
/* choose variable for objective change:
* - prefer variables that may not be rounded without destroying LP feasibility:
* - of these variables, change objective value of variable with largest rel. difference of pseudo cost values
* - if all remaining fractional variables may be rounded without destroying LP feasibility:
* - change objective value of variable with largest rel. difference of pseudo cost values
*/
bestcand = -1;
bestpscostquot = -1.0;
bestcandmayrounddown = TRUE;
bestcandmayroundup = TRUE;
bestcandroundup = FALSE;
for( c = 0; c < nlpcands; ++c )
{
var = lpcands[c];
mayrounddown = SCIPvarMayRoundDown(var);
mayroundup = SCIPvarMayRoundUp(var);
primsol = lpcandssol[c];
frac = lpcandsfrac[c];
if( mayrounddown || mayroundup )
{
/* the candidate may be rounded: choose this candidate only, if the best candidate may also be rounded */
if( bestcandmayrounddown || bestcandmayroundup )
{
/* choose rounding direction:
* - if variable may be rounded in both directions, round corresponding to the pseudo cost values
* - otherwise, round in the infeasible direction, because feasible direction is tried by rounding
* the current fractional solution
*/
roundup = FALSE;
if( mayrounddown && mayroundup )
calcPscostQuot(scip, var, primsol, frac, 0, &pscostquot, &roundup);
else if( mayrounddown )
calcPscostQuot(scip, var, primsol, frac, +1, &pscostquot, &roundup);
else
calcPscostQuot(scip, var, primsol, frac, -1, &pscostquot, &roundup);
/* prefer variables, that have already been soft rounded but failed to get integral */
varidx = SCIPvarGetProbindex(var);
assert(0 <= varidx && varidx < nvars);
if( roundings[varidx] != 0 )
pscostquot *= 1000.0;
/* check, if candidate is new best candidate */
if( pscostquot > bestpscostquot )
{
bestcand = c;
bestpscostquot = pscostquot;
bestcandmayrounddown = mayrounddown;
bestcandmayroundup = mayroundup;
bestcandroundup = roundup;
}
}
}
else
{
/* the candidate may not be rounded: calculate pseudo cost quotient and preferred direction */
calcPscostQuot(scip, var, primsol, frac, 0, &pscostquot, &roundup);
/* prefer variables, that have already been soft rounded but failed to get integral */
varidx = SCIPvarGetProbindex(var);
assert(0 <= varidx && varidx < nvars);
if( roundings[varidx] != 0 )
pscostquot *= 1000.0;
/* check, if candidate is new best candidate: prefer unroundable candidates in any case */
if( bestcandmayrounddown || bestcandmayroundup || pscostquot > bestpscostquot )
{
bestcand = c;
bestpscostquot = pscostquot;
bestcandmayrounddown = FALSE;
bestcandmayroundup = FALSE;
bestcandroundup = roundup;
}
}
}
assert(bestcand != -1);
/* if all candidates are roundable, try to round the solution */
if( bestcandmayrounddown || bestcandmayroundup )
{
SCIP_Bool success;
/* create solution from diving LP and try to round it */
SCIP_CALL( SCIPlinkLPSol(scip, heurdata->sol) );
SCIP_CALL( SCIProundSol(scip, heurdata->sol, &success) );
if( success )
{
SCIPdebugMessage("objpscostdiving found roundable primal solution: obj=%g\n",
SCIPgetSolOrigObj(scip, heurdata->sol));
/* try to add solution to SCIP */
SCIP_CALL( SCIPtrySol(scip, heurdata->sol, FALSE, FALSE, FALSE, FALSE, &success) );
/* check, if solution was feasible and good enough */
if( success )
{
SCIPdebugMessage(" -> solution was feasible and good enough\n");
*result = SCIP_FOUNDSOL;
}
}
}
var = lpcands[bestcand];
/* check, if the best candidate was already subject to soft rounding */
varidx = SCIPvarGetProbindex(var);
assert(0 <= varidx && varidx < nvars);
if( roundings[varidx] == +1 )
{
/* variable was already soft rounded upwards: hard round it downwards */
SCIP_CALL( SCIPchgVarUbDive(scip, var, SCIPfeasFloor(scip, lpcandssol[bestcand])) );
SCIPdebugMessage(" dive %d/%d: var <%s>, round=%u/%u, sol=%g, was already soft rounded upwards -> bounds=[%g,%g]\n",
divedepth, maxdivedepth, SCIPvarGetName(var), bestcandmayrounddown, bestcandmayroundup,
lpcandssol[bestcand], SCIPgetVarLbDive(scip, var), SCIPgetVarUbDive(scip, var));
}
else if( roundings[varidx] == -1 )
{
/* variable was already soft rounded downwards: hard round it upwards */
SCIP_CALL( SCIPchgVarLbDive(scip, var, SCIPfeasCeil(scip, lpcandssol[bestcand])) );
SCIPdebugMessage(" dive %d/%d: var <%s>, round=%u/%u, sol=%g, was already soft rounded downwards -> bounds=[%g,%g]\n",
divedepth, maxdivedepth, SCIPvarGetName(var), bestcandmayrounddown, bestcandmayroundup,
lpcandssol[bestcand], SCIPgetVarLbDive(scip, var), SCIPgetVarUbDive(scip, var));
}
else
{
assert(roundings[varidx] == 0);
/* apply soft rounding of best candidate via a change in the objective value */
objscale = divedepth * 1000.0;
oldobj = SCIPgetVarObjDive(scip, var);
if( bestcandroundup )
{
/* soft round variable up: make objective value (more) negative */
if( oldobj < 0.0 )
newobj = objscale * oldobj;
else
newobj = -objscale * oldobj;
newobj = MIN(newobj, -objscale);
/* remember, that this variable was soft rounded upwards */
roundings[varidx] = +1;
}
else
{
/* soft round variable down: make objective value (more) positive */
if( oldobj > 0.0 )
newobj = objscale * oldobj;
else
newobj = -objscale * oldobj;
newobj = MAX(newobj, objscale);
/* remember, that this variable was soft rounded downwards */
roundings[varidx] = -1;
}
SCIP_CALL( SCIPchgVarObjDive(scip, var, newobj) );
SCIPdebugMessage(" dive %d/%d, LP iter %"SCIP_LONGINT_FORMAT"/%"SCIP_LONGINT_FORMAT": var <%s>, round=%u/%u, sol=%g, bounds=[%g,%g], obj=%g, newobj=%g\n",
divedepth, maxdivedepth, heurdata->nlpiterations, maxnlpiterations,
SCIPvarGetName(var), bestcandmayrounddown, bestcandmayroundup,
lpcandssol[bestcand], SCIPgetVarLbDive(scip, var), SCIPgetVarUbDive(scip, var), oldobj, newobj);
}
/* resolve the diving LP */
nlpiterations = SCIPgetNLPIterations(scip);
retcode = SCIPsolveDiveLP(scip, MAX((int)(maxnlpiterations - heurdata->nlpiterations), MINLPITER), &lperror);
lpsolstat = SCIPgetLPSolstat(scip);
/* Errors in the LP solver should not kill the overall solving process, if the LP is just needed for a heuristic.
* Hence in optimized mode, the return code is caught and a warning is printed, only in debug mode, SCIP will stop.
*/
if( retcode != SCIP_OKAY )
{
#ifndef NDEBUG
if( lpsolstat != SCIP_LPSOLSTAT_UNBOUNDEDRAY )
{
SCIP_CALL( retcode );
}
#endif
SCIPwarningMessage("Error while solving LP in Objpscostdiving heuristic; LP solve terminated with code <%d>\n", retcode);
SCIPwarningMessage("This does not affect the remaining solution procedure --> continue\n");
}
if( lperror )
break;
/* update iteration count */
heurdata->nlpiterations += SCIPgetNLPIterations(scip) - nlpiterations;
/* get LP solution status and fractional variables, that should be integral */
if( lpsolstat == SCIP_LPSOLSTAT_OPTIMAL )
{
/* get new fractional variables */
SCIP_CALL( SCIPgetLPBranchCands(scip, &lpcands, &lpcandssol, &lpcandsfrac, &nlpcands, NULL) );
}
SCIPdebugMessage(" -> lpsolstat=%d, nfrac=%d\n", lpsolstat, nlpcands);
}
/* check if a solution has been found */
if( nlpcands == 0 && !lperror && lpsolstat == SCIP_LPSOLSTAT_OPTIMAL )
{
SCIP_Bool success;
/* create solution from diving LP */
SCIP_CALL( SCIPlinkLPSol(scip, heurdata->sol) );
SCIPdebugMessage("objpscostdiving found primal solution: obj=%g\n", SCIPgetSolOrigObj(scip, heurdata->sol));
/* try to add solution to SCIP */
SCIP_CALL( SCIPtrySol(scip, heurdata->sol, FALSE, FALSE, FALSE, FALSE, &success) );
/* check, if solution was feasible and good enough */
if( success )
{
SCIPdebugMessage(" -> solution was feasible and good enough\n");
*result = SCIP_FOUNDSOL;
}
}
/* end diving */
SCIP_CALL( SCIPendDive(scip) );
if( *result == SCIP_FOUNDSOL )
heurdata->nsuccess++;
/* free temporary memory for remembering the current soft roundings */
SCIPfreeBufferArray(scip, &roundings);
SCIPdebugMessage("objpscostdiving heuristic finished\n");
return SCIP_OKAY;
}
/** execution method of presolver */
static
SCIP_DECL_PRESOLEXEC(presolExecDualagg)
{ /*lint --e{715}*/
SCIP_MATRIX* matrix;
SCIP_Bool initialized;
SCIP_Bool complete;
assert(result != NULL);
*result = SCIP_DIDNOTRUN;
if( (SCIPgetStage(scip) != SCIP_STAGE_PRESOLVING) || SCIPinProbing(scip) || SCIPisNLPEnabled(scip) )
return SCIP_OKAY;
if( SCIPisStopped(scip) || SCIPgetNActivePricers(scip) > 0 )
return SCIP_OKAY;
if( SCIPgetNBinVars(scip) == 0 )
return SCIP_OKAY;
if( !SCIPallowDualReds(scip) )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
matrix = NULL;
SCIP_CALL( SCIPmatrixCreate(scip, &matrix, &initialized, &complete) );
/* we only work on pure MIPs currently */
if( initialized && complete )
{
AGGRTYPE* aggtypes;
SCIP_VAR** binvars;
int nvaragg;
int ncols;
ncols = SCIPmatrixGetNColumns(matrix);
nvaragg = 0;
SCIP_CALL( SCIPallocBufferArray(scip, &aggtypes, ncols) );
BMSclearMemoryArray(aggtypes, ncols);
SCIP_CALL( SCIPallocBufferArray(scip, &binvars, ncols) );
SCIPdebug( BMSclearMemoryArray(binvars, ncols) );
/* search for aggregations */
SCIP_CALL( findUplockAggregations(scip, matrix, &nvaragg, aggtypes, binvars) );
SCIP_CALL( findDownlockAggregations(scip, matrix, &nvaragg, aggtypes, binvars) );
/* apply aggregations, if we found any */
if( nvaragg > 0 )
{
int v;
for( v = 0; v < ncols; v++ )
{
if( aggtypes[v] != NOAGG )
{
SCIP_Bool infeasible;
SCIP_Bool redundant;
SCIP_Bool aggregated;
SCIP_Real ub;
SCIP_Real lb;
ub = SCIPmatrixGetColUb(matrix, v);
lb = SCIPmatrixGetColLb(matrix, v);
/* aggregate variable */
assert(binvars[v] != NULL);
if( aggtypes[v] == BIN0UBOUND )
{
SCIP_CALL( SCIPaggregateVars(scip, SCIPmatrixGetVar(matrix, v), binvars[v], 1.0, ub-lb,
ub, &infeasible, &redundant, &aggregated) );
}
else
{
assert(aggtypes[v] == BIN0LBOUND);
SCIP_CALL( SCIPaggregateVars(scip, SCIPmatrixGetVar(matrix, v), binvars[v], 1.0, lb-ub,
lb, &infeasible, &redundant, &aggregated) );
}
/* infeasible aggregation */
if( infeasible )
{
SCIPdebugMessage(" -> infeasible aggregation\n");
*result = SCIP_CUTOFF;
return SCIP_OKAY;
}
if( aggregated )
(*naggrvars)++;
}
}
/* set result pointer */
if( (*naggrvars) > 0 )
*result = SCIP_SUCCESS;
}
SCIPfreeBufferArray(scip, &binvars);
SCIPfreeBufferArray(scip, &aggtypes);
}
SCIPmatrixFree(scip, &matrix);
return SCIP_OKAY;
}
/** creates a subproblem for subscip by fixing a number of variables */
static
SCIP_RETCODE createSubproblem(
SCIP* scip, /**< original SCIP data structure */
SCIP* subscip, /**< SCIP data structure for the subproblem */
SCIP_VAR** subvars, /**< the variables of the subproblem */
SCIP_Real minfixingrate, /**< percentage of integer variables that have to be fixed */
SCIP_Bool binarybounds, /**< should general integers get binary bounds [floor(.),ceil(.)] ? */
SCIP_Bool uselprows, /**< should subproblem be created out of the rows in the LP rows? */
SCIP_Bool* success /**< pointer to store whether the problem was created successfully */
)
{
SCIP_VAR** vars; /* original SCIP variables */
SCIP_Real fixingrate;
int nvars;
int nbinvars;
int nintvars;
int i;
int fixingcounter;
assert(scip != NULL);
assert(subscip != NULL);
assert(subvars != NULL);
assert(0.0 <= minfixingrate && minfixingrate <= 1.0);
/* get required variable data */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, &nbinvars, &nintvars, NULL, NULL) );
fixingcounter = 0;
/* change bounds of variables of the subproblem */
for( i = 0; i < nbinvars + nintvars; i++ )
{
SCIP_Real lpsolval;
SCIP_Real lb;
SCIP_Real ub;
/* get the current LP solution for each variable */
lpsolval = SCIPgetRelaxSolVal(scip, vars[i]);
if( SCIPisFeasIntegral(scip, lpsolval) )
{
/* fix variables to current LP solution if it is integral,
* use exact integral value, if the variable is only integral within numerical tolerances
*/
lb = SCIPfloor(scip, lpsolval+0.5);
ub = lb;
fixingcounter++;
}
else if( binarybounds )
{
/* if the sub problem should be a binary problem, change the bounds to nearest integers */
lb = SCIPfeasFloor(scip,lpsolval);
ub = SCIPfeasCeil(scip,lpsolval);
}
else
{
/* otherwise just copy bounds */
lb = SCIPvarGetLbGlobal(vars[i]);
ub = SCIPvarGetUbGlobal(vars[i]);
}
/* perform the bound change */
SCIP_CALL( SCIPchgVarLbGlobal(subscip, subvars[i], lb) );
SCIP_CALL( SCIPchgVarUbGlobal(subscip, subvars[i], ub) );
}
/* abort, if all integer variables were fixed (which should not happen for MIP) */
if( fixingcounter == nbinvars + nintvars )
{
*success = FALSE;
return SCIP_OKAY;
}
else
fixingrate = fixingcounter / (SCIP_Real)(MAX(nbinvars + nintvars, 1));
SCIPdebugMessage("fixing rate: %g = %d of %d\n", fixingrate, fixingcounter, nbinvars + nintvars);
/* abort, if the amount of fixed variables is insufficient */
if( fixingrate < minfixingrate )
{
*success = FALSE;
return SCIP_OKAY;
}
if( uselprows )
{
SCIP_ROW** rows; /* original scip rows */
int nrows;
/* get the rows and their number */
SCIP_CALL( SCIPgetLPRowsData(scip, &rows, &nrows) );
/* copy all rows to linear constraints */
for( i = 0; i < nrows; i++ )
{
SCIP_CONS* cons;
SCIP_VAR** consvars;
SCIP_COL** cols;
SCIP_Real constant;
SCIP_Real lhs;
SCIP_Real rhs;
SCIP_Real* vals;
int nnonz;
int j;
/* ignore rows that are only locally valid */
if( SCIProwIsLocal(rows[i]) )
continue;
/* get the row's data */
constant = SCIProwGetConstant(rows[i]);
lhs = SCIProwGetLhs(rows[i]) - constant;
rhs = SCIProwGetRhs(rows[i]) - constant;
vals = SCIProwGetVals(rows[i]);
nnonz = SCIProwGetNNonz(rows[i]);
cols = SCIProwGetCols(rows[i]);
assert( lhs <= rhs );
/* allocate memory array to be filled with the corresponding subproblem variables */
SCIP_CALL( SCIPallocBufferArray(subscip, &consvars, nnonz) );
for( j = 0; j < nnonz; j++ )
consvars[j] = subvars[SCIPvarGetProbindex(SCIPcolGetVar(cols[j]))];
/* create a new linear constraint and add it to the subproblem */
SCIP_CALL( SCIPcreateConsLinear(subscip, &cons, SCIProwGetName(rows[i]), nnonz, consvars, vals, lhs, rhs,
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
SCIP_CALL( SCIPaddCons(subscip, cons) );
SCIP_CALL( SCIPreleaseCons(subscip, &cons) );
/* free temporary memory */
SCIPfreeBufferArray(subscip, &consvars);
}
}
*success = TRUE;
return SCIP_OKAY;
}
/** create the extra constraint of local branching and add it to subscip */
static
SCIP_RETCODE addLocalBranchingConstraint(
SCIP* scip, /**< SCIP data structure of the original problem */
SCIP* subscip, /**< SCIP data structure of the subproblem */
SCIP_VAR** subvars, /**< variables of the subproblem */
SCIP_HEURDATA* heurdata /**< heuristic's data structure */
)
{
SCIP_CONS* cons; /* local branching constraint to create */
SCIP_VAR** consvars;
SCIP_VAR** vars;
SCIP_SOL* bestsol;
int nbinvars;
int i;
SCIP_Real lhs;
SCIP_Real rhs;
SCIP_Real* consvals;
char consname[SCIP_MAXSTRLEN];
(void) SCIPsnprintf(consname, SCIP_MAXSTRLEN, "%s_localbranchcons", SCIPgetProbName(scip));
/* get the data of the variables and the best solution */
SCIP_CALL( SCIPgetVarsData(scip, &vars, NULL, &nbinvars, NULL, NULL, NULL) );
bestsol = SCIPgetBestSol(scip);
assert( bestsol != NULL );
/* memory allocation */
SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nbinvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &consvals, nbinvars) );
/* set initial left and right hand sides of local branching constraint */
lhs = (SCIP_Real)heurdata->emptyneighborhoodsize + 1.0;
rhs = (SCIP_Real)heurdata->curneighborhoodsize;
/* create the distance (to incumbent) function of the binary variables */
for( i = 0; i < nbinvars; i++ )
{
SCIP_Real solval;
solval = SCIPgetSolVal(scip, bestsol, vars[i]);
assert( SCIPisFeasIntegral(scip,solval) );
/* is variable i part of the binary support of bestsol? */
if( SCIPisFeasEQ(scip,solval,1.0) )
{
consvals[i] = -1.0;
rhs -= 1.0;
lhs -= 1.0;
}
else
consvals[i] = 1.0;
consvars[i] = subvars[i];
assert( SCIPvarGetType(consvars[i]) == SCIP_VARTYPE_BINARY );
}
/* creates localbranching constraint and adds it to subscip */
SCIP_CALL( SCIPcreateConsLinear(subscip, &cons, consname, nbinvars, consvars, consvals,
lhs, rhs, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
SCIP_CALL( SCIPaddCons(subscip, cons) );
SCIP_CALL( SCIPreleaseCons(subscip, &cons) );
/* free local memory */
SCIPfreeBufferArray(scip, &consvals);
SCIPfreeBufferArray(scip, &consvars);
return SCIP_OKAY;
}
/** LP solution separation method of separator */
static
SCIP_DECL_SEPAEXECLP(sepaExeclpGomory)
{ /*lint --e{715}*/
SCIP_SEPADATA* sepadata;
SCIP_VAR** vars;
SCIP_COL** cols;
SCIP_ROW** rows;
SCIP_Real* binvrow;
SCIP_Real* cutcoefs;
SCIP_Real maxscale;
SCIP_Real minfrac;
SCIP_Real maxfrac;
SCIP_Longint maxdnom;
SCIP_Bool cutoff;
int* basisind;
int naddedcuts;
int nvars;
int ncols;
int nrows;
int ncalls;
int depth;
int maxdepth;
int maxsepacuts;
int c;
int i;
assert(sepa != NULL);
assert(strcmp(SCIPsepaGetName(sepa), SEPA_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
*result = SCIP_DIDNOTRUN;
sepadata = SCIPsepaGetData(sepa);
assert(sepadata != NULL);
depth = SCIPgetDepth(scip);
ncalls = SCIPsepaGetNCallsAtNode(sepa);
minfrac = sepadata->away;
maxfrac = 1.0 - sepadata->away;
/* only call separator, if we are not close to terminating */
if( SCIPisStopped(scip) )
return SCIP_OKAY;
/* only call the gomory cut separator a given number of times at each node */
if( (depth == 0 && sepadata->maxroundsroot >= 0 && ncalls >= sepadata->maxroundsroot)
|| (depth > 0 && sepadata->maxrounds >= 0 && ncalls >= sepadata->maxrounds) )
return SCIP_OKAY;
/* only call separator, if an optimal LP solution is at hand */
if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
return SCIP_OKAY;
/* only call separator, if the LP solution is basic */
if( !SCIPisLPSolBasic(scip) )
return SCIP_OKAY;
/* only call separator, if there are fractional variables */
if( SCIPgetNLPBranchCands(scip) == 0 )
return SCIP_OKAY;
/* get variables data */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
/* get LP data */
SCIP_CALL( SCIPgetLPColsData(scip, &cols, &ncols) );
SCIP_CALL( SCIPgetLPRowsData(scip, &rows, &nrows) );
if( ncols == 0 || nrows == 0 )
return SCIP_OKAY;
#if 0 /* if too many columns, separator is usually very slow: delay it until no other cuts have been found */
if( ncols >= 50*nrows )
return SCIP_OKAY;
if( ncols >= 5*nrows )
{
int ncutsfound;
ncutsfound = SCIPgetNCutsFound(scip);
if( ncutsfound > sepadata->lastncutsfound || !SCIPsepaWasLPDelayed(sepa) )
{
sepadata->lastncutsfound = ncutsfound;
*result = SCIP_DELAYED;
return SCIP_OKAY;
}
}
#endif
/* set the maximal denominator in rational representation of gomory cut and the maximal scale factor to
* scale resulting cut to integral values to avoid numerical instabilities
*/
/**@todo find better but still stable gomory cut settings: look at dcmulti, gesa3, khb0525, misc06, p2756 */
maxdepth = SCIPgetMaxDepth(scip);
if( depth == 0 )
{
maxdnom = 1000;
maxscale = 1000.0;
}
else if( depth <= maxdepth/4 )
{
maxdnom = 1000;
maxscale = 1000.0;
}
else if( depth <= maxdepth/2 )
{
maxdnom = 100;
maxscale = 100.0;
}
else
{
maxdnom = 10;
maxscale = 10.0;
}
/* allocate temporary memory */
SCIP_CALL( SCIPallocBufferArray(scip, &cutcoefs, nvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &basisind, nrows) );
SCIP_CALL( SCIPallocBufferArray(scip, &binvrow, nrows) );
/* get basis indices */
SCIP_CALL( SCIPgetLPBasisInd(scip, basisind) );
/* get the maximal number of cuts allowed in a separation round */
if( depth == 0 )
maxsepacuts = sepadata->maxsepacutsroot;
else
maxsepacuts = sepadata->maxsepacuts;
SCIPdebugMessage("searching gomory cuts: %d cols, %d rows, maxdnom=%"SCIP_LONGINT_FORMAT", maxscale=%g, maxcuts=%d\n",
ncols, nrows, maxdnom, maxscale, maxsepacuts);
cutoff = FALSE;
naddedcuts = 0;
/* for all basic columns belonging to integer variables, try to generate a gomory cut */
for( i = 0; i < nrows && naddedcuts < maxsepacuts && !SCIPisStopped(scip) && !cutoff; ++i )
{
SCIP_Bool tryrow;
tryrow = FALSE;
c = basisind[i];
if( c >= 0 )
{
SCIP_VAR* var;
assert(c < ncols);
var = SCIPcolGetVar(cols[c]);
if( SCIPvarGetType(var) != SCIP_VARTYPE_CONTINUOUS )
{
SCIP_Real primsol;
primsol = SCIPcolGetPrimsol(cols[c]);
assert(SCIPgetVarSol(scip, var) == primsol); /*lint !e777*/
if( SCIPfeasFrac(scip, primsol) >= minfrac )
{
SCIPdebugMessage("trying gomory cut for col <%s> [%g]\n", SCIPvarGetName(var), primsol);
tryrow = TRUE;
}
}
}
else if( sepadata->separaterows )
{
SCIP_ROW* row;
assert(0 <= -c-1 && -c-1 < nrows);
row = rows[-c-1];
if( SCIProwIsIntegral(row) && !SCIProwIsModifiable(row) )
{
SCIP_Real primsol;
primsol = SCIPgetRowActivity(scip, row);
if( SCIPfeasFrac(scip, primsol) >= minfrac )
{
SCIPdebugMessage("trying gomory cut for row <%s> [%g]\n", SCIProwGetName(row), primsol);
tryrow = TRUE;
}
}
}
if( tryrow )
{
SCIP_Real cutrhs;
SCIP_Real cutact;
SCIP_Bool success;
SCIP_Bool cutislocal;
/* get the row of B^-1 for this basic integer variable with fractional solution value */
SCIP_CALL( SCIPgetLPBInvRow(scip, i, binvrow) );
cutact = 0.0;
cutrhs = SCIPinfinity(scip);
/* create a MIR cut out of the weighted LP rows using the B^-1 row as weights */
SCIP_CALL( SCIPcalcMIR(scip, NULL, BOUNDSWITCH, USEVBDS, ALLOWLOCAL, FIXINTEGRALRHS, NULL, NULL,
(int) MAXAGGRLEN(nvars), sepadata->maxweightrange, minfrac, maxfrac,
binvrow, 1.0, NULL, NULL, cutcoefs, &cutrhs, &cutact, &success, &cutislocal) );
assert(ALLOWLOCAL || !cutislocal);
/* @todo Currently we are using the SCIPcalcMIR() function to compute the coefficients of the Gomory
* cut. Alternatively, we could use the direct version (see thesis of Achterberg formula (8.4)) which
* leads to cut a of the form \sum a_i x_i \geq 1. Rumor has it that these cuts are better.
*/
SCIPdebugMessage(" -> success=%u: %g <= %g\n", success, cutact, cutrhs);
/* if successful, convert dense cut into sparse row, and add the row as a cut */
if( success && SCIPisFeasGT(scip, cutact, cutrhs) )
{
SCIP_ROW* cut;
char cutname[SCIP_MAXSTRLEN];
int v;
/* construct cut name */
if( c >= 0 )
(void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "gom%d_x%d", SCIPgetNLPs(scip), c);
else
(void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "gom%d_s%d", SCIPgetNLPs(scip), -c-1);
/* create empty cut */
SCIP_CALL( SCIPcreateEmptyRowSepa(scip, &cut, sepa, cutname, -SCIPinfinity(scip), cutrhs,
cutislocal, FALSE, sepadata->dynamiccuts) );
/* cache the row extension and only flush them if the cut gets added */
SCIP_CALL( SCIPcacheRowExtensions(scip, cut) );
/* collect all non-zero coefficients */
for( v = 0; v < nvars; ++v )
{
if( !SCIPisZero(scip, cutcoefs[v]) )
{
SCIP_CALL( SCIPaddVarToRow(scip, cut, vars[v], cutcoefs[v]) );
}
}
if( SCIProwGetNNonz(cut) == 0 )
{
assert(SCIPisFeasNegative(scip, cutrhs));
SCIPdebugMessage(" -> gomory cut detected infeasibility with cut 0 <= %f\n", cutrhs);
cutoff = TRUE;
}
else if( SCIProwGetNNonz(cut) == 1 )
{
/* add the bound change as cut to avoid that the LP gets modified. that would mean the LP is not flushed
* and the method SCIPgetLPBInvRow() fails; SCIP internally will apply that bound change automatically
*/
SCIP_CALL( SCIPaddCut(scip, NULL, cut, TRUE) );
naddedcuts++;
}
else
{
/* Only take efficacious cuts, except for cuts with one non-zero coefficients (= bound
* changes); the latter cuts will be handeled internally in sepastore.
*/
if( SCIPisCutEfficacious(scip, NULL, cut) )
{
assert(success == TRUE);
SCIPdebugMessage(" -> gomory cut for <%s>: act=%f, rhs=%f, eff=%f\n",
c >= 0 ? SCIPvarGetName(SCIPcolGetVar(cols[c])) : SCIProwGetName(rows[-c-1]),
cutact, cutrhs, SCIPgetCutEfficacy(scip, NULL, cut));
if( sepadata->makeintegral )
{
/* try to scale the cut to integral values */
SCIP_CALL( SCIPmakeRowIntegral(scip, cut, -SCIPepsilon(scip), SCIPsumepsilon(scip),
maxdnom, maxscale, MAKECONTINTEGRAL, &success) );
if( sepadata->forcecuts )
success = TRUE;
/* in case the left hand side in minus infinity and the right hand side is plus infinity the cut is
* useless so we are not taking it at all
*/
if( (SCIPisInfinity(scip, -SCIProwGetLhs(cut)) && SCIPisInfinity(scip, SCIProwGetRhs(cut))) )
success = FALSE;
/* @todo Trying to make the Gomory cut integral might fail. Due to numerical reasons/arguments we
* currently ignore such cuts. If the cut, however, has small support (let's say smaller or equal to
* 5), we might want to add that cut (even it does not have integral coefficients). To be able to
* do that we need to add a rank to the data structure of a row. The rank of original rows are
* zero and for aggregated rows it is the maximum over all used rows plus one.
*/
}
if( success )
{
SCIPdebugMessage(" -> found gomory cut <%s>: act=%f, rhs=%f, norm=%f, eff=%f, min=%f, max=%f (range=%f)\n",
cutname, SCIPgetRowLPActivity(scip, cut), SCIProwGetRhs(cut), SCIProwGetNorm(cut),
SCIPgetCutEfficacy(scip, NULL, cut),
SCIPgetRowMinCoef(scip, cut), SCIPgetRowMaxCoef(scip, cut),
SCIPgetRowMaxCoef(scip, cut)/SCIPgetRowMinCoef(scip, cut));
/* flush all changes before adding the cut */
SCIP_CALL( SCIPflushRowExtensions(scip, cut) );
/* add global cuts which are not implicit bound changes to the cut pool */
if( !cutislocal )
{
if( sepadata->delayedcuts )
{
SCIP_CALL( SCIPaddDelayedPoolCut(scip, cut) );
}
else
{
SCIP_CALL( SCIPaddPoolCut(scip, cut) );
}
}
else
{
/* local cuts we add to the sepastore */
SCIP_CALL( SCIPaddCut(scip, NULL, cut, FALSE) );
}
naddedcuts++;
}
}
}
/* release the row */
SCIP_CALL( SCIPreleaseRow(scip, &cut) );
}
}
}
/* free temporary memory */
SCIPfreeBufferArray(scip, &binvrow);
SCIPfreeBufferArray(scip, &basisind);
SCIPfreeBufferArray(scip, &cutcoefs);
SCIPdebugMessage("end searching gomory cuts: found %d cuts\n", naddedcuts);
sepadata->lastncutsfound = SCIPgetNCutsFound(scip);
/* evalute the result of the separation */
if( cutoff )
*result = SCIP_CUTOFF;
else if ( naddedcuts > 0 )
*result = SCIP_SEPARATED;
else
*result = SCIP_DIDNOTFIND;
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecRootsoldiving) /*lint --e{715}*/
{ /*lint --e{715}*/
SCIP_HEURDATA* heurdata;
SCIP_VAR** vars;
SCIP_Real* rootsol;
SCIP_Real* objchgvals;
int* softroundings;
int* intvalrounds;
int nvars;
int nbinvars;
int nintvars;
int nlpcands;
SCIP_LPSOLSTAT lpsolstat;
SCIP_Real absstartobjval;
SCIP_Real objstep;
SCIP_Real alpha;
SCIP_Real oldobj;
SCIP_Real newobj;
SCIP_Bool lperror;
SCIP_Bool lpsolchanged;
SCIP_Longint nsolsfound;
SCIP_Longint ncalls;
SCIP_Longint nlpiterations;
SCIP_Longint maxnlpiterations;
int depth;
int maxdepth;
int maxdivedepth;
int divedepth;
int startnlpcands;
int ncycles;
int i;
assert(heur != NULL);
assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
assert(SCIPhasCurrentNodeLP(scip));
*result = SCIP_DELAYED;
/* only call heuristic, if an optimal LP solution is at hand */
if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
return SCIP_OKAY;
/* only call heuristic, if the LP solution is basic (which allows fast resolve in diving) */
if( !SCIPisLPSolBasic(scip) )
return SCIP_OKAY;
/* don't dive two times at the same node */
if( SCIPgetLastDivenode(scip) == SCIPgetNNodes(scip) && SCIPgetDepth(scip) > 0 )
return SCIP_OKAY;
*result = SCIP_DIDNOTRUN;
/* get heuristic's data */
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
/* only apply heuristic, if only a few solutions have been found */
if( heurdata->maxsols >= 0 && SCIPgetNSolsFound(scip) >= heurdata->maxsols )
return SCIP_OKAY;
/* only try to dive, if we are in the correct part of the tree, given by minreldepth and maxreldepth */
depth = SCIPgetDepth(scip);
maxdepth = SCIPgetMaxDepth(scip);
maxdepth = MAX(maxdepth, 30);
if( depth < heurdata->minreldepth*maxdepth || depth > heurdata->maxreldepth*maxdepth )
return SCIP_OKAY;
/* calculate the maximal number of LP iterations until heuristic is aborted */
nlpiterations = SCIPgetNNodeLPIterations(scip);
ncalls = SCIPheurGetNCalls(heur);
nsolsfound = 10*SCIPheurGetNBestSolsFound(heur) + heurdata->nsuccess;
maxnlpiterations = (SCIP_Longint)((1.0 + 10.0*(nsolsfound+1.0)/(ncalls+1.0)) * heurdata->maxlpiterquot * nlpiterations);
maxnlpiterations += heurdata->maxlpiterofs;
/* don't try to dive, if we took too many LP iterations during diving */
if( heurdata->nlpiterations >= maxnlpiterations )
return SCIP_OKAY;
/* allow at least a certain number of LP iterations in this dive */
maxnlpiterations = MAX(maxnlpiterations, heurdata->nlpiterations + MINLPITER);
/* get number of fractional variables, that should be integral */
nlpcands = SCIPgetNLPBranchCands(scip);
/* don't try to dive, if there are no fractional variables */
if( nlpcands == 0 )
return SCIP_OKAY;
/* calculate the maximal diving depth */
nvars = SCIPgetNBinVars(scip) + SCIPgetNIntVars(scip);
if( SCIPgetNSolsFound(scip) == 0 )
maxdivedepth = (int)(heurdata->depthfacnosol * nvars);
else
maxdivedepth = (int)(heurdata->depthfac * nvars);
maxdivedepth = MAX(maxdivedepth, 10);
*result = SCIP_DIDNOTFIND;
/* get all variables of LP */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, &nbinvars, &nintvars, NULL, NULL) );
/* get root solution value of all binary and integer variables */
SCIP_CALL( SCIPallocBufferArray(scip, &rootsol, nbinvars + nintvars) );
for( i = 0; i < nbinvars + nintvars; i++ )
rootsol[i] = SCIPvarGetRootSol(vars[i]);
/* get current LP objective value, and calculate length of a single step in an objective coefficient */
absstartobjval = SCIPgetLPObjval(scip);
absstartobjval = ABS(absstartobjval);
absstartobjval = MAX(absstartobjval, 1.0);
objstep = absstartobjval / 10.0;
/* initialize array storing the preferred soft rounding directions and counting the integral value rounds */
SCIP_CALL( SCIPallocBufferArray(scip, &softroundings, nbinvars + nintvars) );
BMSclearMemoryArray(softroundings, nbinvars + nintvars);
SCIP_CALL( SCIPallocBufferArray(scip, &intvalrounds, nbinvars + nintvars) );
BMSclearMemoryArray(intvalrounds, nbinvars + nintvars);
/* allocate temporary memory for buffering objective changes */
SCIP_CALL( SCIPallocBufferArray(scip, &objchgvals, nbinvars + nintvars) );
/* start diving */
SCIP_CALL( SCIPstartDive(scip) );
SCIPdebugMessage("(node %"SCIP_LONGINT_FORMAT") executing rootsoldiving heuristic: depth=%d, %d fractionals, dualbound=%g, maxnlpiterations=%"SCIP_LONGINT_FORMAT", maxdivedepth=%d, LPobj=%g, objstep=%g\n",
SCIPgetNNodes(scip), SCIPgetDepth(scip), nlpcands, SCIPgetDualbound(scip), maxnlpiterations, maxdivedepth,
SCIPgetLPObjval(scip), objstep);
lperror = FALSE;
divedepth = 0;
lpsolstat = SCIP_LPSOLSTAT_OPTIMAL;
alpha = heurdata->alpha;
ncycles = 0;
lpsolchanged = TRUE;
startnlpcands = nlpcands;
while( !lperror && lpsolstat == SCIP_LPSOLSTAT_OPTIMAL && nlpcands > 0 && ncycles < 10
&& (divedepth < 10
|| nlpcands <= startnlpcands - divedepth/2
|| (divedepth < maxdivedepth && heurdata->nlpiterations < maxnlpiterations))
&& !SCIPisStopped(scip) )
{
SCIP_Bool success;
int hardroundingidx;
int hardroundingdir;
SCIP_Real hardroundingoldbd;
SCIP_Real hardroundingnewbd;
SCIP_Bool boundschanged;
SCIP_RETCODE retcode;
/* create solution from diving LP and try to round it */
SCIP_CALL( SCIPlinkLPSol(scip, heurdata->sol) );
SCIP_CALL( SCIProundSol(scip, heurdata->sol, &success) );
if( success )
{
SCIPdebugMessage("rootsoldiving found roundable primal solution: obj=%g\n", SCIPgetSolOrigObj(scip, heurdata->sol));
/* try to add solution to SCIP */
SCIP_CALL( SCIPtrySol(scip, heurdata->sol, FALSE, FALSE, FALSE, FALSE, &success) );
/* check, if solution was feasible and good enough */
if( success )
{
SCIPdebugMessage(" -> solution was feasible and good enough\n");
*result = SCIP_FOUNDSOL;
}
}
divedepth++;
hardroundingidx = -1;
hardroundingdir = 0;
hardroundingoldbd = 0.0;
hardroundingnewbd = 0.0;
boundschanged = FALSE;
SCIPdebugMessage("dive %d/%d, LP iter %"SCIP_LONGINT_FORMAT"/%"SCIP_LONGINT_FORMAT":\n", divedepth, maxdivedepth, heurdata->nlpiterations, maxnlpiterations);
/* round solution x* from diving LP:
* - x~_j = down(x*_j) if x*_j is integer or binary variable and x*_j <= root solution_j
* - x~_j = up(x*_j) if x*_j is integer or binary variable and x*_j > root solution_j
* - x~_j = x*_j if x*_j is continuous variable
* change objective function in diving LP:
* - if x*_j is integral, or j is a continuous variable, set obj'_j = alpha * obj_j
* - otherwise, set obj'_j = alpha * obj_j + sign(x*_j - x~_j)
*/
for( i = 0; i < nbinvars + nintvars; i++ )
{
SCIP_VAR* var;
SCIP_Real solval;
var = vars[i];
oldobj = SCIPgetVarObjDive(scip, var);
newobj = oldobj;
solval = SCIPvarGetLPSol(var);
if( SCIPisFeasIntegral(scip, solval) )
{
/* if the variable became integral after a soft rounding, count the rounds; after a while, fix it to its
* current integral value;
* otherwise, fade out the objective value
*/
if( softroundings[i] != 0 && lpsolchanged )
{
intvalrounds[i]++;
if( intvalrounds[i] == 5 && SCIPgetVarLbDive(scip, var) < SCIPgetVarUbDive(scip, var) - 0.5 )
{
/* use exact integral value, if the variable is only integral within numerical tolerances */
solval = SCIPfloor(scip, solval+0.5);
SCIPdebugMessage(" -> fixing <%s> = %g\n", SCIPvarGetName(var), solval);
SCIP_CALL( SCIPchgVarLbDive(scip, var, solval) );
SCIP_CALL( SCIPchgVarUbDive(scip, var, solval) );
boundschanged = TRUE;
}
}
else
newobj = alpha * oldobj;
}
else if( solval <= rootsol[i] )
{
/* if the variable was soft rounded most of the time downwards, round it downwards by changing the bounds;
* otherwise, apply soft rounding by changing the objective value
*/
softroundings[i]--;
if( softroundings[i] <= -10 && hardroundingidx == -1 )
{
SCIPdebugMessage(" -> hard rounding <%s>[%g] <= %g\n",
SCIPvarGetName(var), solval, SCIPfeasFloor(scip, solval));
hardroundingidx = i;
hardroundingdir = -1;
hardroundingoldbd = SCIPgetVarUbDive(scip, var);
hardroundingnewbd = SCIPfeasFloor(scip, solval);
SCIP_CALL( SCIPchgVarUbDive(scip, var, hardroundingnewbd) );
boundschanged = TRUE;
}
else
newobj = alpha * oldobj + objstep;
}
else
{
/* if the variable was soft rounded most of the time upwards, round it upwards by changing the bounds;
* otherwise, apply soft rounding by changing the objective value
*/
softroundings[i]++;
if( softroundings[i] >= +10 && hardroundingidx == -1 )
{
SCIPdebugMessage(" -> hard rounding <%s>[%g] >= %g\n",
SCIPvarGetName(var), solval, SCIPfeasCeil(scip, solval));
hardroundingidx = i;
hardroundingdir = +1;
hardroundingoldbd = SCIPgetVarLbDive(scip, var);
hardroundingnewbd = SCIPfeasCeil(scip, solval);
SCIP_CALL( SCIPchgVarLbDive(scip, var, hardroundingnewbd) );
boundschanged = TRUE;
}
else
newobj = alpha * oldobj - objstep;
}
/* remember the objective change */
objchgvals[i] = newobj;
}
/* apply objective changes if there was no bound change */
if( !boundschanged )
{
/* apply cached changes on integer variables */
for( i = 0; i < nbinvars + nintvars; ++i )
{
SCIP_VAR* var;
var = vars[i];
SCIPdebugMessage(" -> i=%d var <%s>, solval=%g, rootsol=%g, oldobj=%g, newobj=%g\n",
i, SCIPvarGetName(var), SCIPvarGetLPSol(var), rootsol[i], SCIPgetVarObjDive(scip, var), objchgvals[i]);
SCIP_CALL( SCIPchgVarObjDive(scip, var, objchgvals[i]) );
}
/* fade out the objective values of the continuous variables */
for( i = nbinvars + nintvars; i < nvars; i++ )
{
SCIP_VAR* var;
var = vars[i];
oldobj = SCIPgetVarObjDive(scip, var);
newobj = alpha * oldobj;
SCIPdebugMessage(" -> i=%d var <%s>, solval=%g, oldobj=%g, newobj=%g\n",
i, SCIPvarGetName(var), SCIPvarGetLPSol(var), oldobj, newobj);
SCIP_CALL( SCIPchgVarObjDive(scip, var, newobj) );
}
}
SOLVEAGAIN:
/* resolve the diving LP */
nlpiterations = SCIPgetNLPIterations(scip);
retcode = SCIPsolveDiveLP(scip, MAX((int)(maxnlpiterations - heurdata->nlpiterations), MINLPITER), &lperror);
lpsolstat = SCIPgetLPSolstat(scip);
/* Errors in the LP solver should not kill the overall solving process, if the LP is just needed for a heuristic.
* Hence in optimized mode, the return code is caught and a warning is printed, only in debug mode, SCIP will stop.
*/
if( retcode != SCIP_OKAY )
{
#ifndef NDEBUG
if( lpsolstat != SCIP_LPSOLSTAT_UNBOUNDEDRAY )
{
SCIP_CALL( retcode );
}
#endif
SCIPwarningMessage(scip, "Error while solving LP in Rootsoldiving heuristic; LP solve terminated with code <%d>\n", retcode);
SCIPwarningMessage(scip, "This does not affect the remaining solution procedure --> continue\n");
}
if( lperror )
break;
/* update iteration count */
heurdata->nlpiterations += SCIPgetNLPIterations(scip) - nlpiterations;
/* if no LP iterations were performed, we stayed at the same solution -> count this cycling */
lpsolchanged = (SCIPgetNLPIterations(scip) != nlpiterations);
if( lpsolchanged )
ncycles = 0;
else if( !boundschanged ) /* do not count if integral variables have been fixed */
ncycles++;
/* get LP solution status and number of fractional variables, that should be integral */
if( lpsolstat == SCIP_LPSOLSTAT_INFEASIBLE && hardroundingidx != -1 )
{
SCIP_VAR* var;
var = vars[hardroundingidx];
/* round the hard rounded variable to the opposite direction and resolve the LP */
if( hardroundingdir == -1 )
{
SCIPdebugMessage(" -> opposite hard rounding <%s> >= %g\n", SCIPvarGetName(var), hardroundingnewbd + 1.0);
SCIP_CALL( SCIPchgVarUbDive(scip, var, hardroundingoldbd) );
SCIP_CALL( SCIPchgVarLbDive(scip, var, hardroundingnewbd + 1.0) );
}
else
{
SCIPdebugMessage(" -> opposite hard rounding <%s> <= %g\n", SCIPvarGetName(var), hardroundingnewbd - 1.0);
SCIP_CALL( SCIPchgVarLbDive(scip, var, hardroundingoldbd) );
SCIP_CALL( SCIPchgVarUbDive(scip, var, hardroundingnewbd - 1.0) );
}
hardroundingidx = -1;
goto SOLVEAGAIN;
}
if( lpsolstat == SCIP_LPSOLSTAT_OPTIMAL )
nlpcands = SCIPgetNLPBranchCands(scip);
SCIPdebugMessage(" -> lpsolstat=%d, nfrac=%d\n", lpsolstat, nlpcands);
}
SCIPdebugMessage("---> diving finished: lpsolstat = %d, depth %d/%d, LP iter %"SCIP_LONGINT_FORMAT"/%"SCIP_LONGINT_FORMAT"\n",
lpsolstat, divedepth, maxdivedepth, heurdata->nlpiterations, maxnlpiterations);
/* check if a solution has been found */
if( nlpcands == 0 && !lperror && lpsolstat == SCIP_LPSOLSTAT_OPTIMAL )
{
SCIP_Bool success;
/* create solution from diving LP */
SCIP_CALL( SCIPlinkLPSol(scip, heurdata->sol) );
SCIPdebugMessage("rootsoldiving found primal solution: obj=%g\n", SCIPgetSolOrigObj(scip, heurdata->sol));
/* try to add solution to SCIP */
SCIP_CALL( SCIPtrySol(scip, heurdata->sol, FALSE, FALSE, FALSE, FALSE, &success) );
/* check, if solution was feasible and good enough */
if( success )
{
SCIPdebugMessage(" -> solution was feasible and good enough\n");
*result = SCIP_FOUNDSOL;
}
}
/* end diving */
SCIP_CALL( SCIPendDive(scip) );
if( *result == SCIP_FOUNDSOL )
heurdata->nsuccess++;
/* free temporary memory */
SCIPfreeBufferArray(scip, &objchgvals);
SCIPfreeBufferArray(scip, &intvalrounds);
SCIPfreeBufferArray(scip, &softroundings);
SCIPfreeBufferArray(scip, &rootsol);
SCIPdebugMessage("rootsoldiving heuristic finished\n");
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecShifting) /*lint --e{715}*/
{ /*lint --e{715}*/
SCIP_HEURDATA* heurdata;
SCIP_SOL* sol;
SCIP_VAR** lpcands;
SCIP_Real* lpcandssol;
SCIP_ROW** lprows;
SCIP_Real* activities;
SCIP_ROW** violrows;
SCIP_Real* nincreases;
SCIP_Real* ndecreases;
int* violrowpos;
int* nfracsinrow;
SCIP_Real increaseweight;
SCIP_Real obj;
SCIP_Real bestshiftval;
SCIP_Real minobj;
int nlpcands;
int nlprows;
int nvars;
int nfrac;
int nviolrows;
int nprevviolrows;
int minnviolrows;
int nnonimprovingshifts;
int c;
int r;
SCIP_Longint nlps;
SCIP_Longint ncalls;
SCIP_Longint nsolsfound;
SCIP_Longint nnodes;
assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
assert(SCIPhasCurrentNodeLP(scip));
*result = SCIP_DIDNOTRUN;
/* only call heuristic, if an optimal LP solution is at hand */
if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
return SCIP_OKAY;
/* only call heuristic, if the LP objective value is smaller than the cutoff bound */
if( SCIPisGE(scip, SCIPgetLPObjval(scip), SCIPgetCutoffbound(scip)) )
return SCIP_OKAY;
/* get heuristic data */
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
/* don't call heuristic, if we have already processed the current LP solution */
nlps = SCIPgetNLPs(scip);
if( nlps == heurdata->lastlp )
return SCIP_OKAY;
heurdata->lastlp = nlps;
/* don't call heuristic, if it was not successful enough in the past */
ncalls = SCIPheurGetNCalls(heur);
nsolsfound = 10*SCIPheurGetNBestSolsFound(heur) + SCIPheurGetNSolsFound(heur);
nnodes = SCIPgetNNodes(scip);
if( nnodes % ((ncalls/100)/(nsolsfound+1)+1) != 0 )
return SCIP_OKAY;
/* get fractional variables, that should be integral */
/* todo check if heuristic should include implicit integer variables for its calculations */
SCIP_CALL( SCIPgetLPBranchCands(scip, &lpcands, &lpcandssol, NULL, &nlpcands, NULL, NULL) );
nfrac = nlpcands;
/* only call heuristic, if LP solution is fractional */
if( nfrac == 0 )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
/* get LP rows */
SCIP_CALL( SCIPgetLPRowsData(scip, &lprows, &nlprows) );
SCIPdebugMessage("executing shifting heuristic: %d LP rows, %d fractionals\n", nlprows, nfrac);
/* get memory for activities, violated rows, and row violation positions */
nvars = SCIPgetNVars(scip);
SCIP_CALL( SCIPallocBufferArray(scip, &activities, nlprows) );
SCIP_CALL( SCIPallocBufferArray(scip, &violrows, nlprows) );
SCIP_CALL( SCIPallocBufferArray(scip, &violrowpos, nlprows) );
SCIP_CALL( SCIPallocBufferArray(scip, &nfracsinrow, nlprows) );
SCIP_CALL( SCIPallocBufferArray(scip, &nincreases, nvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &ndecreases, nvars) );
BMSclearMemoryArray(nfracsinrow, nlprows);
BMSclearMemoryArray(nincreases, nvars);
BMSclearMemoryArray(ndecreases, nvars);
/* get the activities for all globally valid rows;
* the rows should be feasible, but due to numerical inaccuracies in the LP solver, they can be violated
*/
nviolrows = 0;
for( r = 0; r < nlprows; ++r )
{
SCIP_ROW* row;
row = lprows[r];
assert(SCIProwGetLPPos(row) == r);
if( !SCIProwIsLocal(row) )
{
activities[r] = SCIPgetRowActivity(scip, row);
if( SCIPisFeasLT(scip, activities[r], SCIProwGetLhs(row))
|| SCIPisFeasGT(scip, activities[r], SCIProwGetRhs(row)) )
{
violrows[nviolrows] = row;
violrowpos[r] = nviolrows;
nviolrows++;
}
else
violrowpos[r] = -1;
}
}
/* calc the current number of fractional variables in rows */
for( c = 0; c < nlpcands; ++c )
addFracCounter(nfracsinrow, nlprows, lpcands[c], +1);
/* get the working solution from heuristic's local data */
sol = heurdata->sol;
assert(sol != NULL);
/* copy the current LP solution to the working solution */
SCIP_CALL( SCIPlinkLPSol(scip, sol) );
/* calculate the minimal objective value possible after rounding fractional variables */
minobj = SCIPgetSolTransObj(scip, sol);
assert(minobj < SCIPgetCutoffbound(scip));
for( c = 0; c < nlpcands; ++c )
{
obj = SCIPvarGetObj(lpcands[c]);
bestshiftval = obj > 0.0 ? SCIPfeasFloor(scip, lpcandssol[c]) : SCIPfeasCeil(scip, lpcandssol[c]);
minobj += obj * (bestshiftval - lpcandssol[c]);
}
/* try to shift remaining variables in order to become/stay feasible */
nnonimprovingshifts = 0;
minnviolrows = INT_MAX;
increaseweight = 1.0;
while( (nfrac > 0 || nviolrows > 0) && nnonimprovingshifts < MAXSHIFTINGS )
{
SCIP_VAR* shiftvar;
SCIP_Real oldsolval;
SCIP_Real newsolval;
SCIP_Bool oldsolvalisfrac;
int probindex;
SCIPdebugMessage("shifting heuristic: nfrac=%d, nviolrows=%d, obj=%g (best possible obj: %g), cutoff=%g\n",
nfrac, nviolrows, SCIPgetSolOrigObj(scip, sol), SCIPretransformObj(scip, minobj),
SCIPretransformObj(scip, SCIPgetCutoffbound(scip)));
nprevviolrows = nviolrows;
/* choose next variable to process:
* - if a violated row exists, shift a variable decreasing the violation, that has least impact on other rows
* - otherwise, shift a variable, that has strongest devastating impact on rows in opposite direction
*/
shiftvar = NULL;
oldsolval = 0.0;
newsolval = 0.0;
if( nviolrows > 0 && (nfrac == 0 || nnonimprovingshifts < MAXSHIFTINGS-1) )
{
SCIP_ROW* row;
int rowidx;
int rowpos;
int direction;
rowidx = -1;
rowpos = -1;
row = NULL;
if( nfrac > 0 )
{
for( rowidx = nviolrows-1; rowidx >= 0; --rowidx )
{
row = violrows[rowidx];
rowpos = SCIProwGetLPPos(row);
assert(violrowpos[rowpos] == rowidx);
if( nfracsinrow[rowpos] > 0 )
break;
}
}
if( rowidx == -1 )
{
rowidx = SCIPgetRandomInt(0, nviolrows-1, &heurdata->randseed);
row = violrows[rowidx];
rowpos = SCIProwGetLPPos(row);
assert(0 <= rowpos && rowpos < nlprows);
assert(violrowpos[rowpos] == rowidx);
assert(nfracsinrow[rowpos] == 0);
}
assert(violrowpos[rowpos] == rowidx);
SCIPdebugMessage("shifting heuristic: try to fix violated row <%s>: %g <= %g <= %g\n",
SCIProwGetName(row), SCIProwGetLhs(row), activities[rowpos], SCIProwGetRhs(row));
SCIPdebug( SCIP_CALL( SCIPprintRow(scip, row, NULL) ) );
/* get direction in which activity must be shifted */
assert(SCIPisFeasLT(scip, activities[rowpos], SCIProwGetLhs(row))
|| SCIPisFeasGT(scip, activities[rowpos], SCIProwGetRhs(row)));
direction = SCIPisFeasLT(scip, activities[rowpos], SCIProwGetLhs(row)) ? +1 : -1;
/* search a variable that can shift the activity in the necessary direction */
SCIP_CALL( selectShifting(scip, sol, row, activities[rowpos], direction,
nincreases, ndecreases, increaseweight, &shiftvar, &oldsolval, &newsolval) );
}
if( shiftvar == NULL && nfrac > 0 )
{
SCIPdebugMessage("shifting heuristic: search rounding variable and try to stay feasible\n");
SCIP_CALL( selectEssentialRounding(scip, sol, minobj, lpcands, nlpcands, &shiftvar, &oldsolval, &newsolval) );
}
/* check, whether shifting was possible */
if( shiftvar == NULL || SCIPisEQ(scip, oldsolval, newsolval) )
{
SCIPdebugMessage("shifting heuristic: -> didn't find a shifting variable\n");
break;
}
SCIPdebugMessage("shifting heuristic: -> shift var <%s>[%g,%g], type=%d, oldval=%g, newval=%g, obj=%g\n",
SCIPvarGetName(shiftvar), SCIPvarGetLbGlobal(shiftvar), SCIPvarGetUbGlobal(shiftvar), SCIPvarGetType(shiftvar),
oldsolval, newsolval, SCIPvarGetObj(shiftvar));
/* update row activities of globally valid rows */
SCIP_CALL( updateActivities(scip, activities, violrows, violrowpos, &nviolrows, nlprows,
shiftvar, oldsolval, newsolval) );
if( nviolrows >= nprevviolrows )
nnonimprovingshifts++;
else if( nviolrows < minnviolrows )
{
minnviolrows = nviolrows;
nnonimprovingshifts = 0;
}
/* store new solution value and decrease fractionality counter */
SCIP_CALL( SCIPsetSolVal(scip, sol, shiftvar, newsolval) );
/* update fractionality counter and minimal objective value possible after shifting remaining variables */
oldsolvalisfrac = !SCIPisFeasIntegral(scip, oldsolval)
&& (SCIPvarGetType(shiftvar) == SCIP_VARTYPE_BINARY || SCIPvarGetType(shiftvar) == SCIP_VARTYPE_INTEGER);
obj = SCIPvarGetObj(shiftvar);
if( (SCIPvarGetType(shiftvar) == SCIP_VARTYPE_BINARY || SCIPvarGetType(shiftvar) == SCIP_VARTYPE_INTEGER)
&& oldsolvalisfrac )
{
assert(SCIPisFeasIntegral(scip, newsolval));
nfrac--;
nnonimprovingshifts = 0;
minnviolrows = INT_MAX;
addFracCounter(nfracsinrow, nlprows, shiftvar, -1);
/* the rounding was already calculated into the minobj -> update only if rounding in "wrong" direction */
if( obj > 0.0 && newsolval > oldsolval )
minobj += obj;
else if( obj < 0.0 && newsolval < oldsolval )
minobj -= obj;
}
else
{
/* update minimal possible objective value */
minobj += obj * (newsolval - oldsolval);
}
/* update increase/decrease arrays */
if( !oldsolvalisfrac )
{
probindex = SCIPvarGetProbindex(shiftvar);
assert(0 <= probindex && probindex < nvars);
increaseweight *= WEIGHTFACTOR;
if( newsolval < oldsolval )
ndecreases[probindex] += increaseweight;
else
nincreases[probindex] += increaseweight;
if( increaseweight >= 1e+09 )
{
int i;
for( i = 0; i < nvars; ++i )
{
nincreases[i] /= increaseweight;
ndecreases[i] /= increaseweight;
}
increaseweight = 1.0;
}
}
SCIPdebugMessage("shifting heuristic: -> nfrac=%d, nviolrows=%d, obj=%g (best possible obj: %g)\n",
nfrac, nviolrows, SCIPgetSolOrigObj(scip, sol), SCIPretransformObj(scip, minobj));
}
/* check, if the new solution is feasible */
if( nfrac == 0 && nviolrows == 0 )
{
SCIP_Bool stored;
/* check solution for feasibility, and add it to solution store if possible
* neither integrality nor feasibility of LP rows has to be checked, because this is already
* done in the shifting heuristic itself; however, we better check feasibility of LP rows,
* because of numerical problems with activity updating
*/
SCIP_CALL( SCIPtrySol(scip, sol, FALSE, FALSE, FALSE, TRUE, &stored) );
if( stored )
{
SCIPdebugMessage("found feasible shifted solution:\n");
SCIPdebug( SCIP_CALL( SCIPprintSol(scip, sol, NULL, FALSE) ) );
*result = SCIP_FOUNDSOL;
}
}
/* free memory buffers */
SCIPfreeBufferArray(scip, &ndecreases);
SCIPfreeBufferArray(scip, &nincreases);
SCIPfreeBufferArray(scip, &nfracsinrow);
SCIPfreeBufferArray(scip, &violrowpos);
SCIPfreeBufferArray(scip, &violrows);
SCIPfreeBufferArray(scip, &activities);
return SCIP_OKAY;
}
/** constraint copying method of constraint handler */
static
SCIP_DECL_CONSCOPY(consCopyConjunction)
{ /*lint --e{715}*/
SCIP_CONSDATA* sourcedata;
SCIP_CONS** sourceconss;
SCIP_CONS** conss;
int nconss;
int c;
*valid = TRUE;
sourcedata = SCIPconsGetData(sourcecons);
assert(sourcedata != NULL);
sourceconss = sourcedata->conss;
nconss = sourcedata->nconss;
if( nconss > 0 )
{
assert(sourceconss != NULL);
SCIP_CALL( SCIPallocBufferArray(scip, &conss, nconss) );
/* copy each constraint one by one */
for( c = 0; c < nconss && (*valid); ++c )
{
SCIP_CALL( SCIPgetConsCopy(sourcescip, scip, sourceconss[c], &conss[c], SCIPconsGetHdlr(sourceconss[c]),
varmap, consmap, SCIPconsGetName(sourceconss[c]),
SCIPconsIsInitial(sourceconss[c]), SCIPconsIsSeparated(sourceconss[c]), SCIPconsIsEnforced(sourceconss[c]),
SCIPconsIsChecked(sourceconss[c]), SCIPconsIsPropagated(sourceconss[c]),
SCIPconsIsLocal(sourceconss[c]), SCIPconsIsModifiable(sourceconss[c]),
SCIPconsIsDynamic(sourceconss[c]), SCIPconsIsRemovable(sourceconss[c]), SCIPconsIsStickingAtNode(sourceconss[c]),
global, valid) );
assert(!(*valid) || conss[c] != NULL);
}
if( *valid )
{
if( name == NULL )
{
SCIP_CALL( SCIPcreateConsConjunction(scip, cons, SCIPconsGetName(sourcecons), nconss, conss,
enforce, check, local, modifiable, dynamic) );
}
else
{
SCIP_CALL( SCIPcreateConsConjunction(scip, cons, name, nconss, conss,
enforce, check, local, modifiable, dynamic) );
}
}
/* release the copied constraints */
for( c = (*valid ? c - 1 : c - 2); c >= 0; --c )
{
assert(conss[c] != NULL);
SCIP_CALL( SCIPreleaseCons(scip, &conss[c]) );
}
SCIPfreeBufferArray(scip, &conss);
}
return SCIP_OKAY;
}
/**
* Preprocessing of the graph, using 2 methods in order to find redundant nodes
* that can be deleted and easily colored later.
*
* Foundation of these methods is the computation of a maximum clique C with M nodes.
* After this computation, the following two steps are repeated until no node was deleted
* in the last iteration:
*
* 1: Low-Degree:
* Iterativly delete all nodes v in the graph G with degree d(v) < M ( don't delete nodes of C )
*
* 2: Dominated Neighbourhood:
* If the neighbourhood of one node v is part of the neighbourhood of another node w, v can
* be deleted, since it can later get the same color as w.
*/
static
SCIP_RETCODE preprocessGraph(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_PROBDATA* probdata; /* the problemdata */
SCIP_Bool changed; /* was the graph changed in the last round of preprocessing? */
SCIP_Bool dominates; /* is the neighbourhood of one node dominated by the neigbourhood of another one?*/
int* maxcliquenodes; /* pointer to store nodes of the maximum weight clique */
int nmaxcliquenodes; /* number of nodes in the maximum weight clique */
TCLIQUE_WEIGHT maxcliqueweight; /* weight of the maximum weight clique */
TCLIQUE_STATUS status; /* status of clique-computation */
TCLIQUE_GRAPH* currgraph; /* the current, not preprocessed graph in each step */
int currnnodes; /* the current number of nodes in each step */
int actnewnode; /* the number of nodes yet marked for beeing in the graph in the next round */
int* newnodes; /* the nodes that will be in the graph in the next round */
int* degrees; /* the degrees of the nodes */
int myround; /* the number of the current round */
int ndeletednodes; /* the total number of deleted nodes */
int nnodesdeleteddegreethisround; /* the number of nodes deleted due to low degree in the current round */
int nnodesdeletedneighbourthisround; /* the number of nodes deleted due to neighbourhood in the current round */
int* firstedge; /* pointer for the edges in the graph */
int* lastedge; /* pointer for the edges in the graph */
int i;
int j;
char opt;
assert(scip != NULL);
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
printf("\npreprocessing...\n");
/* copy the old graph */
if( !tcliqueCreate(&currgraph) )
{
SCIPerrorMessage("could not flush the clique graph\n");
return SCIP_ERROR;
}
assert(currgraph != NULL);
if( !tcliqueAddNode(currgraph, tcliqueGetNNodes(probdata->oldgraph)-1, 0) )
{
SCIPerrorMessage("could not add a node to the clique graph\n");
return SCIP_ERROR;
}
for ( i = 0; i < tcliqueGetNNodes(probdata->oldgraph); i++ )
{
/* get adjacent nodes for node i */
firstedge = tcliqueGetFirstAdjedge(probdata->oldgraph, i);
lastedge = tcliqueGetLastAdjedge(probdata->oldgraph, i);
while ( firstedge <= lastedge )
{
if ( *firstedge > i )
{
if( !tcliqueAddEdge(currgraph, i, *firstedge) )
{
SCIPerrorMessage("could not add an edge to the clique graph\n");
return SCIP_ERROR;
}
}
firstedge++;
}
}
if( !tcliqueFlush(currgraph) )
{
SCIPerrorMessage("could not flush the clique graph\n");
return SCIP_ERROR;
}
currnnodes = tcliqueGetNNodes(probdata->oldgraph);
/* get memory for array of deletednodes */
SCIP_CALL( SCIPallocMemoryArray(scip, &(probdata->deletednodes), COLORprobGetOriginalNNodes(scip)) );
/* get memory for array new2oldnodes */
SCIP_CALL( SCIPallocMemoryArray(scip, &(probdata->new2oldnode), COLORprobGetOriginalNNodes(scip)) );
SCIP_CALL( SCIPallocBufferArray(scip, &newnodes, COLORprobGetOriginalNNodes(scip)) );
SCIP_CALL( SCIPallocBufferArray(scip, &maxcliquenodes, COLORprobGetOriginalNNodes(scip)) );
for ( i = 0; i < currnnodes; i++ )
{
/* set weights of the nodes to 1 */
tcliqueChangeWeight(currgraph, i, 1);
/* every node in the graph represents the same node in the original graph */
probdata->new2oldnode[i] = i;
}
/* compute maximum clique */
tcliqueMaxClique(NULL, NULL, NULL, NULL, currgraph, NULL, NULL, maxcliquenodes,
&nmaxcliquenodes, &maxcliqueweight, 0, 0, 50000, 0, INT_MAX, -1, NULL, &status);
opt = ( status == TCLIQUE_OPTIMAL ? ' ' : '*' );
printf("size of the maximum clique: %d%c \n", nmaxcliquenodes, opt);
ndeletednodes = 0;
nnodesdeleteddegreethisround = 1;
nnodesdeletedneighbourthisround = 1;
myround = 0;
/* main loop */
while ( (nnodesdeleteddegreethisround > 0) || (nnodesdeletedneighbourthisround > 0) )
{
myround++;
nnodesdeleteddegreethisround = 0;
nnodesdeletedneighbourthisround = 0;
changed = TRUE;
/* node degree deletion loop */
while ( changed == TRUE )
{
changed = FALSE;
actnewnode = 0;
degrees = tcliqueGetDegrees(currgraph);
for (i = 0; i < currnnodes; i++)
{
/* degree is low, node can be deleted */
if ( (degrees[i] < nmaxcliquenodes )
&& (!COLORprobIsNodeInArray(probdata->new2oldnode[i], maxcliquenodes, nmaxcliquenodes)) )
{
probdata->deletednodes[ndeletednodes] = probdata->new2oldnode[i];
changed = TRUE;
nnodesdeleteddegreethisround++;
ndeletednodes++;
}
/* node will be in the new graph, because degree is not low enought for deletion or it is in the maxClique */
else
{
newnodes[actnewnode] = probdata->new2oldnode[i];
actnewnode++;
}
}
/* at least one node was deletet, create new graph ( tclique doesn't support deletion of nodes) */
if ( changed )
{
assert(actnewnode+ndeletednodes == COLORprobGetOriginalNNodes(scip));
/* create new current graph */
tcliqueFree(&currgraph);
if( !tcliqueCreate(&currgraph) )
{
SCIPerrorMessage("could not create the clique graph\n");
return SCIP_ERROR;
}
assert(currgraph != NULL);
if( !tcliqueAddNode(currgraph, actnewnode-1, 0) )
{
SCIPerrorMessage("could not add a node to the clique graph\n");
return SCIP_ERROR;
}
for ( i = 0; i < actnewnode; i++ )
{
/* get adjacent nodes for node newnodes[i] */
firstedge = tcliqueGetFirstAdjedge(probdata->oldgraph, newnodes[i]);
lastedge = tcliqueGetLastAdjedge(probdata->oldgraph, newnodes[i]);
while ( firstedge <= lastedge )
{
/* try to find a node in newnodes which corresponds
to the node in the old graph, that is the end-node of the edge */
for ( j = i+1; j < actnewnode; j++ )
{
if ( *firstedge == newnodes[j] )
{
if( !tcliqueAddEdge(currgraph, i, j) )
{
SCIPerrorMessage("could not add an edge to the clique graph\n");
return SCIP_ERROR;
}
break;
}
}
firstedge++;
}
}
if( !tcliqueFlush(currgraph) )
{
SCIPerrorMessage("could not flush the clique graph\n");
return SCIP_ERROR;
}
/* update currnnodes */
currnnodes = tcliqueGetNNodes(currgraph);
/* update new2oldnodes */
for ( i = 0; i < actnewnode; i++ )
{
probdata->new2oldnode[i] = newnodes[i];
}
/* set the corresponding old node to -1 for all nodes not in the current graph (only for bug-finding) */
for ( i = actnewnode; i < COLORprobGetOriginalNNodes(scip); i++ )
{
probdata->new2oldnode[i] = -1;
}
}
} /* end node degree deletion loop */
/* set changed to TRUE for getting into the while-loop */
changed = TRUE;
/* loop for finding dominated neighbourhoods */
while ( changed == TRUE )
{
changed = FALSE;
actnewnode = 0;
/* i is the node which is checked for beeing dominated */
for ( i = 0; i < currnnodes; i++ )
{
assert(!COLORprobIsNodeInArray(probdata->new2oldnode[i], probdata->deletednodes, ndeletednodes));
/* i must be not in the clique and not yet deleted */
if ( (!COLORprobIsNodeInArray(probdata->new2oldnode[i], maxcliquenodes, nmaxcliquenodes)) )
{
/* j is the node for which is checked whether it dominates i */
for ( j = 0; j < currnnodes; j++ )
{
/* i must be distinct from j, there must be no edge between i and j,
j may not have been deleted due to degree in the last round */
if ( (j != i) && !tcliqueIsEdge(currgraph, i, j)
&& (!COLORprobIsNodeInArray(probdata->new2oldnode[j], probdata->deletednodes, ndeletednodes)) )
/** @todo only check nodes deleted in the last round */
{
/* check whether nodes adjacent to i are also adjacent to j <-> j dominates i */
dominates = TRUE;
/* get adjacent nodes for node i in currgraph */
firstedge = tcliqueGetFirstAdjedge(currgraph, i);
lastedge = tcliqueGetLastAdjedge(currgraph, i);
while ( firstedge <= lastedge )
{
/* check whether (j,firstedge) is in currgraph, if not, j doesn't dominate i */
if ( !tcliqueIsEdge(currgraph, j, *firstedge) )
{
dominates = FALSE;
break;
}
firstedge++;
}
if ( dominates )
{
probdata->deletednodes[ndeletednodes] = probdata->new2oldnode[i];
changed = TRUE;
ndeletednodes++;
nnodesdeletedneighbourthisround++;
break; /* for j, because we already now that i is dominated and deleted i */
}
}
} /* end for j */
/* if i is dominated by no other node and thus not deleted,
put it into newnodes, so that it is in the next graph */
if ( ndeletednodes == 0 || probdata->deletednodes[ndeletednodes-1] != probdata->new2oldnode[i])
{
newnodes[actnewnode] = probdata->new2oldnode[i];
actnewnode++;
}
}
/* if i is in the maxClique und was thus not deleted,
put it into newnodes, so that it is in the next graph */
else
{
newnodes[actnewnode] = probdata->new2oldnode[i];
actnewnode++;
}
} /*end for i */
/* at least one node was deletet, create new graph ( tclique doesn't support deletion of nodes) */
if ( changed )
{
assert(actnewnode+ndeletednodes == COLORprobGetOriginalNNodes(scip));
/* create new current graph */
tcliqueFree(&currgraph);
if( !tcliqueCreate(&currgraph) )
{
SCIPerrorMessage("could not create the clique graph\n");
return SCIP_ERROR;
}
assert(currgraph != NULL);
if( !tcliqueAddNode(currgraph, actnewnode-1, 0) )
{
SCIPerrorMessage("could not add a node to the clique graph\n");
return SCIP_ERROR;
}
for ( i = 0; i < actnewnode; i++ )
{
/* get adjacent nodes for node newnodes[i] */
firstedge = tcliqueGetFirstAdjedge(probdata->oldgraph, newnodes[i]);
lastedge = tcliqueGetLastAdjedge(probdata->oldgraph, newnodes[i]);
while ( firstedge <= lastedge )
{
/* try to find a node in newnodes which corresponds
to the node in the old graph, that is the end-node of the edge */
for ( j = i+1; j < actnewnode; j++ )
{
if ( *firstedge == newnodes[j] )
{
if( !tcliqueAddEdge(currgraph, i, j) )
{
SCIPerrorMessage("could not add an edge to the clique graph\n");
return SCIP_ERROR;
}
break;
}
}
firstedge++;
}
}
if( !tcliqueFlush(currgraph) )
{
SCIPerrorMessage("could not flush the clique graph\n");
return SCIP_ERROR;
}
/* update currnnodes */
currnnodes = tcliqueGetNNodes(currgraph);
/* update new2oldnodes */
for ( i = 0; i < actnewnode; i++ )
{
probdata->new2oldnode[i] = newnodes[i];
}
/* set the corresponding old node to -1 for all nodes not in the current graph (only for bug-finding) */
for ( i = actnewnode; i < COLORprobGetOriginalNNodes(scip); i++ )
{
probdata->new2oldnode[i] = -1;
}
}
} /* end of loop for finding dominated neighbourhoods */
printf("Round %d of preprocessing:\n", myround);
printf(" deleted low degree vertices: %d\n", nnodesdeleteddegreethisround);
printf(" deleted almost cliques: %d\n", nnodesdeletedneighbourthisround);
}
for ( i = ndeletednodes; i < COLORprobGetOriginalNNodes(scip); i++ )
{
probdata->deletednodes[i] = -1;
}
printf("preprocessing overall deleted vertices: %d\n\n", ndeletednodes);
/* copy preprocessed graph into problem data */
probdata->graph = currgraph;
SCIPfreeBufferArray(scip, &newnodes);
SCIPfreeBufferArray(scip, &maxcliquenodes);
return SCIP_OKAY;
}
/** constraint parsing method of constraint handler */
static
SCIP_DECL_CONSPARSE(consParseConjunction)
{ /*lint --e{715}*/
SCIP_CONS** conss;
int nconss;
int sconss;
char* token;
char* saveptr;
char* nexttokenstart;
char* copystr;
assert(scip != NULL);
assert(conshdlr != NULL);
assert(cons != NULL);
assert(success != NULL);
assert(str != NULL);
assert(name != NULL);
SCIPdebugMessage("parsing conjunction <%s>\n", name);
*success = TRUE;
/* allocate memory for constraint in conjunction, initial size is set to 10 */
nconss = 0;
sconss = 10;
SCIP_CALL( SCIPallocBufferArray(scip, &conss, sconss) );
SCIP_CALL( SCIPduplicateBufferArray(scip, ©str, str, (int)strlen(str)+1) );
/* find '(' at the beginning, string should start with 'conjunction(' */
saveptr = strpbrk(copystr, "("); /*lint !e158*/
if( saveptr == NULL )
{
SCIPdebugMessage("error parsing conjunctive constraint: \"%s\"\n", str);
*success = FALSE;
goto TERMINATE;
}
/* skip '(' */
++saveptr;
/* remember token start position */
nexttokenstart = saveptr;
/* brackets '(' and ')' can exist co we check for them and the constraint delimeter */
saveptr = strpbrk(saveptr, "(,");
/* brackets '(' and ')' can exist in the rest of the string so we need to skip them to find the end of the first
* sub-constraint marked by a ','
*/
if( saveptr != NULL )
{
do
{
int bracketcounter = 0;
if( *saveptr == '(' )
{
do
{
++bracketcounter;
++saveptr;
/* find last ending bracket */
while( bracketcounter > 0 )
{
saveptr = strpbrk(saveptr, "()");
if( saveptr != NULL )
{
if( *saveptr == '(' )
++bracketcounter;
else
--bracketcounter;
++saveptr;
}
else
{
SCIPdebugMessage("error parsing conjunctive constraint: \"%s\"\n", str);
*success = FALSE;
goto TERMINATE;
}
}
saveptr = strpbrk(saveptr, "(,");
}
while( saveptr != NULL && *saveptr == '(' );
}
/* we found a ',' so the end of the first sub-constraint is determined */
if( saveptr != NULL )
{
assert(*saveptr == ',');
/* resize constraint array if necessary */
if( nconss == sconss )
{
sconss = SCIPcalcMemGrowSize(scip, nconss+1);
assert(nconss < sconss);
SCIP_CALL( SCIPreallocBufferArray(scip, &conss, sconss) );
}
assert(saveptr > nexttokenstart);
/* extract token for parsing */
SCIP_CALL( SCIPduplicateBufferArray(scip, &token, nexttokenstart, saveptr - nexttokenstart + 1) );
token[saveptr - nexttokenstart] = '\0';
SCIPdebugMessage("conjunctive parsing token(constraint): %s\n", token);
/* parsing a constraint, part of the conjunction */
SCIP_CALL( SCIPparseCons(scip, &(conss[nconss]), token, initial, separate, enforce, check, propagate, local, modifiable, dynamic, removable, stickingatnode, success) );
SCIPfreeBufferArray(scip, &token);
if( *success )
++nconss;
else
{
SCIPdebugMessage("error parsing conjunctive constraint: \"%s\"\n", str);
goto TERMINATE;
}
/* skip ',' delimeter */
++saveptr;
/* remember token start position */
nexttokenstart = saveptr;
saveptr = strpbrk(saveptr, "(,");
}
}
while( saveptr != NULL );
}
/* find end of conjunction constraint */
saveptr = strrchr(nexttokenstart, ')');
if( saveptr == NULL )
{
SCIPdebugMessage("error parsing conjunctive constraint: \"%s\"\n", str);
*success = FALSE;
goto TERMINATE;
}
/* parse last sub-constraint */
else
{
/* resize constraint array if necessary */
if( nconss == sconss )
{
++sconss;
SCIP_CALL( SCIPreallocBufferArray(scip, &conss, sconss) );
}
assert(saveptr > nexttokenstart);
/* extract token for parsing */
SCIP_CALL( SCIPduplicateBufferArray(scip, &token, nexttokenstart, saveptr - nexttokenstart + 1) );
token[saveptr - nexttokenstart] = '\0';
SCIPdebugMessage("conjunctive parsing token(constraint): %s\n", token);
/* parsing a constraint, part of the conjunction */
SCIP_CALL( SCIPparseCons(scip, &(conss[nconss]), token, initial, separate, enforce, check, propagate, local, modifiable, dynamic, removable, stickingatnode, success) );
if( *success )
++nconss;
SCIPfreeBufferArray(scip, &token);
}
assert(nconss > 0 || !(*success));
/* if parsing sub-constraints was fine, create the conjunctive constraint */
if( *success )
{
/* create conjunctive constraint */
SCIP_CALL( SCIPcreateConsConjunction(scip, cons, name, nconss, conss,
enforce, check, local, modifiable, dynamic) );
}
/* free parsed constraints */
for( --nconss; nconss >= 0; --nconss )
{
SCIP_CALL( SCIPreleaseCons(scip, &conss[nconss]) );
}
TERMINATE:
/* free temporary memory */
SCIPfreeBufferArray(scip, ©str);
SCIPfreeBufferArray(scip, &conss);
return SCIP_OKAY;
}
/** creates and captures an indicator constraint
*
* @note @a binvar is checked to be binary only later. This enables a change of the type in
* procedures reading an instance.
*
* @note the constraint gets captured, hence at one point you have to release it using the method {@link releaseCons()}
*/
JNIEXPORT
jlong JNISCIPCONSINDICATOR(createConsIndicator)(
JNIEnv* env, /**< JNI environment variable */
jobject jobj, /**< JNI class pointer */
jlong jscip, /**< SCIP data structure */
jstring jname, /**< name of constraint */
jlong jbinvar, /**< binary indicator variable (or NULL) */
jint nvars, /**< number of variables in the constraint */
jlongArray jvars, /**< array with variables of constraint entries */
jdoubleArray jvals, /**< values of variables in inequality (or NULL) */
jdouble rhs, /**< rhs of the inequality */
jboolean jinitial, /**< should the LP relaxation of constraint be in the initial LP?
* Usually set to TRUE. Set to FALSE for 'lazy constraints'. */
jboolean jseparate, /**< should the constraint be separated during LP processing?
* Usually set to TRUE. */
jboolean jenforce, /**< should the constraint be enforced during node processing?
* TRUE for model constraints, FALSE for additional, redundant constraints. */
jboolean jcheck, /**< should the constraint be checked for feasibility?
* TRUE for model constraints, FALSE for additional, redundant constraints. */
jboolean jpropagate, /**< should the constraint be propagated during node processing?
* Usually set to TRUE. */
jboolean jlocal, /**< is constraint only valid locally?
* Usually set to FALSE. Has to be set to TRUE, e.g., for branching constraints. */
jboolean jdynamic, /**< is constraint subject to aging?
* Usually set to FALSE. Set to TRUE for own cuts which
* are seperated as constraints. */
jboolean jremovable, /**< 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 jstickingatnode /**< 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;
SCIP_VAR* binvar;
SCIP_VAR** vars;
SCIP_Real* vals;
/* 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();
/* convert JNI pointer into C pointer */
binvar = (SCIP_VAR*) (size_t) jbinvar;
JNISCIP_CALL( SCIPallocBufferArray(scip, &vars, nvars) );
JNISCIP_CALL( SCIPallocBufferArray(scip, &vals, nvars) );
(*env)->GetLongArrayRegion(env, jvars, 0, nvars, (jlong*)(*vars));
(*env)->GetDoubleArrayRegion(env, jvals, 0, nvars, (jdouble*)vals);
JNISCIP_CALL( SCIPcreateConsIndicator(scip, &cons, name, binvar, (int)nvars, vars, vals, (SCIP_Real)rhs,
(SCIP_Bool) jinitial, (SCIP_Bool) jseparate, (SCIP_Bool) jenforce, (SCIP_Bool) jcheck, (SCIP_Bool) jpropagate,
(SCIP_Bool) jlocal, (SCIP_Bool) jdynamic, (SCIP_Bool) jremovable, (SCIP_Bool) jstickingatnode) );
SCIPfreeBufferArray(scip, &vals);
SCIPfreeBufferArray(scip, &vars);
(*env)->ReleaseStringUTFChars(env, jname, name);
return (jlong)(size_t)cons;
}
/** creates a cumulative scheduling problem */
SCIP_RETCODE SCIPcreateSchedulingProblem(
SCIP* scip, /**< SCIP data structure */
const char* problemname, /**< problem name */
const char** jobnames, /**< job names, or NULL */
const char** resourcenames, /**< resource names, or NULL */
int** demands, /**< demand matrix resource job demand */
SCIP_DIGRAPH* precedencegraph, /**< direct graph to store the precedence conditions */
int* durations, /**< array to store the processing for each job */
int* capacities, /**< array to store the different capacities */
int njobs, /**< number of jobs to be parsed */
int nresources /**< number of capacities to be parsed */
)
{
SCIP_VAR** jobs;
SCIP_VAR** vars;
SCIP_VAR* var;
SCIP_CONS* cons;
char name[SCIP_MAXSTRLEN];
int* consdurations;
int* consdemands;
int nvars;
int ubmakespan;
int i;
int j;
int r;
assert( scip != NULL );
assert( njobs >= 0 );
SCIPdebugMessage( "start method SCIPcreateSchedulingSMProblem\n");
/* create SCIP data structure */
SCIP_CALL( SCIPcreateProb(scip, problemname, NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
/* compute a feasible upper bound on the makespan */
ubmakespan = computeUbmakespan(durations, njobs);
ubmakespan *= 100;
/* allocate buffer for jobs and precedence constraints */
SCIP_CALL( SCIPallocBufferArray(scip, &jobs, njobs) );
/* create an activity constraint for each activity */
for( j = 0; j < njobs - 1; ++j ) /* but not for last job which is the makespan (-1) */
{
/* construct variable name */
if( jobnames != NULL )
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "start_%s", jobnames[j]);
else
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "start_%d", j);
/* create integer starting variable */
SCIP_CALL( SCIPcreateVar(scip, &var, name, 0.0, (SCIP_Real)ubmakespan, 0.0, SCIP_VARTYPE_INTEGER,
TRUE, FALSE, NULL, NULL, NULL, NULL, NULL) );
SCIP_CALL( SCIPaddVar(scip, var) );
SCIP_CALL( SCIPmarkDoNotMultaggrVar(scip, var) );
jobs[j] = var;
SCIP_CALL( SCIPreleaseVar(scip, &var) );
}
/* create makespan variable */
SCIP_CALL( SCIPcreateVar(scip, &var, "makespan", 0.0, (SCIP_Real)ubmakespan, 1.0, SCIP_VARTYPE_INTEGER,
TRUE, FALSE, NULL, NULL, NULL, NULL, NULL) );
SCIP_CALL( SCIPaddVar(scip, var) );
SCIP_CALL( SCIPmarkDoNotMultaggrVar(scip, var) );
jobs[njobs-1] = var;
SCIP_CALL( SCIPreleaseVar(scip, &var) );
/* precedence constraints */
for( j = 0; j < njobs - 1; ++j )
{
SCIP_VAR* predvar;
int nsuccessors;
nsuccessors = SCIPdigraphGetNSuccessors(precedencegraph, j);
predvar = jobs[j];
assert(predvar != NULL);
if( nsuccessors > 0 )
{
int* successors;
void** distances;
successors = SCIPdigraphGetSuccessors(precedencegraph, j);
distances = SCIPdigraphGetSuccessorsDatas(precedencegraph, j);
for( i = 0; i < nsuccessors; ++i )
{
SCIP_VAR* succvar;
int distance;
succvar = jobs[successors[i]];
assert(succvar != NULL);
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "precedences_(%d,%d)", j, successors[i]);
distance = (int)(size_t)distances[i];
if( distance == INT_MAX )
distance = durations[j];
SCIP_CALL( SCIPcreateConsVarbound(scip, &cons, name, predvar, succvar, -1.0,
-SCIPinfinity(scip), -distance,
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE) );
SCIP_CALL( SCIPaddCons(scip, cons) );
SCIP_CALL( SCIPreleaseCons(scip, &cons) );
}
}
else
{
/* add precedence constraints for those jobs without successor */
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "precedences_(%d,%d)", j, njobs);
SCIP_CALL( SCIPcreateConsVarbound(scip, &cons, name, predvar, jobs[njobs-1], -1.0,
-SCIPinfinity(scip), -durations[j],
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE) );
SCIP_CALL( SCIPaddCons(scip, cons) );
SCIP_CALL( SCIPreleaseCons(scip, &cons) );
}
}
SCIP_CALL( SCIPallocBufferArray(scip, &vars, njobs) );
SCIP_CALL( SCIPallocBufferArray(scip, &consdemands, njobs) );
SCIP_CALL( SCIPallocBufferArray(scip, &consdurations, njobs) );
/* create resource constraints */
for( r = 0; r < nresources; ++r )
{
nvars = 0;
for( j = 0; j < njobs; ++j ) /* also makespan constraint! */
{
if( demands[j][r] > 0 )
{
vars[nvars] = jobs[j];
consdemands[nvars] = demands[j][r];
consdurations[nvars] = durations[j];
nvars++;
}
}
if( nvars > 0 )
{
/* construct constraint name */
if( resourcenames != NULL )
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "R%s", resourcenames[r]);
else
(void)SCIPsnprintf(name, SCIP_MAXSTRLEN, "R%d", r);
SCIP_CALL( SCIPcreateConsCumulative(scip, &cons, name,
nvars, vars, consdurations, consdemands, capacities[r],
TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE) );
SCIP_CALL( SCIPaddCons(scip, cons) );
SCIP_CALL( SCIPreleaseCons(scip, &cons) );
}
}
/* initialize the problem specific heuristic */
SCIP_CALL( SCIPinitializeHeurListScheduling(scip, precedencegraph, jobs,
durations, demands, capacities, njobs, nresources) );
/* free buffer array */
SCIPfreeBufferArray(scip, &consdurations);
SCIPfreeBufferArray(scip, &consdemands);
SCIPfreeBufferArray(scip, &vars);
SCIPfreeBufferArray(scip, &jobs);
return SCIP_OKAY;
}
/** prints given linear constraint information in PPM format to file stream */
static
SCIP_RETCODE printLinearCons(
SCIP* scip, /**< SCIP data structure */
FILE* file, /**< output file (or NULL for standard output) */
SCIP_READERDATA* readerdata, /**< information for reader */
SCIP_VAR** vars, /**< array of variables */
SCIP_Real* vals, /**< array of coefficients values (or NULL if all coefficient values are 1) */
int nvars, /**< number of variables */
int ncompletevars, /**< number of variables in whole problem */
SCIP_Bool transformed, /**< transformed constraint? */
SCIP_Real* maxcoef, /**< maximal coefficient */
SCIP_Bool printbool /**< print row or calculate maximum coefficient */
)
{
int v;
SCIP_VAR** activevars;
SCIP_Real* activevals;
int nactivevars;
SCIP_Real activeconstant = 0.0;
assert( scip != NULL );
assert( vars != NULL );
assert( nvars > 0 );
assert( readerdata != NULL );
/* duplicate variable and value array */
nactivevars = nvars;
SCIP_CALL( SCIPduplicateBufferArray(scip, &activevars, vars, nactivevars ) );
if( vals != NULL )
{
SCIP_CALL( SCIPduplicateBufferArray(scip, &activevals, vals, nactivevars ) );
}
else
{
SCIP_CALL( SCIPallocBufferArray(scip, &activevals, nactivevars) );
for( v = 0; v < nactivevars; ++v )
activevals[v] = 1.0;
}
/* retransform given variables to active variables */
SCIP_CALL( getActiveVariables(scip, activevars, activevals, &nactivevars, &activeconstant, transformed) );
if(!readerdata->rgb_relativ)
{
if(!printbool)
for(v = 0; v < nactivevars; ++v)
{
if( REALABS(activevals[v]) > *maxcoef)
*maxcoef = REALABS(activevals[v]);
}
else
{
assert (*maxcoef > 0);
/* print constraint */
printRow(scip, file, readerdata, activevars, activevals, nactivevars, ncompletevars, *maxcoef);
}
}
else
{
/* print constraint */
printRow(scip, file, readerdata, activevars, activevals, nactivevars, ncompletevars, *maxcoef);
}
/* free buffer arrays */
SCIPfreeBufferArray(scip, &activevars);
SCIPfreeBufferArray(scip, &activevals);
return SCIP_OKAY;
}
static
SCIP_DECL_BRANCHEXECPS(branchExecpsMyfullstrong)
{ /*lint --e{715}*/
SCIP_PROBDATA* probdata;
SCIP_VAR** cands;
int ncands;
SCIP_NODE* childnode_0; /* z_j = 0 */
SCIP_NODE* childnode_1; /* z_j = 1 */
/* probdata */
int p;
int ndep;
SCIP_VAR** var_z; /* [p] 01 variables */
/* "_" means the matrix for blas */
SCIP_Real* orig_Q_; /* [p*p] <- (X^t) X */
SCIP_Real* orig_q; /* [p] <- (X^t) y */
SCIP_Real r;
int* Mdep; /* [ndep] */
int* groupX; /* [ndep*p] */
int dim;
SCIP_Real RSS; /* residual sum of square */
SCIP_Real RSS_new;
SCIP_Real* a; /* [dim] */
int ublb;
int *Branchz; /* [3*p] */
int *Branchz_new; /* [3*p] */
SCIP_Real* Q_; /* sub matrix of orig_Q_ */
SCIP_Real* Xy; /* sub vector of orig_q */
int* list; /* list of candidate variables */
int i,j,t,memo,ct;
int ind;
int dpv;
#if MYPARA_LOG
printf("[myfullstrong brnaching]");
Longline();
#endif
/* get branching rule data */
/*
SCIP_BRANCHRULEDATA* branchruledata;
branchruledata = SCIPbranchruleGetData(branchrule);
assert(branchruledata != NULL);
*/
/* get problem data*/
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
p = SCIPprobdataGetNexvars(probdata);
ndep = SCIPprobdataGetNdep(probdata);
orig_Q_ = SCIPprobdataGetQ(probdata);
orig_q = SCIPprobdataGetq(probdata);
r = SCIPprobdataGetr(probdata);
var_z = SCIPprobdataGetVars_z(probdata);
if( ndep ){
Mdep = SCIPprobdataGetMdep(probdata);
groupX = SCIPprobdataGetgroupX(probdata);
}else{
Mdep = NULL;
groupX = NULL;
}
/* alloc */
SCIP_CALL( SCIPallocBufferArray(scip, &list, p));
SCIP_CALL( SCIPallocBufferArray(scip, &Branchz, 3*p));
SCIP_CALL( SCIPallocBufferArray(scip, &Branchz_new, 3*p));
GenerateZeroVecInt( p, list);
GenerateZeroVecInt( 3*p, Branchz);
/* get pseudo candidates (non-fixed integer variables) */
SCIP_CALL( SCIPgetPseudoBranchCands(scip, &cands, NULL, &ncands) );
for(i=0; i<ncands; i++){
for(j=0; j<p; j++){
if( cands[i]==var_z[j] ){
list[j] = 1;
break;
}
}
}
#if MYPARA_LOG
printf("list:");
printintv( p, list);
#endif
/* get branching info */
for(i=0; i<p; ++i){
ublb = SCIPround(scip, SCIPcomputeVarUbLocal(scip, var_z[i])
+ SCIPcomputeVarLbLocal(scip, var_z[i]));
*(Branchz+(ublb*p)+i) = 1;
}
#if MYPARA_LOG
for(i=0; i<3; i++){
for(j=0; j<p; j++){
printf("%d, ", *(Branchz+(i*p)+j));
}
newline();
}
#endif
RSS = -1.0;
ind = -1;
for(i=0; i<p; i++){
/* copy */
for(j=0; j<(3*p); j++){
Branchz_new[j] = Branchz[j];
}
/*
* solve
* Q a = Xy
*/
if( list[i] == 1 ){
Branchz_new[i] = 1;
Branchz_new[p+i] = 0;
if( ndep ){
for(t=0; t<ndep; t++){
memo = -1;
for(j=0; j<p; j++){
if( *(groupX+(t*p)+j)==1 ){
if( Branchz_new[j]==1 ) break;
if( Branchz_new[p+j]==1 ) memo=j;
if( j==Mdep[t] ){
if( memo==-1 ){
printf("error in branch_myfullstrong.c\n");
stop();
}
*(Branchz_new+p+memo) = 0;
*(Branchz_new+memo) = 1;
break;
}
}
}
}
}
dim = p - sumint( &Branchz_new[0], p);
/* alloc */
SCIP_CALL( SCIPallocBufferArray( scip, &a, dim));
SCIP_CALL( SCIPallocBufferArray( scip, &Q_, dim*dim));
SCIP_CALL( SCIPallocBufferArray( scip, &Xy, dim));
/* generate Q and Xy */
/* Q */
ct = 0;
for(j=0; j<p; j++){
if( (Branchz_new[j]==0) && (j != i) ){
for(t=0; t<p; t++){
if( (Branchz_new[t]==0) && (t != i ) ){
Q_[ct++] = mat_( orig_Q_, p, j, t);
}
}
}
}
if( ct != (dim*dim) ){
printf("error in branch_myfullstrong.c\n");
stop();
}
/* Xy */
ct = 0;
for(j=0; j<p; j++){
if( (Branchz_new[j]==0) && (j != i) ){
Xy[ct++] = orig_q[j];
}
}
if( ct != dim ){
printf("error in branch_myfullstrong.c\n");
stop();
}
dpv = _dposv_( Q_, Xy, dim, a);
if( dpv == 0 ){
/* test */
RSS_new = RSSvalue( dim, a, Xy, r);
if( RSS_new > RSS ){
RSS = RSS_new;
ind = i;
}
#if MYPARA_LOG
printf("%d: RSS = %f\n", i, RSS_new);
#endif
}
/* free */
SCIPfreeBufferArray(scip, &Q_);
SCIPfreeBufferArray(scip, &Xy);
SCIPfreeBufferArray(scip, &a);
}
}
#if MYPARA_LOG
printf("max->%dth var. \n", ind);
#endif
if( ind == -1 ){
/* free */
SCIPfreeBufferArray(scip, &list);
SCIPfreeBufferArray(scip, &Branchz);
SCIPfreeBufferArray(scip, &Branchz_new);
*result = SCIP_DIDNOTRUN;
return SCIP_OKAY;
}
SCIP_CALL( SCIPbranchVar( scip, var_z[ind], &childnode_0, NULL, &childnode_1));
/* free */
SCIPfreeBufferArray(scip, &list);
SCIPfreeBufferArray(scip, &Branchz);
SCIPfreeBufferArray(scip, &Branchz_new);
*result = SCIP_BRANCHED;
return SCIP_OKAY;
}
/** writes problem to file */
SCIP_RETCODE SCIPwritePpm(
SCIP* scip, /**< SCIP data structure */
FILE* file, /**< output file, or NULL if standard output should be used */
const char* name, /**< problem name */
SCIP_READERDATA* readerdata, /**< information for reader */
SCIP_Bool transformed, /**< TRUE iff problem is the transformed problem */
SCIP_VAR** vars, /**< array with active variables ordered binary, integer, implicit, continuous */
int nvars, /**< number of active variables in the problem */
SCIP_CONS** conss, /**< array with constraints of the problem */
int nconss, /**< number of constraints in the problem */
SCIP_RESULT* result /**< pointer to store the result of the file writing call */
)
{ /*lint --e{715}*/
int c;
int v;
int i;
int linecnt;
char linebuffer[PPM_MAX_LINELEN];
SCIP_CONSHDLR* conshdlr;
const char* conshdlrname;
SCIP_CONS* cons;
SCIP_VAR** consvars;
SCIP_Real* consvals;
int nconsvars;
int i_max = 1;
SCIP_Real maxcoef = 0;
SCIP_Bool printbool = FALSE;
assert( scip != NULL );
assert(readerdata != NULL);
/* print statistics as comment to file */
if(readerdata->rgb_ascii)
SCIPinfoMessage(scip, file, "P6\n");
else
SCIPinfoMessage(scip, file, "P3\n");
SCIPinfoMessage(scip, file, "# %s\n", name);
SCIPinfoMessage(scip, file, "%d %d\n", nvars, nconss);
SCIPinfoMessage(scip, file, "255\n");
clearLine(linebuffer, &linecnt);
if(!(readerdata->rgb_relativ))
{
i_max = 2;
}
for(i = 0; i < i_max; ++i)
{
if(i)
{
printbool = TRUE;
SCIPdebugPrintf("Maximal coefficient = %g\n", maxcoef);
}
for(c = 0; c < nconss; ++c)
{
cons = conss[c];
assert( cons != NULL);
/* in case the transformed is written only constraint are posted which are enabled in the current node */
assert(!transformed || SCIPconsIsEnabled(cons));
conshdlr = SCIPconsGetHdlr(cons);
assert( conshdlr != NULL );
conshdlrname = SCIPconshdlrGetName(conshdlr);
assert( transformed == SCIPconsIsTransformed(cons) );
if( strcmp(conshdlrname, "linear") == 0 )
{
consvars = SCIPgetVarsLinear(scip, cons);
nconsvars = SCIPgetNVarsLinear(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( printLinearCons(scip, file, readerdata, consvars, SCIPgetValsLinear(scip, cons),
nconsvars, nvars, transformed, &maxcoef, printbool) );
}
}
else if( strcmp(conshdlrname, "setppc") == 0 )
{
consvars = SCIPgetVarsSetppc(scip, cons);
nconsvars = SCIPgetNVarsSetppc(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( printLinearCons(scip, file, readerdata, consvars, NULL,
nconsvars, nvars, transformed, &maxcoef, printbool) );
}
}
else if( strcmp(conshdlrname, "logicor") == 0 )
{
consvars = SCIPgetVarsLogicor(scip, cons);
nconsvars = SCIPgetNVarsLogicor(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( printLinearCons(scip, file, readerdata, consvars, NULL,
nconsvars, nvars, transformed, &maxcoef, printbool) );
}
}
else if( strcmp(conshdlrname, "knapsack") == 0 )
{
SCIP_Longint* weights;
consvars = SCIPgetVarsKnapsack(scip, cons);
nconsvars = SCIPgetNVarsKnapsack(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
/* copy Longint array to SCIP_Real array */
weights = SCIPgetWeightsKnapsack(scip, cons);
SCIP_CALL( SCIPallocBufferArray(scip, &consvals, nconsvars) );
for( v = 0; v < nconsvars; ++v )
consvals[v] = (SCIP_Real)weights[v];
if( nconsvars > 0 )
{
SCIP_CALL( printLinearCons(scip, file, readerdata, consvars, consvals, nconsvars, nvars, transformed, &maxcoef, printbool) );
}
SCIPfreeBufferArray(scip, &consvals);
}
else if( strcmp(conshdlrname, "varbound") == 0 )
{
SCIP_CALL( SCIPallocBufferArray(scip, &consvars, 2) );
SCIP_CALL( SCIPallocBufferArray(scip, &consvals, 2) );
consvars[0] = SCIPgetVarVarbound(scip, cons);
consvars[1] = SCIPgetVbdvarVarbound(scip, cons);
consvals[0] = 1.0;
consvals[1] = SCIPgetVbdcoefVarbound(scip, cons);
SCIP_CALL( printLinearCons(scip, file, readerdata, consvars, consvals, 2, nvars, transformed, &maxcoef, printbool) );
SCIPfreeBufferArray(scip, &consvars);
SCIPfreeBufferArray(scip, &consvals);
}
else
{
SCIPwarningMessage(scip, "constraint handler <%s> cannot print requested format\n", conshdlrname );
SCIPinfoMessage(scip, file, "\\ ");
SCIP_CALL( SCIPprintCons(scip, cons, file) );
SCIPinfoMessage(scip, file, ";\n");
}
}
}
*result = SCIP_SUCCESS;
return SCIP_OKAY;
}
/** arbitrary primal solution separation method of separator */
static
SCIP_DECL_SEPAEXECSOL(sepaExecsolImpliedbounds)
{ /*lint --e{715}*/
SCIP_VAR** vars;
SCIP_VAR** fracvars;
SCIP_Real* solvals;
SCIP_Real* fracvals;
SCIP_Bool cutoff;
int nvars;
int nbinvars;
int nfracs;
int ncuts;
int i;
assert(sepa != NULL);
assert(scip != NULL);
*result = SCIP_DIDNOTRUN;
/* gets active problem variables */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, &nbinvars, NULL, NULL, NULL) );
if( nbinvars == 0 )
return SCIP_OKAY;
/* get solution values for all variables */
SCIP_CALL( SCIPallocBufferArray(scip, &solvals, nvars) );
SCIP_CALL( SCIPgetSolVals(scip, sol, nvars, vars, solvals) );
/* get binary problem variables that are fractional in given solution */
SCIP_CALL( SCIPallocBufferArray(scip, &fracvars, nbinvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &fracvals, nbinvars) );
nfracs = 0;
for( i = 0; i < nbinvars; ++i )
{
if( !SCIPisFeasIntegral(scip, solvals[i]) )
{
fracvars[nfracs] = vars[i];
fracvals[nfracs] = solvals[i];
nfracs++;
}
}
/* call the cut separation */
ncuts = 0;
cutoff = FALSE;
if( nfracs > 0 )
{
SCIP_CALL( separateCuts(scip, sepa, sol, solvals, fracvars, fracvals, nfracs, &cutoff, &ncuts) );
}
/* adjust result code */
if ( cutoff )
*result = SCIP_CUTOFF;
else if ( ncuts > 0 )
*result = SCIP_SEPARATED;
else
*result = SCIP_DIDNOTFIND;
/* free temporary memory */
SCIPfreeBufferArray(scip, &fracvals);
SCIPfreeBufferArray(scip, &fracvars);
SCIPfreeBufferArray(scip, &solvals);
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecLocalbranching)
{ /*lint --e{715}*/
SCIP_Longint maxnnodes; /* maximum number of subnodes */
SCIP_Longint nsubnodes; /* nodelimit for subscip */
SCIP_HEURDATA* heurdata;
SCIP* subscip; /* the subproblem created by localbranching */
SCIP_VAR** subvars; /* subproblem's variables */
SCIP_SOL* bestsol; /* best solution so far */
SCIP_EVENTHDLR* eventhdlr; /* event handler for LP events */
SCIP_Real timelimit; /* timelimit for subscip (equals remaining time of scip) */
SCIP_Real cutoff; /* objective cutoff for the subproblem */
SCIP_Real upperbound;
SCIP_Real memorylimit;
SCIP_HASHMAP* varmapfw; /* mapping of SCIP variables to sub-SCIP variables */
SCIP_VAR** vars;
int nvars;
int i;
SCIP_Bool success;
SCIP_RETCODE retcode;
assert(heur != NULL);
assert(scip != NULL);
assert(result != NULL);
*result = SCIP_DIDNOTRUN;
/* get heuristic's data */
heurdata = SCIPheurGetData(heur);
assert( heurdata != NULL );
/* there should be enough binary variables that a local branching constraint makes sense */
if( SCIPgetNBinVars(scip) < 2*heurdata->neighborhoodsize )
return SCIP_OKAY;
*result = SCIP_DELAYED;
/* only call heuristic, if an IP solution is at hand */
if( SCIPgetNSols(scip) <= 0 )
return SCIP_OKAY;
bestsol = SCIPgetBestSol(scip);
assert(bestsol != NULL);
/* only call heuristic, if the best solution comes from transformed problem */
if( SCIPsolIsOriginal(bestsol) )
return SCIP_OKAY;
/* only call heuristic, if enough nodes were processed since last incumbent */
if( SCIPgetNNodes(scip) - SCIPgetSolNodenum(scip, bestsol) < heurdata->nwaitingnodes)
return SCIP_OKAY;
/* only call heuristic, if the best solution does not come from trivial heuristic */
if( SCIPsolGetHeur(bestsol) != NULL && strcmp(SCIPheurGetName(SCIPsolGetHeur(bestsol)), "trivial") == 0 )
return SCIP_OKAY;
/* reset neighborhood and minnodes, if new solution was found */
if( heurdata->lastsol != bestsol )
{
heurdata->curneighborhoodsize = heurdata->neighborhoodsize;
heurdata->curminnodes = heurdata->minnodes;
heurdata->emptyneighborhoodsize = 0;
heurdata->callstatus = EXECUTE;
heurdata->lastsol = bestsol;
}
/* if no new solution was found and local branching also seems to fail, just keep on waiting */
if( heurdata->callstatus == WAITFORNEWSOL )
return SCIP_OKAY;
*result = SCIP_DIDNOTRUN;
/* calculate the maximal number of branching nodes until heuristic is aborted */
maxnnodes = (SCIP_Longint)(heurdata->nodesquot * SCIPgetNNodes(scip));
/* reward local branching if it succeeded often */
maxnnodes = (SCIP_Longint)(maxnnodes * (1.0 + 2.0*(SCIPheurGetNBestSolsFound(heur)+1.0)/(SCIPheurGetNCalls(heur)+1.0)));
maxnnodes -= 100 * SCIPheurGetNCalls(heur); /* count the setup costs for the sub-MIP as 100 nodes */
maxnnodes += heurdata->nodesofs;
/* determine the node limit for the current process */
nsubnodes = maxnnodes - heurdata->usednodes;
nsubnodes = MIN(nsubnodes, heurdata->maxnodes);
/* check whether we have enough nodes left to call sub problem solving */
if( nsubnodes < heurdata->curminnodes )
return SCIP_OKAY;
if( SCIPisStopped(scip) )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
SCIPdebugMessage("running localbranching heuristic ...\n");
/* get the data of the variables and the best solution */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
/* initializing the subproblem */
SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
SCIP_CALL( SCIPcreate(&subscip) );
/* create the variable mapping hash map */
SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), SCIPcalcHashtableSize(5 * nvars)) );
success = FALSE;
eventhdlr = NULL;
if( heurdata->uselprows )
{
char probname[SCIP_MAXSTRLEN];
/* copy all plugins */
SCIP_CALL( SCIPincludeDefaultPlugins(subscip) );
/* get name of the original problem and add the string "_localbranchsub" */
(void) SCIPsnprintf(probname, SCIP_MAXSTRLEN, "%s_localbranchsub", SCIPgetProbName(scip));
/* create the subproblem */
SCIP_CALL( SCIPcreateProb(subscip, probname, NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
/* copy all variables */
SCIP_CALL( SCIPcopyVars(scip, subscip, varmapfw, NULL, TRUE) );
}
else
{
SCIP_CALL( SCIPcopy(scip, subscip, varmapfw, NULL, "localbranchsub", TRUE, FALSE, TRUE, &success) );
if( heurdata->copycuts )
{
/* copies all active cuts from cutpool of sourcescip to linear constraints in targetscip */
SCIP_CALL( SCIPcopyCuts(scip, subscip, varmapfw, NULL, TRUE, NULL) );
}
/* create event handler for LP events */
SCIP_CALL( SCIPincludeEventhdlrBasic(subscip, &eventhdlr, EVENTHDLR_NAME, EVENTHDLR_DESC, eventExecLocalbranching, NULL) );
if( eventhdlr == NULL )
{
SCIPerrorMessage("event handler for "HEUR_NAME" heuristic not found.\n");
return SCIP_PLUGINNOTFOUND;
}
}
SCIPdebugMessage("Copying the plugins was %ssuccessful.\n", success ? "" : "not ");
for (i = 0; i < nvars; ++i)
subvars[i] = (SCIP_VAR*) SCIPhashmapGetImage(varmapfw, vars[i]);
/* free hash map */
SCIPhashmapFree(&varmapfw);
/* if the subproblem could not be created, free memory and return */
if( !success )
{
*result = SCIP_DIDNOTRUN;
goto TERMINATE;
}
/* do not abort subproblem on CTRL-C */
SCIP_CALL( SCIPsetBoolParam(subscip, "misc/catchctrlc", FALSE) );
#ifndef SCIP_DEBUG
/* disable output to console */
SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 0) );
#endif
/* check whether there is enough time and memory left */
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 )
goto TERMINATE;
/* set limits for the subproblem */
heurdata->nodelimit = nsubnodes;
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nsubnodes) );
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/stallnodes", MAX(10, nsubnodes/10)) );
SCIP_CALL( SCIPsetIntParam(subscip, "limits/bestsol", 3) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/time", timelimit) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/memory", memorylimit) );
/* forbid recursive call of heuristics and separators solving subMIPs */
SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );
/* disable cutting plane separation */
SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );
/* disable expensive presolving */
SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );
/* use best estimate node selection */
if( SCIPfindNodesel(subscip, "estimate") != NULL && !SCIPisParamFixed(subscip, "nodeselection/estimate/stdpriority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/estimate/stdpriority", INT_MAX/4) );
}
/* use inference branching */
if( SCIPfindBranchrule(subscip, "inference") != NULL && !SCIPisParamFixed(subscip, "branching/inference/priority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "branching/inference/priority", INT_MAX/4) );
}
/* disable conflict analysis */
if( !SCIPisParamFixed(subscip, "conflict/useprop") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useprop", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/useinflp") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useinflp", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/useboundlp") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useboundlp", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/usesb") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usesb", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/usepseudo") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usepseudo", FALSE) );
}
/* 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", 500) );
}
/* copy the original problem and add the local branching constraint */
if( heurdata->uselprows )
{
SCIP_CALL( createSubproblem(scip, subscip, subvars) );
}
SCIP_CALL( addLocalBranchingConstraint(scip, subscip, subvars, heurdata) );
/* add an objective cutoff */
cutoff = SCIPinfinity(scip);
assert( !SCIPisInfinity(scip,SCIPgetUpperbound(scip)) );
upperbound = SCIPgetUpperbound(scip) - SCIPsumepsilon(scip);
if( !SCIPisInfinity(scip,-1.0*SCIPgetLowerbound(scip)) )
{
cutoff = (1-heurdata->minimprove)*SCIPgetUpperbound(scip) + heurdata->minimprove*SCIPgetLowerbound(scip);
}
else
{
if( SCIPgetUpperbound ( scip ) >= 0 )
cutoff = ( 1 - heurdata->minimprove ) * SCIPgetUpperbound ( scip );
else
cutoff = ( 1 + heurdata->minimprove ) * SCIPgetUpperbound ( scip );
}
cutoff = MIN(upperbound, cutoff );
SCIP_CALL( SCIPsetObjlimit(subscip, cutoff) );
/* catch LP events of sub-SCIP */
if( !heurdata->uselprows )
{
assert(eventhdlr != NULL);
SCIP_CALL( SCIPtransformProb(subscip) );
SCIP_CALL( SCIPcatchEvent(subscip, SCIP_EVENTTYPE_LPSOLVED, eventhdlr, (SCIP_EVENTDATA*) heurdata, NULL) );
}
/* solve the subproblem */
SCIPdebugMessage("solving local branching subproblem with neighborhoodsize %d and maxnodes %"SCIP_LONGINT_FORMAT"\n",
heurdata->curneighborhoodsize, nsubnodes);
retcode = SCIPsolve(subscip);
/* drop LP events of sub-SCIP */
if( !heurdata->uselprows )
{
assert(eventhdlr != NULL);
SCIP_CALL( SCIPdropEvent(subscip, SCIP_EVENTTYPE_LPSOLVED, 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 local branching heuristic; sub-SCIP terminated with code <%d>\n",retcode);
}
/* print solving statistics of subproblem if we are in SCIP's debug mode */
SCIPdebug( SCIP_CALL( SCIPprintStatistics(subscip, NULL) ) );
heurdata->usednodes += SCIPgetNNodes(subscip);
SCIPdebugMessage("local branching used %"SCIP_LONGINT_FORMAT"/%"SCIP_LONGINT_FORMAT" nodes\n",
SCIPgetNNodes(subscip), nsubnodes);
/* check, whether a solution was found */
if( SCIPgetNSols(subscip) > 0 )
{
SCIP_SOL** subsols;
int nsubsols;
/* 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; ++i )
{
SCIP_CALL( createNewSol(scip, subscip, subvars, heur, subsols[i], &success) );
}
if( success )
{
SCIPdebugMessage("-> accepted solution of value %g\n", SCIPgetSolOrigObj(subscip, subsols[i]));
*result = SCIP_FOUNDSOL;
}
}
/* check the status of the sub-MIP */
switch( SCIPgetStatus(subscip) )
{
case SCIP_STATUS_OPTIMAL:
case SCIP_STATUS_BESTSOLLIMIT:
heurdata->callstatus = WAITFORNEWSOL; /* new solution will immediately be installed at next call */
SCIPdebugMessage(" -> found new solution\n");
break;
case SCIP_STATUS_NODELIMIT:
case SCIP_STATUS_STALLNODELIMIT:
case SCIP_STATUS_TOTALNODELIMIT:
heurdata->callstatus = EXECUTE;
heurdata->curneighborhoodsize = (heurdata->emptyneighborhoodsize + heurdata->curneighborhoodsize)/2;
heurdata->curminnodes *= 2;
SCIPdebugMessage(" -> node limit reached: reduced neighborhood to %d, increased minnodes to %d\n",
heurdata->curneighborhoodsize, heurdata->curminnodes);
if( heurdata->curneighborhoodsize <= heurdata->emptyneighborhoodsize )
{
heurdata->callstatus = WAITFORNEWSOL;
SCIPdebugMessage(" -> new neighborhood was already proven to be empty: wait for new solution\n");
}
break;
case SCIP_STATUS_INFEASIBLE:
case SCIP_STATUS_INFORUNBD:
heurdata->emptyneighborhoodsize = heurdata->curneighborhoodsize;
heurdata->curneighborhoodsize += heurdata->curneighborhoodsize/2;
heurdata->curneighborhoodsize = MAX(heurdata->curneighborhoodsize, heurdata->emptyneighborhoodsize + 2);
heurdata->callstatus = EXECUTE;
SCIPdebugMessage(" -> neighborhood is empty: increased neighborhood to %d\n", heurdata->curneighborhoodsize);
break;
case SCIP_STATUS_UNKNOWN:
case SCIP_STATUS_USERINTERRUPT:
case SCIP_STATUS_TIMELIMIT:
case SCIP_STATUS_MEMLIMIT:
case SCIP_STATUS_GAPLIMIT:
case SCIP_STATUS_SOLLIMIT:
case SCIP_STATUS_UNBOUNDED:
default:
heurdata->callstatus = WAITFORNEWSOL;
SCIPdebugMessage(" -> unexpected sub-MIP status <%d>: waiting for new solution\n", SCIPgetStatus(subscip));
break;
}
TERMINATE:
/* free subproblem */
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/** LP solution separation method of separator */
static
SCIP_DECL_SEPAEXECLP(sepaExeclpRapidlearning)
{/*lint --e{715}*/
SCIP* subscip; /* the subproblem created by rapid learning */
SCIP_SEPADATA* sepadata; /* separator's private data */
SCIP_VAR** vars; /* original problem's variables */
SCIP_VAR** subvars; /* subproblem's variables */
SCIP_HASHMAP* varmapfw; /* mapping of SCIP variables to sub-SCIP variables */
SCIP_HASHMAP* varmapbw; /* mapping of sub-SCIP variables to SCIP variables */
SCIP_CONSHDLR** conshdlrs; /* array of constraint handler's that might that might obtain conflicts */
int* oldnconss; /* number of constraints without rapid learning conflicts */
SCIP_Longint nodelimit; /* node limit for the subproblem */
SCIP_Real timelimit; /* time limit for the subproblem */
SCIP_Real memorylimit; /* memory limit for the subproblem */
int nconshdlrs; /* size of conshdlr and oldnconss array */
int nfixedvars; /* number of variables that could be fixed by rapid learning */
int nvars; /* number of variables */
int restartnum; /* maximal number of conflicts that should be created */
int i; /* counter */
SCIP_Bool success; /* was problem creation / copying constraint successful? */
SCIP_RETCODE retcode; /* used for catching sub-SCIP errors in debug mode */
int nconflicts; /* statistic: number of conflicts applied */
int nbdchgs; /* statistic: number of bound changes applied */
int n1startinfers; /* statistic: number of one side infer values */
int n2startinfers; /* statistic: number of both side infer values */
SCIP_Bool soladded; /* statistic: was a new incumbent found? */
SCIP_Bool dualboundchg; /* statistic: was a new dual bound found? */
SCIP_Bool disabledualreductions; /* TRUE, if dual reductions in sub-SCIP are not valid for original SCIP,
* e.g., because a constraint could not be copied or a primal solution
* could not be copied back
*/
int ndiscvars;
soladded = FALSE;
assert(sepa != NULL);
assert(scip != NULL);
assert(result != NULL);
*result = SCIP_DIDNOTRUN;
ndiscvars = SCIPgetNBinVars(scip) + SCIPgetNIntVars(scip)+SCIPgetNImplVars(scip);
/* only run when still not fixed binary variables exists */
if( ndiscvars == 0 )
return SCIP_OKAY;
/* get separator's data */
sepadata = SCIPsepaGetData(sepa);
assert(sepadata != NULL);
/* only run for integer programs */
if( !sepadata->contvars && ndiscvars != SCIPgetNVars(scip) )
return SCIP_OKAY;
/* only run if there are few enough continuous variables */
if( sepadata->contvars && SCIPgetNContVars(scip) > sepadata->contvarsquot * SCIPgetNVars(scip) )
return SCIP_OKAY;
/* do not run if pricers are present */
if( SCIPgetNActivePricers(scip) > 0 )
return SCIP_OKAY;
/* if the separator should be exclusive to the root node, this prevents multiple calls due to restarts */
if( SCIPsepaGetFreq(sepa) == 0 && SCIPsepaGetNCalls(sepa) > 0)
return SCIP_OKAY;
/* call separator at most once per node */
if( SCIPsepaGetNCallsAtNode(sepa) > 0 )
return SCIP_OKAY;
/* do not call rapid learning, if the problem is too big */
if( SCIPgetNVars(scip) > sepadata->maxnvars || SCIPgetNConss(scip) > sepadata->maxnconss )
return SCIP_OKAY;
if( SCIPisStopped(scip) )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
/* initializing the subproblem */
SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
SCIP_CALL( SCIPcreate(&subscip) );
SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), SCIPcalcHashtableSize(5 * nvars)) );
success = FALSE;
/* copy the subproblem */
SCIP_CALL( SCIPcopy(scip, subscip, varmapfw, NULL, "rapid", FALSE, FALSE, &success) );
if( sepadata->copycuts )
{
/** copies all active cuts from cutpool of sourcescip to linear constraints in targetscip */
SCIP_CALL( SCIPcopyCuts(scip, subscip, varmapfw, NULL, FALSE) );
}
for( i = 0; i < nvars; i++ )
subvars[i] = (SCIP_VAR*) (size_t) SCIPhashmapGetImage(varmapfw, vars[i]);
SCIPhashmapFree(&varmapfw);
/* this avoids dual presolving */
if( !success )
{
for( i = 0; i < nvars; i++ )
{
SCIP_CALL( SCIPaddVarLocks(subscip, subvars[i], 1, 1 ) );
}
}
SCIPdebugMessage("Copying SCIP was%s successful.\n", success ? "" : " not");
/* mimic an FD solver: DFS, no LP solving, 1-FUIP instead of all-FUIP */
SCIP_CALL( SCIPsetIntParam(subscip, "lp/solvefreq", -1) );
SCIP_CALL( SCIPsetIntParam(subscip, "conflict/fuiplevels", 1) );
SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/dfs/stdpriority", INT_MAX/4) );
SCIP_CALL( SCIPsetBoolParam(subscip, "constraints/disableenfops", TRUE) );
SCIP_CALL( SCIPsetIntParam(subscip, "propagating/pseudoobj/freq", -1) );
/* use inference branching */
SCIP_CALL( SCIPsetBoolParam(subscip, "branching/inference/useweightedsum", FALSE) );
/* only create short conflicts */
SCIP_CALL( SCIPsetRealParam(subscip, "conflict/maxvarsfac", 0.05) );
/* set limits for the subproblem */
nodelimit = SCIPgetNLPIterations(scip);
nodelimit = MAX(sepadata->minnodes, nodelimit);
nodelimit = MIN(sepadata->maxnodes, nodelimit);
restartnum = 1000;
/* check whether there is enough time and memory left */
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;
if( timelimit <= 0.0 || memorylimit <= 0.0 )
goto TERMINATE;
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nodelimit/5) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/time", timelimit) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/memory", memorylimit) );
SCIP_CALL( SCIPsetIntParam(subscip, "limits/restarts", 0) );
SCIP_CALL( SCIPsetIntParam(subscip, "conflict/restartnum", restartnum) );
/* forbid recursive call of heuristics and separators solving subMIPs */
SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );
/* disable cutting plane separation */
SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );
/* disable expensive presolving */
SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );
/* do not abort subproblem on CTRL-C */
SCIP_CALL( SCIPsetBoolParam(subscip, "misc/catchctrlc", FALSE) );
#ifndef SCIP_DEBUG
/* disable output to console */
SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 0) );
#endif
/* add an objective cutoff */
SCIP_CALL( SCIPsetObjlimit(subscip, SCIPgetUpperbound(scip)) );
/* create the variable mapping hash map */
SCIP_CALL( SCIPhashmapCreate(&varmapbw, SCIPblkmem(scip), SCIPcalcHashtableSize(5 * nvars)) );
/* store reversing mapping of variables */
SCIP_CALL( SCIPtransformProb(subscip) );
for( i = 0; i < nvars; ++i)
{
SCIP_CALL( SCIPhashmapInsert(varmapbw, SCIPvarGetTransVar(subvars[i]), vars[i]) );
}
/** allocate memory for constraints storage. Each constraint that will be created from now on will be a conflict.
* Therefore, we need to remember oldnconss to get the conflicts from the FD search.
*/
nconshdlrs = 4;
SCIP_CALL( SCIPallocBufferArray(scip, &conshdlrs, nconshdlrs) );
SCIP_CALL( SCIPallocBufferArray(scip, &oldnconss, nconshdlrs) );
/* store number of constraints before rapid learning search */
conshdlrs[0] = SCIPfindConshdlr(subscip, "bounddisjunction");
conshdlrs[1] = SCIPfindConshdlr(subscip, "setppc");
conshdlrs[2] = SCIPfindConshdlr(subscip, "linear");
conshdlrs[3] = SCIPfindConshdlr(subscip, "logicor");
/* redundant constraints might be eliminated in presolving */
SCIP_CALL( SCIPpresolve(subscip));
for( i = 0; i < nconshdlrs; ++i)
{
if( conshdlrs[i] != NULL )
oldnconss[i] = SCIPconshdlrGetNConss(conshdlrs[i]);
}
nfixedvars = SCIPgetNFixedVars(scip);
/* solve the subproblem */
retcode = SCIPsolve(subscip);
/* 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("Error while solving subproblem in rapid learning separator; sub-SCIP terminated with code <%d>\n",retcode);
}
/* abort solving, if limit of applied conflicts is reached */
if( SCIPgetNConflictConssApplied(subscip) >= restartnum )
{
SCIPdebugMessage("finish after %lld successful conflict calls.\n", SCIPgetNConflictConssApplied(subscip));
}
/* if the first 20% of the solution process were successful, proceed */
else if( (sepadata->applyprimalsol && SCIPgetNSols(subscip) > 0 && SCIPisFeasLT(scip, SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip) ) )
|| (sepadata->applybdchgs && SCIPgetNFixedVars(subscip) > nfixedvars)
|| (sepadata->applyconflicts && SCIPgetNConflictConssApplied(subscip) > 0) )
{
SCIPdebugMessage("proceed solving after the first 20%% of the solution process, since:\n");
if( SCIPgetNSols(subscip) > 0 && SCIPisFeasLE(scip, SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip) ) )
{
SCIPdebugMessage(" - there was a better solution (%f < %f)\n",SCIPgetUpperbound(subscip), SCIPgetUpperbound(scip));
}
if( SCIPgetNFixedVars(subscip) > nfixedvars )
{
SCIPdebugMessage(" - there were %d variables fixed\n", SCIPgetNFixedVars(scip)-nfixedvars );
}
if( SCIPgetNConflictConssFound(subscip) > 0 )
{
SCIPdebugMessage(" - there were %lld conflict constraints created\n", SCIPgetNConflictConssApplied(subscip));
}
/* set node limit to 100% */
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nodelimit) );
/* solve the subproblem */
retcode = SCIPsolve(subscip);
/* 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("Error while solving subproblem in rapid learning separator; sub-SCIP terminated with code <%d>\n",retcode);
}
}
else
{
SCIPdebugMessage("do not proceed solving after the first 20%% of the solution process.\n");
}
#ifdef SCIP_DEBUG
SCIP_CALL( SCIPprintStatistics(subscip, NULL) );
#endif
disabledualreductions = FALSE;
/* check, whether a solution was found */
if( sepadata->applyprimalsol && SCIPgetNSols(subscip) > 0 && SCIPfindHeur(scip, "trysol") != NULL )
{
SCIP_HEUR* heurtrysol;
SCIP_SOL** subsols;
int nsubsols;
/* check, whether a solution was found;
* due to numerics, it might happen that not all solutions are feasible -> try all solutions until was declared to be feasible
*/
nsubsols = SCIPgetNSols(subscip);
subsols = SCIPgetSols(subscip);
soladded = FALSE;
heurtrysol = SCIPfindHeur(scip, "trysol");
/* sequentially add solutions to trysol heuristic */
for( i = 0; i < nsubsols && !soladded; ++i )
{
SCIPdebugMessage("Try to create new solution by copying subscip solution.\n");
SCIP_CALL( createNewSol(scip, subscip, subvars, heurtrysol, subsols[i], &soladded) );
}
if( !soladded || !SCIPisEQ(scip, SCIPgetSolOrigObj(subscip, subsols[i-1]), SCIPgetSolOrigObj(subscip, subsols[0])) )
disabledualreductions = TRUE;
}
/* if the sub problem was solved completely, we update the dual bound */
dualboundchg = FALSE;
if( sepadata->applysolved && !disabledualreductions
&& (SCIPgetStatus(subscip) == SCIP_STATUS_OPTIMAL || SCIPgetStatus(subscip) == SCIP_STATUS_INFEASIBLE) )
{
/* we need to multiply the dualbound with the scaling factor and add the offset,
* because this information has been disregarded in the sub-SCIP */
SCIPdebugMessage("Update old dualbound %g to new dualbound %g.\n", SCIPgetDualbound(scip), SCIPgetTransObjscale(scip) * SCIPgetDualbound(subscip) + SCIPgetTransObjoffset(scip));
SCIP_CALL( SCIPupdateLocalDualbound(scip, SCIPgetDualbound(subscip) * SCIPgetTransObjscale(scip) + SCIPgetTransObjoffset(scip)) );
dualboundchg = TRUE;
}
/* check, whether conflicts were created */
nconflicts = 0;
if( sepadata->applyconflicts && !disabledualreductions && SCIPgetNConflictConssApplied(subscip) > 0 )
{
SCIP_HASHMAP* consmap;
int hashtablesize;
assert(SCIPgetNConflictConssApplied(subscip) < (SCIP_Longint) INT_MAX);
hashtablesize = (int) SCIPgetNConflictConssApplied(subscip);
assert(hashtablesize < INT_MAX/5);
hashtablesize *= 5;
/* create the variable mapping hash map */
SCIP_CALL( SCIPhashmapCreate(&consmap, SCIPblkmem(scip), SCIPcalcHashtableSize(hashtablesize)) );
/* loop over all constraint handlers that might contain conflict constraints */
for( i = 0; i < nconshdlrs; ++i)
{
/* copy constraints that have been created in FD run */
if( conshdlrs[i] != NULL && SCIPconshdlrGetNConss(conshdlrs[i]) > oldnconss[i] )
{
SCIP_CONS** conss;
int c;
int nconss;
nconss = SCIPconshdlrGetNConss(conshdlrs[i]);
conss = SCIPconshdlrGetConss(conshdlrs[i]);
/* loop over all constraints that have been added in sub-SCIP run, these are the conflicts */
for( c = oldnconss[i]; c < nconss; ++c)
{
SCIP_CONS* cons;
SCIP_CONS* conscopy;
cons = conss[c];
assert(cons != NULL);
success = FALSE;
SCIP_CALL( SCIPgetConsCopy(subscip, scip, cons, &conscopy, conshdlrs[i], varmapbw, consmap, NULL,
SCIPconsIsInitial(cons), SCIPconsIsSeparated(cons), SCIPconsIsEnforced(cons), SCIPconsIsChecked(cons),
SCIPconsIsPropagated(cons), TRUE, FALSE, SCIPconsIsDynamic(cons),
SCIPconsIsRemovable(cons), FALSE, TRUE, &success) );
if( success )
{
nconflicts++;
SCIP_CALL( SCIPaddCons(scip, conscopy) );
SCIP_CALL( SCIPreleaseCons(scip, &conscopy) );
}
else
{
SCIPdebugMessage("failed to copy conflict constraint %s back to original SCIP\n", SCIPconsGetName(cons));
}
}
}
}
SCIPhashmapFree(&consmap);
}
/* check, whether tighter global bounds were detected */
nbdchgs = 0;
if( sepadata->applybdchgs && !disabledualreductions )
for( i = 0; i < nvars; ++i )
{
SCIP_Bool infeasible;
SCIP_Bool tightened;
assert(SCIPisLE(scip, SCIPvarGetLbGlobal(vars[i]), SCIPvarGetLbGlobal(subvars[i])));
assert(SCIPisLE(scip, SCIPvarGetLbGlobal(subvars[i]), SCIPvarGetUbGlobal(subvars[i])));
assert(SCIPisLE(scip, SCIPvarGetUbGlobal(subvars[i]), SCIPvarGetUbGlobal(vars[i])));
/* update the bounds of the original SCIP, if a better bound was proven in the sub-SCIP */
SCIP_CALL( SCIPtightenVarUb(scip, vars[i], SCIPvarGetUbGlobal(subvars[i]), FALSE, &infeasible, &tightened) );
if( tightened )
nbdchgs++;
SCIP_CALL( SCIPtightenVarLb(scip, vars[i], SCIPvarGetLbGlobal(subvars[i]), FALSE, &infeasible, &tightened) );
if( tightened )
nbdchgs++;
}
n1startinfers = 0;
n2startinfers = 0;
/* install start values for inference branching */
if( sepadata->applyinfervals && (!sepadata->reducedinfer || soladded || nbdchgs+nconflicts > 0) )
{
for( i = 0; i < nvars; ++i )
{
SCIP_Real downinfer;
SCIP_Real upinfer;
SCIP_Real downvsids;
SCIP_Real upvsids;
SCIP_Real downconflen;
SCIP_Real upconflen;
/* copy downwards branching statistics */
downvsids = SCIPgetVarVSIDS(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);
downconflen = SCIPgetVarAvgConflictlength(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);
downinfer = SCIPgetVarAvgInferences(subscip, subvars[i], SCIP_BRANCHDIR_DOWNWARDS);
/* copy upwards branching statistics */
upvsids = SCIPgetVarVSIDS(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);
upconflen = SCIPgetVarAvgConflictlength(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);
upinfer = SCIPgetVarAvgInferences(subscip, subvars[i], SCIP_BRANCHDIR_UPWARDS);
/* memorize statistics */
if( downinfer+downconflen+downvsids > 0.0 || upinfer+upconflen+upvsids != 0 )
n1startinfers++;
if( downinfer+downconflen+downvsids > 0.0 && upinfer+upconflen+upvsids != 0 )
n2startinfers++;
SCIP_CALL( SCIPinitVarBranchStats(scip, vars[i], 0.0, 0.0, downvsids, upvsids, downconflen, upconflen, downinfer, upinfer, 0.0, 0.0) );
}
}
SCIPdebugPrintf("XXX Rapidlearning added %d conflicts, changed %d bounds, %s primal solution, %s dual bound improvement.\n", nconflicts, nbdchgs, soladded ? "found" : "no",
dualboundchg ? "found" : "no");
SCIPdebugPrintf("YYY Infervalues initialized on one side: %5.2f %% of variables, %5.2f %% on both sides\n",
100.0 * n1startinfers/(SCIP_Real)nvars, 100.0 * n2startinfers/(SCIP_Real)nvars);
/* change result pointer */
if( nconflicts > 0 || dualboundchg )
*result = SCIP_CONSADDED;
else if( nbdchgs > 0 )
*result = SCIP_REDUCEDDOM;
/* free local data */
SCIPfreeBufferArray(scip, &oldnconss);
SCIPfreeBufferArray(scip, &conshdlrs);
SCIPhashmapFree(&varmapbw);
TERMINATE:
/* free subproblem */
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/** writes problem to file */
SCIP_RETCODE SCIPwriteCcg(
SCIP* scip, /**< SCIP data structure */
FILE* file, /**< output file, or NULL if standard output should be used */
const char* name, /**< problem name */
SCIP_Bool transformed, /**< TRUE iff problem is the transformed problem */
SCIP_VAR** vars, /**< array with active variables ordered binary, integer, implicit, continuous */
int nvars, /**< number of active variables in the problem */
SCIP_CONS** conss, /**< array with constraints of the problem */
int nconss, /**< number of constraints in the problem */
SCIP_RESULT* result /**< pointer to store the result of the file writing call */
)
{ /*lint --e{715}*/
int c;
int v;
int i;
SCIP_CONSHDLR* conshdlr;
const char* conshdlrname;
SCIP_CONS* cons;
SCIP_VAR** consvars;
SCIP_Real* consvals;
int nconsvars;
SparseGraph G;
assert( scip != NULL );
assert( nvars >= 0 );
/* initialize graph */
SCIP_CALL( initGraph(scip, &G, (unsigned int) nvars, 10) );
/* check all constraints */
for( c = 0; c < nconss; ++c)
{
cons = conss[c];
assert( cons != NULL);
/* in case the transformed is written only constraint are posted which are enabled in the current node */
assert(!transformed || SCIPconsIsEnabled(cons));
conshdlr = SCIPconsGetHdlr(cons);
assert( conshdlr != NULL );
conshdlrname = SCIPconshdlrGetName(conshdlr);
assert( transformed == SCIPconsIsTransformed(cons) );
if( strcmp(conshdlrname, "linear") == 0 )
{
consvars = SCIPgetVarsLinear(scip, cons);
nconsvars = SCIPgetNVarsLinear(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( handleLinearCons(scip, SCIPgetVarsLinear(scip, cons), SCIPgetValsLinear(scip, cons),
SCIPgetNVarsLinear(scip, cons), transformed, &G) );
}
}
else if( strcmp(conshdlrname, "setppc") == 0 )
{
consvars = SCIPgetVarsSetppc(scip, cons);
nconsvars = SCIPgetNVarsSetppc(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( handleLinearCons(scip, consvars, NULL, nconsvars, transformed, &G) );
}
}
else if( strcmp(conshdlrname, "logicor") == 0 )
{
consvars = SCIPgetVarsLogicor(scip, cons);
nconsvars = SCIPgetNVarsLogicor(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
if( nconsvars > 0 )
{
SCIP_CALL( handleLinearCons(scip, SCIPgetVarsLogicor(scip, cons), NULL, SCIPgetNVarsLogicor(scip, cons), transformed, &G) );
}
}
else if( strcmp(conshdlrname, "knapsack") == 0 )
{
SCIP_Longint* w;
consvars = SCIPgetVarsKnapsack(scip, cons);
nconsvars = SCIPgetNVarsKnapsack(scip, cons);
assert( consvars != NULL || nconsvars == 0 );
/* copy Longint array to SCIP_Real array */
w = SCIPgetWeightsKnapsack(scip, cons);
SCIP_CALL( SCIPallocBufferArray(scip, &consvals, nconsvars) );
for( v = 0; v < nconsvars; ++v )
consvals[v] = (SCIP_Real)w[v];
if( nconsvars > 0 )
{
SCIP_CALL( handleLinearCons(scip, consvars, consvals, nconsvars, transformed, &G) );
}
SCIPfreeBufferArray(scip, &consvals);
}
else if( strcmp(conshdlrname, "varbound") == 0 )
{
SCIP_CALL( SCIPallocBufferArray(scip, &consvars, 2) );
SCIP_CALL( SCIPallocBufferArray(scip, &consvals, 2) );
consvars[0] = SCIPgetVarVarbound(scip, cons);
consvars[1] = SCIPgetVbdvarVarbound(scip, cons);
consvals[0] = 1.0;
consvals[1] = SCIPgetVbdcoefVarbound(scip, cons);
SCIP_CALL( handleLinearCons(scip, consvars, consvals, 2, transformed, &G) );
SCIPfreeBufferArray(scip, &consvars);
SCIPfreeBufferArray(scip, &consvals);
}
else
{
SCIPwarningMessage(scip, "constraint handler <%s> cannot print requested format\n", conshdlrname );
SCIPinfoMessage(scip, file, "\\ ");
SCIP_CALL( SCIPprintCons(scip, cons, file) );
SCIPinfoMessage(scip, file, ";\n");
}
}
/* output graph */
SCIPinfoMessage(scip, file, "c graph generated from %s\n", name);
SCIPinfoMessage(scip, file, "p edge %d %d\n", nvars, G.m);
for( i = 0; i < nvars; ++i )
{
unsigned int k;
int a;
k = 0;
a = G.A[i][k];
while( a >= 0 )
{
/* only output edges from lower to higher number */
if( i < a )
{
/* note: node numbers start with 1 in the DIMACS format */
SCIPinfoMessage(scip, file, "e %d %d %f\n", i+1, a+1, G.W[i][k]);
}
a = G.A[i][++k];
assert( k <= G.size[i] );
}
assert( k == G.deg[i] );
}
freeGraph(scip, &G);
*result = SCIP_SUCCESS;
return SCIP_OKAY;
}
/** problem reading method of reader */
static
SCIP_DECL_READERREAD(readerReadBpa)
{ /*lint --e{715}*/
SCIP_FILE* file;
SCIP_Longint* weights;
int* ids;
SCIP_Bool error;
char name[SCIP_MAXSTRLEN];
char format[16];
char buffer[SCIP_MAXSTRLEN];
int capacity;
int nitems;
int bestsolvalue;
int nread;
int weight;
int nweights;
int lineno;
*result = SCIP_DIDNOTRUN;
/* open file */
file = SCIPfopen(filename, "r");
if( file == NULL )
{
SCIPerrorMessage("cannot open file <%s> for reading\n", filename);
SCIPprintSysError(filename);
return SCIP_NOFILE;
}
lineno = 0;
sprintf(name, "++ uninitialized ++");
/* read problem name */
if( !SCIPfeof(file) )
{
/* get next line */
if( SCIPfgets(buffer, (int)sizeof(buffer), file) == NULL )
return SCIP_READERROR;
lineno++;
/* parse dimension line */
sprintf(format, "%%%ds\n", SCIP_MAXSTRLEN);
nread = sscanf(buffer, format, name);
if( nread == 0 )
{
SCIPwarningMessage(scip, "invalid input line %d in file <%s>: <%s>\n", lineno, filename, buffer);
return SCIP_READERROR;
}
SCIPdebugMessage("problem name <%s>\n", name);
}
capacity = 0;
nitems = 0;
/* read problem dimension */
if( !SCIPfeof(file) )
{
/* get next line */
if( SCIPfgets(buffer, (int)sizeof(buffer), file) == NULL )
return SCIP_READERROR;
lineno++;
/* parse dimension line */
nread = sscanf(buffer, "%d %d %d\n", &capacity, &nitems, &bestsolvalue);
if( nread < 2 )
{
SCIPwarningMessage(scip, "invalid input line %d in file <%s>: <%s>\n", lineno, filename, buffer);
return SCIP_READERROR;
}
SCIPdebugMessage("capacity = <%d>, number of items = <%d>, best known solution = <%d>\n", capacity, nitems, bestsolvalue);
}
/* allocate buffer memory for storing the weights and ids temporary */
SCIP_CALL( SCIPallocBufferArray(scip, &weights, nitems) );
SCIP_CALL( SCIPallocBufferArray(scip, &ids, nitems) );
/* pasre weights */
nweights = 0;
error = FALSE;
while( !SCIPfeof(file) && !error )
{
/* get next line */
if( SCIPfgets(buffer, (int)sizeof(buffer), file) == NULL )
break;
lineno++;
/* parse the line */
nread = sscanf(buffer, "%d\n", &weight);
if( nread == 0 )
{
SCIPwarningMessage(scip, "invalid input line %d in file <%s>: <%s>\n", lineno, filename, buffer);
error = TRUE;
break;
}
SCIPdebugMessage("found weight %d <%d>\n", nweights, weight);
weights[nweights] = weight;
ids[nweights] = nweights;
nweights++;
if( nweights == nitems )
break;
}
if( nweights < nitems )
{
SCIPwarningMessage(scip, "set nitems from <%d> to <%d> since the file <%s> only contains <%d> weights\n", nitems, weights, filename, weights);
nitems = nweights;
}
if( !error )
{
/* create a new problem in SCIP */
SCIP_CALL( SCIPprobdataCreate(scip, name, ids, weights, nitems, (SCIP_Longint)capacity) );
}
(void)SCIPfclose(file);
SCIPfreeBufferArray(scip, &ids);
SCIPfreeBufferArray(scip, &weights);
if( error )
return SCIP_READERROR;
*result = SCIP_SUCCESS;
return SCIP_OKAY;
}
/** main procedure of the RENS heuristic, creates and solves a subMIP */
SCIP_RETCODE SCIPapplyGcgrens(
SCIP* scip, /**< original SCIP data structure */
SCIP_HEUR* heur, /**< heuristic data structure */
SCIP_RESULT* result, /**< result data structure */
SCIP_Real minfixingrate, /**< minimum percentage of integer variables that have to be fixed */
SCIP_Real minimprove, /**< factor by which RENS should at least improve the incumbent */
SCIP_Longint maxnodes, /**< maximum number of nodes for the subproblem */
SCIP_Longint nstallnodes, /**< number of stalling nodes for the subproblem */
SCIP_Bool binarybounds, /**< should general integers get binary bounds [floor(.),ceil(.)]? */
SCIP_Bool uselprows /**< should subproblem be created out of the rows in the LP rows? */
)
{
SCIP* subscip; /* the subproblem created by RENS */
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_Real cutoff; /* objective cutoff for the subproblem */
SCIP_Real timelimit;
SCIP_Real memorylimit;
int nvars;
int i;
SCIP_Bool success;
SCIP_RETCODE retcode;
assert(scip != NULL);
assert(heur != NULL);
assert(result != NULL);
assert(maxnodes >= 0);
assert(nstallnodes >= 0);
assert(0.0 <= minfixingrate && minfixingrate <= 1.0);
assert(0.0 <= minimprove && minimprove <= 1.0);
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) );
if( uselprows )
{
char probname[SCIP_MAXSTRLEN];
/* copy all plugins */
SCIP_CALL( SCIPincludeDefaultPlugins(subscip) );
/* get name of the original problem and add the string "_gcgrenssub" */
(void) SCIPsnprintf(probname, SCIP_MAXSTRLEN, "%s_gcgrenssub", SCIPgetProbName(scip));
/* create the subproblem */
SCIP_CALL( SCIPcreateProb(subscip, probname, NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
/* copy all variables */
SCIP_CALL( SCIPcopyVars(scip, subscip, varmapfw, NULL, TRUE) );
}
else
{
SCIP_Bool valid;
SCIP_HEURDATA* heurdata;
valid = FALSE;
SCIP_CALL( SCIPcopy(scip, subscip, varmapfw, NULL, "gcgrens", TRUE, FALSE, TRUE, &valid) ); /** @todo check for thread safeness */
/* get heuristic's data */
heurdata = SCIPheurGetData(heur);
assert( heurdata != NULL );
if( heurdata->copycuts )
{
/** copies all active cuts from cutpool of sourcescip to linear constraints in targetscip */
SCIP_CALL( SCIPcopyCuts(scip, subscip, varmapfw, NULL, TRUE, NULL) );
}
SCIPdebugMessage("Copying the SCIP instance was %s complete.\n", valid ? "" : "not ");
}
for( i = 0; i < nvars; i++ )
subvars[i] = (SCIP_VAR*) SCIPhashmapGetImage(varmapfw, vars[i]);
/* free hash map */
SCIPhashmapFree(&varmapfw);
/* create a new problem, which fixes variables with same value in bestsol and LP relaxation */
SCIP_CALL( createSubproblem(scip, subscip, subvars, minfixingrate, binarybounds, uselprows, &success) );
SCIPdebugMessage("RENS subproblem: %d vars, %d cons, success=%u\n", SCIPgetNVars(subscip), SCIPgetNConss(subscip), success);
/* 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) );
/* 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) );
if( !SCIPisInfinity(scip, memorylimit) )
memorylimit -= SCIPgetMemUsed(scip)/1048576.0;
if( timelimit <= 0.0 || memorylimit <= 0.0 )
goto TERMINATE;
/* set limits for the subproblem */
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/stallnodes", nstallnodes) );
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", maxnodes) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/time", timelimit) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/memory", memorylimit) );
/* forbid recursive call of heuristics and separators solving sub-SCIPs */
SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );
/* disable cutting plane separation */
SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );
/* disable expensive presolving */
SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );
/* use best estimate node selection */
if( SCIPfindNodesel(scip, "estimate") != NULL )
{
SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/estimate/stdpriority", INT_MAX/4) );
}
/* use inference branching */
if( SCIPfindBranchrule(scip, "inference") != NULL )
{
SCIP_CALL( SCIPsetIntParam(subscip, "branching/inference/priority", INT_MAX/4) );
}
/* disable conflict analysis */
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useprop", FALSE) );
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useinflp", FALSE) );
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useboundlp", FALSE) );
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usesb", FALSE) );
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usepseudo", FALSE) );
#ifdef SCIP_DEBUG
/* for debugging RENS, enable MIP output */
SCIP_CALL( SCIPsetIntParam(subscip, "display/verblevel", 5) );
SCIP_CALL( SCIPsetIntParam(subscip, "display/freq", 100000000) );
#endif
/* if the subproblem could not be created, free memory and return */
if( !success )
{
*result = SCIP_DIDNOTRUN;
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/* if there is already a solution, add an objective cutoff */
if( SCIPgetNSols(scip) > 0 )
{
SCIP_Real upperbound;
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( SCIPsetObjlimit(subscip, cutoff) );
}
/* presolve the subproblem */
retcode = SCIPpresolve(subscip);
/* 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 presolving subproblem in GCG RENS heuristic; sub-SCIP terminated with code <%d>\n",retcode);
}
SCIPdebugMessage("GCG RENS presolved subproblem: %d vars, %d cons, success=%u\n", SCIPgetNVars(subscip), SCIPgetNConss(subscip), success);
/* after presolving, we should have at least reached a certain fixing rate over ALL variables (including continuous)
* to ensure that not only the MIP but also the LP relaxation is easy enough
*/
if( ( nvars - SCIPgetNVars(subscip) ) / (SCIP_Real)nvars >= minfixingrate / 2.0 )
{
SCIP_SOL** subsols;
int nsubsols;
/* solve the subproblem */
SCIPdebugMessage("solving subproblem: nstallnodes=%"SCIP_LONGINT_FORMAT", maxnodes=%"SCIP_LONGINT_FORMAT"\n", nstallnodes, maxnodes);
retcode = SCIPsolve(subscip);
/* 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 GCG RENS 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; ++i )
{
SCIP_CALL( createNewSol(scip, subscip, subvars, heur, subsols[i], &success) );
}
if( success )
*result = SCIP_FOUNDSOL;
}
TERMINATE:
/* free subproblem */
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecCrossover)
{ /*lint --e{715}*/
SCIP_HEURDATA* heurdata; /* primal heuristic data */
SCIP* subscip; /* the subproblem created by crossover */
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_SOL** sols;
SCIP_Real memorylimit; /* memory limit for the subproblem */
SCIP_Real timelimit; /* time limit for the subproblem */
SCIP_Real cutoff; /* objective cutoff for the subproblem */
SCIP_Real upperbound;
SCIP_Bool success;
SCIP_Longint nstallnodes; /* node limit for the subproblem */
int* selection; /* pool of solutions crossover uses */
int nvars; /* number of original problem's variables */
int nbinvars;
int nintvars;
int nusedsols;
int i;
SCIP_RETCODE retcode;
assert(heur != NULL);
assert(scip != NULL);
assert(result != NULL);
/* get heuristic's data */
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
nusedsols = heurdata->nusedsols;
*result = SCIP_DELAYED;
/* only call heuristic, if enough solutions are at hand */
if( SCIPgetNSols(scip) < nusedsols )
return SCIP_OKAY;
sols = SCIPgetSols(scip);
assert(sols != NULL);
/* if one good solution was found, heuristic should not be delayed any longer */
if( sols[nusedsols-1] != heurdata->prevlastsol )
{
heurdata->nextnodenumber = SCIPgetNNodes(scip);
if( sols[0] != heurdata->prevbestsol )
heurdata->nfailures = 0;
}
/* in nonrandomized mode: only recall heuristic, if at least one new good solution was found in the meantime */
else if( !heurdata->randomization )
return SCIP_OKAY;
/* if heuristic should be delayed, wait until certain number of nodes is reached */
if( SCIPgetNNodes(scip) < heurdata->nextnodenumber )
return SCIP_OKAY;
/* only call heuristic, if enough nodes were processed since last incumbent */
if( SCIPgetNNodes(scip) - SCIPgetSolNodenum(scip,SCIPgetBestSol(scip)) < heurdata->nwaitingnodes
&& (SCIPgetDepth(scip) > 0 || !heurdata->dontwaitatroot) )
return SCIP_OKAY;
*result = SCIP_DIDNOTRUN;
/* calculate the maximal number of branching nodes until heuristic is aborted */
nstallnodes = (SCIP_Longint)(heurdata->nodesquot * SCIPgetNNodes(scip));
/* reward Crossover if it succeeded often */
nstallnodes = (SCIP_Longint)
(nstallnodes * (1.0 + 2.0*(SCIPheurGetNBestSolsFound(heur)+1.0)/(SCIPheurGetNCalls(heur)+1.0)));
/* count the setup costs for the sub-MIP as 100 nodes */
nstallnodes -= 100 * SCIPheurGetNCalls(heur);
nstallnodes += heurdata->nodesofs;
/* determine the node limit for the current process */
nstallnodes -= heurdata->usednodes;
nstallnodes = MIN(nstallnodes, heurdata->maxnodes);
/* check whether we have enough nodes left to call subproblem solving */
if( nstallnodes < heurdata->minnodes )
return SCIP_OKAY;
if( SCIPisStopped(scip) )
return SCIP_OKAY;
*result = SCIP_DIDNOTFIND;
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, &nbinvars, &nintvars, NULL, NULL) );
assert(nvars > 0);
/* check whether discrete variables are available */
if( nbinvars == 0 && nintvars == 0 )
return SCIP_OKAY;
/* initializing the subproblem */
SCIP_CALL( SCIPcreate(&subscip) );
/* create the variable mapping hash map */
SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), SCIPcalcHashtableSize(5 * nvars)) );
success = FALSE;
if( heurdata->uselprows )
{
char probname[SCIP_MAXSTRLEN];
/* copy all plugins */
SCIP_CALL( SCIPincludeDefaultPlugins(subscip) );
/* get name of the original problem and add the string "_crossoversub" */
(void) SCIPsnprintf(probname, SCIP_MAXSTRLEN, "%s_crossoversub", SCIPgetProbName(scip));
/* create the subproblem */
SCIP_CALL( SCIPcreateProb(subscip, probname, NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
/* copy all variables */
SCIP_CALL( SCIPcopyVars(scip, subscip, varmapfw, NULL, TRUE) );
}
else
{
SCIP_CALL( SCIPcopy(scip, subscip, varmapfw, NULL, "crossover", TRUE, FALSE, TRUE, &success) );
if( heurdata->copycuts )
{
/** copies all active cuts from cutpool of sourcescip to linear constraints in targetscip */
SCIP_CALL( SCIPcopyCuts(scip, subscip, varmapfw, NULL, TRUE, NULL) );
}
}
SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &selection, nusedsols) );
for( i = 0; i < nvars; i++ )
subvars[i] = (SCIP_VAR*) (size_t) SCIPhashmapGetImage(varmapfw, vars[i]);
/* free hash map */
SCIPhashmapFree(&varmapfw);
success = FALSE;
/* create a new problem, which fixes variables with same value in a certain set of solutions */
SCIP_CALL( setupSubproblem(scip, subscip, subvars, selection, heurdata, &success) );
heurdata->prevbestsol = SCIPgetBestSol(scip);
heurdata->prevlastsol = sols[heurdata->nusedsols-1];
/* if creation of sub-SCIP was aborted (e.g. due to number of fixings), free sub-SCIP and abort */
if( !success )
{
*result = SCIP_DIDNOTRUN;
/* this run will be counted as a failure since no new solution tuple could be generated or the neighborhood of the
* solution was not fruitful in the sense that it was too big
*/
updateFailureStatistic(scip, heurdata);
goto TERMINATE;
}
/* 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) );
/* check whether there is enough time and memory left */
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 )
goto TERMINATE;
/* set limits for the subproblem */
SCIP_CALL( SCIPsetLongintParam(subscip, "limits/nodes", nstallnodes) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/time", timelimit) );
SCIP_CALL( SCIPsetRealParam(subscip, "limits/memory", memorylimit) );
/* forbid recursive call of heuristics and separators solving subMIPs */
SCIP_CALL( SCIPsetSubscipsOff(subscip, TRUE) );
/* disable cutting plane separation */
SCIP_CALL( SCIPsetSeparating(subscip, SCIP_PARAMSETTING_OFF, TRUE) );
/* disable expensive presolving */
SCIP_CALL( SCIPsetPresolving(subscip, SCIP_PARAMSETTING_FAST, TRUE) );
/* use best estimate node selection */
if( SCIPfindNodesel(subscip, "estimate") != NULL && !SCIPisParamFixed(subscip, "nodeselection/estimate/stdpriority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "nodeselection/estimate/stdpriority", INT_MAX/4) );
}
/* use inference branching */
if( SCIPfindBranchrule(subscip, "inference") != NULL && !SCIPisParamFixed(subscip, "branching/inference/priority") )
{
SCIP_CALL( SCIPsetIntParam(subscip, "branching/inference/priority", INT_MAX/4) );
}
/* disable conflict analysis */
if( !SCIPisParamFixed(subscip, "conflict/useprop") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useprop", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/useinflp") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useinflp", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/useboundlp") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/useboundlp", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/usesb") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usesb", FALSE) );
}
if( !SCIPisParamFixed(subscip, "conflict/usepseudo") )
{
SCIP_CALL( SCIPsetBoolParam(subscip, "conflict/usepseudo", FALSE) );
}
/* add an objective cutoff */
cutoff = SCIPinfinity(scip);
assert(!SCIPisInfinity(scip, SCIPgetUpperbound(scip)));
upperbound = SCIPgetUpperbound(scip) - SCIPsumepsilon(scip);
if( !SCIPisInfinity(scip,-1.0*SCIPgetLowerbound(scip)) )
{
cutoff = (1-heurdata->minimprove)*SCIPgetUpperbound(scip) + heurdata->minimprove*SCIPgetLowerbound(scip);
}
else
{
if( SCIPgetUpperbound ( scip ) >= 0 )
cutoff = ( 1 - heurdata->minimprove ) * SCIPgetUpperbound ( scip );
else
cutoff = ( 1 + heurdata->minimprove ) * SCIPgetUpperbound ( scip );
}
cutoff = MIN(upperbound, cutoff );
SCIP_CALL( SCIPsetObjlimit(subscip, cutoff) );
/* permute the subproblem to increase diversification */
if( heurdata->permute )
{
SCIP_CALL( SCIPpermuteProb(subscip, (unsigned int) SCIPheurGetNCalls(heur), TRUE, TRUE, TRUE, TRUE, TRUE) );
}
/* solve the subproblem */
SCIPdebugMessage("Solve Crossover subMIP\n");
retcode = SCIPsolve(subscip);
/* 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 Crossover heuristic; sub-SCIP terminated with code <%d>\n", retcode);
}
heurdata->usednodes += SCIPgetNNodes(subscip);
/* check, whether a solution was found */
if( SCIPgetNSols(subscip) > 0 )
{
SCIP_SOL** subsols;
int nsubsols;
int solindex; /* index of the solution created by crossover */
/* 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;
solindex = -1;
for( i = 0; i < nsubsols && !success; ++i )
{
SCIP_CALL( createNewSol(scip, subscip, subvars, heur, subsols[i], &solindex, &success) );
}
if( success )
{
int tmp;
assert(solindex != -1);
*result = SCIP_FOUNDSOL;
/* insert all crossings of the new solution and (nusedsols-1) of its parents into the hashtable
* in order to avoid incest ;)
*/
for( i = 0; i < nusedsols; i++ )
{
SOLTUPLE* elem;
tmp = selection[i];
selection[i] = solindex;
SCIP_CALL( createSolTuple(scip, &elem, selection, nusedsols, heurdata) );
SCIP_CALL( SCIPhashtableInsert(heurdata->hashtable, elem) );
selection[i] = tmp;
}
/* if solution was among the best ones, crossover should not be called until another good solution was found */
if( !heurdata->randomization )
{
heurdata->prevbestsol = SCIPgetBestSol(scip);
heurdata->prevlastsol = SCIPgetSols(scip)[heurdata->nusedsols-1];
}
}
/* if solution is not better then incumbent or could not be added to problem => run is counted as a failure */
if( !success || solindex != SCIPsolGetIndex(SCIPgetBestSol(scip)) )
updateFailureStatistic(scip, heurdata);
}
else
{
/* if no new solution was found, run was a failure */
updateFailureStatistic(scip, heurdata);
}
TERMINATE:
/* free subproblem */
SCIPfreeBufferArray(scip, &selection);
SCIPfreeBufferArray(scip, &subvars);
SCIP_CALL( SCIPfree(&subscip) );
return SCIP_OKAY;
}
/** avoid to generate columns which are fixed to zero; therefore add for each variable which is fixed to zero a
* corresponding logicor constraint to forbid this column
*
* @note variable which are fixed locally to zero should not be generated again by the pricing MIP
*/
static
SCIP_RETCODE addFixedVarsConss(
SCIP* scip, /**< SCIP data structure */
SCIP* subscip, /**< pricing SCIP data structure */
SCIP_VAR** vars, /**< variable array of the subscuip */
SCIP_CONS** conss, /**< array of setppc constraint for each item one */
int nitems /**< number of items */
)
{
SCIP_VAR** origvars;
int norigvars;
SCIP_CONS* cons;
int* consids;
int nconsids;
int consid;
int nvars;
SCIP_VAR** logicorvars;
SCIP_VAR* var;
SCIP_VARDATA* vardata;
SCIP_Bool needed;
int nlogicorvars;
int v;
int c;
int o;
/* collect all variable which are currently existing */
origvars = SCIPgetVars(scip);
norigvars = SCIPgetNVars(scip);
/* loop over all these variables and check if they are fixed to zero */
for( v = 0; v < norigvars; ++v )
{
assert(SCIPvarGetType(origvars[v]) == SCIP_VARTYPE_BINARY);
/* if the upper bound is smaller than 0.5 if follows due to the integrality that the binary variable is fixed to zero */
if( SCIPvarGetUbLocal(origvars[v]) < 0.5 )
{
SCIPdebugMessage("variable <%s> glb=[%.15g,%.15g] loc=[%.15g,%.15g] is fixed to zero\n",
SCIPvarGetName(origvars[v]), SCIPvarGetLbGlobal(origvars[v]), SCIPvarGetUbGlobal(origvars[v]),
SCIPvarGetLbLocal(origvars[v]), SCIPvarGetUbLocal(origvars[v]) );
/* coolect the constraints/items the variable belongs to */
vardata = SCIPvarGetData(origvars[v]);
nconsids = SCIPvardataGetNConsids(vardata);
consids = SCIPvardataGetConsids(vardata);
needed = TRUE;
SCIP_CALL( SCIPallocBufferArray(subscip, &logicorvars, nitems) );
nlogicorvars = 0;
consid = consids[0];
nvars = 0;
/* loop over these items and create a linear (logicor) constraint which forbids this item combination in the
* pricing problem; thereby check if this item combination is already forbidden
*/
for( c = 0, o = 0; o < nitems && needed; ++o )
{
assert(o <= consid);
cons = conss[o];
if( SCIPconsIsEnabled(cons) )
{
assert( SCIPgetNFixedonesSetppc(scip, cons) == 0 );
var = vars[nvars];
nvars++;
assert(var != NULL);
if( o == consid )
{
SCIP_CALL( SCIPgetNegatedVar(subscip, var, &var) );
}
logicorvars[nlogicorvars] = var;
nlogicorvars++;
}
else if( o == consid )
needed = FALSE;
if( o == consid )
{
c++;
if ( c == nconsids )
consid = nitems + 100;
else
{
assert(consid < consids[c]);
consid = consids[c];
}
}
}
if( needed )
{
SCIP_CALL( SCIPcreateConsBasicLogicor(subscip, &cons, SCIPvarGetName(origvars[v]), nlogicorvars, logicorvars) );
SCIP_CALL( SCIPsetConsInitial(subscip, cons, FALSE) );
SCIP_CALL( SCIPaddCons(subscip, cons) );
SCIP_CALL( SCIPreleaseCons(subscip, &cons) );
}
SCIPfreeBufferArray(subscip, &logicorvars);
}
}
return SCIP_OKAY;
}
/** execution method of primal heuristic */
static
SCIP_DECL_HEUREXEC(heurExecForward)
{ /*lint --e{715}*/
SCIP_PROBDATA* probdata;
int n;
int p;
int ndep;
/* "_" means the matrix for blas */
SCIP_Real* y; /* [n] */
SCIP_Real* orig_X_; /* [n*p] */
SCIP_Real* orig_Q_; /* [p*p] <- (X^t) X */
SCIP_Real* orig_q; /* [p] <- (X^t) y */
SCIP_Real r;
int* Mdep; /* [ndep] */
int* groupX; /* [ndep*p] */
/* for forward selection */
int dim;
int* list; /* [p] */
SCIP_Real* a; /* [dim] */
SCIP_Real* a_old; /* [dim-1] */
SCIP_Real* a_new; /* [dim] */
SCIP_Real RSS; /* residual sum of square */
SCIP_Real RSS_new;
SCIP_Real AIC;
SCIP_Real AIC_new;
int ublb;
int *Branchz; /* [3*p] */
/*
* X: sub matrix of orig_X_
* Y: (X^t X)^-1
* X_new = (X, x_i);
* Z: (X_new ^t X_new)^-1
* = ( V v
v^t u )
*/
SCIP_Real* Xy; /* sub vector of orig_q */
SCIP_Real* X_;
SCIP_Real* Y_; /* [(dim-1)*(dim-1)] */
SCIP_Real* Z_; /* [dim*dim] */
SCIP_Real* W_; /* [dim*dim] */
SCIP_Real* V_; /* [(dim-1)*(dim-1)] */
SCIP_Real* v; /* [dim-1] */
SCIP_Real u;
SCIP_Real* b; /* [dim-1] */
SCIP_Real* c; /* [dim-1] */
SCIP_Real* d; /* [n] */
/* variables */
SCIP_VAR** var_a; /* [p] continuous variables */
SCIP_VAR** var_z; /* [p] 01 variables */
SCIP_VAR** var_ep; /* [n] continuous variables */
SCIP_VAR* var_rss; /* continuous variable, residual sum of squares */
SCIP_VAR* var_log; /* continuous variable, log(rss) */
/* set solution */
SCIP_Real *ep;
int nsols;
int store;
SCIP_SOL** sols;
SCIP_Real objval;
SCIP_SOL* sol;
SCIP_Real* solvals;
SCIP_Bool success;
int nvars = SCIPgetNVars(scip);
SCIP_VAR** vars;
int i,j,t,ct;
int memo;
assert(heur != NULL);
assert(scip != NULL);
assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
assert(result != NULL);
#if MYPARA_LOG
printf("forward selection!\n");
#endif
/* get heuristic data */
/*
SCIP_HEURDATA* heurdata;
heurdata = SCIPheurGetData(heur);
assert(heurdata != NULL);
assert(lastsolindices != NULL);
*/
/* get values from probdata */
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
n = SCIPprobdataGetNdatas(probdata);
p = SCIPprobdataGetNexvars(probdata);
ndep = SCIPprobdataGetNdep(probdata);
y = SCIPprobdataGety(probdata);
orig_X_ = SCIPprobdataGetX(probdata);
orig_Q_ = SCIPprobdataGetQ(probdata);
orig_q = SCIPprobdataGetq(probdata);
r = SCIPprobdataGetr(probdata);
if( ndep ){
Mdep = SCIPprobdataGetMdep(probdata);
groupX = SCIPprobdataGetgroupX(probdata);
}else{
Mdep = NULL;
groupX = NULL;
}
/* variables */
var_a = SCIPprobdataGetVars_a(probdata);
var_z = SCIPprobdataGetVars_z(probdata);
var_ep = SCIPprobdataGetVars_ep(probdata);
var_rss = SCIPprobdataGetVar_rss(probdata);
var_log = SCIPprobdataGetVar_log(probdata);
/* get branching info */
/* alloc */
SCIP_CALL( SCIPallocBufferArray(scip, &Branchz, 3*p));
GenerateZeroVecInt( 3*p, Branchz);
for(i=0; i<p; ++i){
ublb = SCIPround(scip, SCIPcomputeVarUbLocal(scip, var_z[i])
+ SCIPcomputeVarLbLocal(scip, var_z[i]));
*(Branchz+(ublb*p)+i) = 1;
}
#if MYPARA_LOG
for(i=0; i<3; i++){
for(j=0; j<p; j++){
printf("%d, ", *(Branchz+(i*p)+j));
}
newline();
}
#endif
if( ndep ){
for(i=0; i<ndep; i++){
memo = -1;
for(j=0; j<p; j++){
if( *(groupX+(i*p)+j)==1 ){
if( *(Branchz+j)==1 ) break;
if( *(Branchz+p+j)==1 ) memo=j;
if( j==Mdep[i] ){
if( memo==-1 ){
printf("error in heur_backward.c\n");
stop();
}
*(Branchz+p+memo) = 0;
*(Branchz+memo) = 1;
break;
}
}
}
}
}
#if MYPARA_LOG
printf("linear dependent\n");
if( ndep ){
for(i=0; i<3; i++){
for(j=0; j<p; j++){
printf("%d, ", *(Branchz+(i*p)+j));
}
newline();
}
}
#endif
/* alloc */
SCIP_CALL( SCIPallocBufferArray(scip, &X_, n*p));
SCIP_CALL( SCIPallocBufferArray(scip, &Xy, p));
SCIP_CALL( SCIPallocBufferArray(scip, &d, n));
SCIP_CALL( SCIPallocBufferArray(scip, &list, p));
/* initialize from Branchz */
#if MYPARA_LOG
printf("initialization\n");
#endif
GenerateZeroVecInt( p, list);
dim = 0;
memo = -1;
AIC = 1e+06;
SCIP_CALL( SCIPallocBufferArray(scip, &a_old, dim+1));
for(i=0; i<p; i++){
if( Branchz[i]==1 ){ /* if z_i is fixed to 0 */
list[i] = -1;
}else if( Branchz[p+i]==1 ){ /* if z_i is unfixed */
list[i] = 0;
}else if( Branchz[2*p+i]==1 ){ /* if z_i is fixed 1 */
dim++;
list[i] = dim;
if( dim == 1 ){
a_old[0] = orig_q[i] / mat_( orig_Q_, p, i, i);
RSS = RSSvalue( 1, a_old, &orig_q[i], r);
AIC = AICvalue( n, dim, RSS);
/* update X_ and Xy */
mydcopy_( &orig_X_[n * i], &X_[n * (dim-1)], n);
Xy[dim-1] = orig_q[i];
/* generate Y ( dim = 1 ) */
SCIP_CALL( SCIPallocBufferArray( scip, &Y_, dim*dim));
Y_[0] = 1 / mat_( orig_Q_, p, i, i);
}else{
/* alloc */
SCIPfreeBufferArray(scip, &a_old);
SCIP_CALL( SCIPallocBufferArray( scip, &a_old, dim));
SCIP_CALL( SCIPallocBufferArray( scip, &b, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &c, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &v, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &V_, (dim-1)*(dim-1)));
SCIP_CALL( SCIPallocBufferArray( scip, &Z_, (dim)*(dim)));
/* 1. b <- X^t x_i */
dgemv_t( X_, n, dim-1, &orig_X_[n * i], b);
//printv( dim-1, b);
/* 2. c <- Y b */
dgemv_2( Y_, dim-1, dim-1, b, c);
//printv( dim-1, c);
/* 3. d <- - X c + x_i */
dgemv_1( X_, n, dim-1, c, &orig_X_[n * i], -1.0, 1.0, d);
//printv( n, d);
/* 4. u <- 1/<x_i, d> */
u = 1.0 / myddot_( &orig_X_[n * i], d, n);
//prints(u);
/* 5. v <- - u c */
mydscal_( c, dim-1, -u, v);
//printv( dim-1, v);
/* 6. V <- Y + u c c^t */
dger_1( Y_, c, c, dim-1, dim-1, u, V_);
//printM_( V_, dim-1, dim-1);
/* 7. Z */
/* V */
for(j=0; j<(dim-1); j++){
for(t=0; t<(dim-1); t++){
*(Z_ + j + (t*dim) ) = mat_( V_, dim-1, j, t);
}
}
/* v */
for(j=0; j<(dim-1); j++){
*(Z_ + dim-1 + (j*dim) ) = v[j];
*(Z_ + j + ((dim-1)*dim)) = v[j];
}
/* u */
*(Z_ + dim-1 + ((dim-1)*dim)) = u;
//printM_( Z_, dim, dim);
/* 8. a_old <- Z (Xy) */
Xy[dim-1] = orig_q[i];
dgemv_2( Z_, dim, dim, Xy, a_old);
//printv( dim, a_old);
RSS = RSSvalue( dim, a_old, Xy, r);
AIC = AICvalue( n, dim, RSS);
/* copy */
SCIPfreeBufferArray(scip, &Y_);
SCIP_CALL( SCIPallocBufferArray(scip, &Y_, dim*dim));
mydcopy_( Z_, Y_, dim*dim);
/* update X_ and Xy */
mydcopy_( &orig_X_[n * i], &X_[n * (dim-1)], n);
Xy[dim-1] = orig_q[i];
/* free */
SCIPfreeBufferArray(scip, &b);
SCIPfreeBufferArray(scip, &c);
SCIPfreeBufferArray(scip, &v);
SCIPfreeBufferArray(scip, &V_);
SCIPfreeBufferArray(scip, &Z_);
}
#if MYPARA_LOG
printf("---> %dth variable, AIC:%f\n", i, AIC);
#endif
}else{
printf("error:heur_forward.c\n");
stop();
}
}
if( dim == 0 ){
#if MYPARA_LOG
printf("[dim:0]\n");
#endif
dim++;
RSS = 1e+06;
for(i=0; i<p; i++){
if( list[i] == 0 ){
a_old[0] = orig_q[i] / mat_( orig_Q_, p, i, i);
RSS_new = RSSvalue( 1, a_old, &orig_q[i], r);
if( RSS_new < RSS ){
RSS = RSS_new;
memo = i;
}
#if MYPARA_LOG
printf("%d: RSS = %f\n", i, RSS_new);
#endif
}
}
if( memo < 0 || memo >= p ){
printf("error in heur_forward.c\n");
stop();
}
AIC = AICvalue( n, dim, RSS);
list[memo] = dim;
/* update X_ and Xy */
mydcopy_( &orig_X_[n * memo], &X_[n * (dim-1)], n);
Xy[dim-1] = orig_q[memo];
/* generate Y ( dim = 1 ) */
SCIP_CALL( SCIPallocBufferArray( scip, &Y_, dim*dim));
Y_[0] = 1 / mat_( orig_Q_, p, memo, memo);
#if MYPARA_LOG
printf("---> %dth variable, AIC:%f\n", memo, AIC);
#endif
} /* if ( dim==0 ) */
while(1){
dim++;
memo = -1;
RSS = 1e+06;
#if MYPARA_LOG
printf("(dim=%d) ", dim);
Longline();
#endif
/* alloc */
SCIP_CALL( SCIPallocBufferArray( scip, &a_new, dim));
SCIP_CALL( SCIPallocBufferArray( scip, &a, dim));
SCIP_CALL( SCIPallocBufferArray( scip, &b, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &c, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &v, dim-1));
SCIP_CALL( SCIPallocBufferArray( scip, &V_, (dim-1)*(dim-1)));
SCIP_CALL( SCIPallocBufferArray( scip, &Z_, (dim)*(dim)));
SCIP_CALL( SCIPallocBufferArray( scip, &W_, (dim)*(dim)));
for(i=0; i<p; i++){
/*
* 1. b <- X^t x_i
* 2. c <- Y b
* 3. d <- - X c + x_i
* 4. u <- 1 / <x_i, d>
* 5. v <- - u c
* 6. V <- Y + u c c^t
* 7. Z <- ( V v
v^t u )
* 8. a_new <- Z (Xy)
*/
if( list[i]==0 ){
/* 1. b <- X^t x_i */
dgemv_t( X_, n, dim-1, &orig_X_[n * i], b);
//printv( dim-1, b);
/* 2. c <- Y b */
dgemv_2( Y_, dim-1, dim-1, b, c);
//printv( dim-1, c);
/* 3. d <- - X c + x_i */
dgemv_1( X_, n, dim-1, c, &orig_X_[n * i], -1.0, 1.0, d);
//printv( n, d);
/* 4. u <- 1/<x_i, d> */
u = 1.0 / myddot_( &orig_X_[n * i], d, n);
//prints(u);
/* 5. v <- - u c */
mydscal_( c, dim-1, -u, v);
//printv( dim-1, v);
/* 6. V <- Y + u c c^t */
dger_1( Y_, c, c, dim-1, dim-1, u, V_);
//printM_( V_, dim-1, dim-1);
/* 7. Z */
/* V */
for(j=0; j<(dim-1); j++){
for(t=0; t<(dim-1); t++){
*(Z_ + j + (t*dim) ) = mat_( V_, dim-1, j, t);
}
}
/* v */
for(j=0; j<(dim-1); j++){
*(Z_ + dim-1 + (j*dim) ) = v[j];
*(Z_ + j + ((dim-1)*dim)) = v[j];
}
/* u */
*(Z_ + dim-1 + ((dim-1)*dim)) = u;
//printM_( Z_, dim, dim);
/* 8. a_new <- Z (Xy) */
Xy[dim-1] = orig_q[i];
dgemv_2( Z_, dim, dim, Xy, a_new);
//printv( dim, a_new);
/* test */
RSS_new = RSSvalue( dim, a_new, Xy, r);
if( RSS_new < RSS ){
RSS = RSS_new;
memo = i;
mydcopy_( Z_, W_, dim*dim);
mydcopy_( a_new, a, dim);
}
#if MYPARA_LOG
printf("%d: RSS = %f\n", i, RSS_new);
#endif
}
}
if( memo < 0 || memo >= p ){
if( memo == -1 ){
for(i=0; i<p; i++){
if( list[i] == 0 ){
memo = i;
break;
}
}
if( memo != -1 ){
printf("error in heur_forward.c\n");
stop();
}
}else{
printf("error in heur_forward.c\n");
stop();
}
}
AIC_new = AICvalue( n, dim, RSS);
if( AIC_new < AIC ){
AIC = AIC_new;
list[memo] = dim;
#if MYPARA_LOG
printf("---> %dth variable, AIC:%f\n", memo, AIC);
#endif
/* copy and free */
SCIPfreeBufferArray(scip, &Y_);
SCIP_CALL( SCIPallocBufferArray(scip, &Y_, dim*dim));
mydcopy_( W_, Y_, dim*dim);
SCIPfreeBufferArray(scip, &a_old);
SCIP_CALL( SCIPallocBufferArray(scip, &a_old, dim));
mydcopy_( a, a_old, dim);
/* update X_ and Xy */
mydcopy_( &orig_X_[n * memo], &X_[n * (dim-1)], n);
Xy[dim-1] = orig_q[memo];
}else{
memo = -1;
SCIPfreeBufferArray(scip, Y_);
#if MYPARA_LOG
printf("--> no selection, (AIC:%f)\n", AIC_new);
#endif
}
/* free */
SCIPfreeBufferArray(scip, &a_new);
SCIPfreeBufferArray(scip, &a);
SCIPfreeBufferArray(scip, &b);
SCIPfreeBufferArray(scip, &c);
SCIPfreeBufferArray(scip, &v);
SCIPfreeBufferArray(scip, &V_);
SCIPfreeBufferArray(scip, &Z_);
SCIPfreeBufferArray(scip, &W_);
if( memo == -1 ){
dim--;
break;
}
}
nsols = SCIPgetNSols(scip);
if( nsols < MP_NUM_SOL ){
store = 1;
}else{
sols = SCIPgetSols(scip);
objval = AIC;
nsols = MP_NUM_SOL;
if( objval < SCIPgetSolOrigObj(scip,sols[nsols-1]) ){
store = 1;
}else{
store = 0;
}
}
if( store ){
/* generate solution */
/* alloc */
SCIP_CALL( SCIPallocBufferArray(scip, &ep, n));
dgemv_1( X_, n, dim, a_old, y, -1.0, 1.0, ep);
/* set solution */
/* alloc */
SCIP_CALL( SCIPallocBufferArray(scip, &solvals, nvars));
SCIP_CALL( SCIPallocBufferArray(scip, &vars, nvars));
ct=0;
/* a */
for(i=0; i<p; ++i){
vars[ct] = var_a[i];
if( list[i] > 0 ){
solvals[ct] = a_old[list[i]-1];
}else{
solvals[ct] = 0.0;
}
ct++;
}
/* z */
for(i=0; i<p; i++){
vars[ct] = var_z[i];
if( list[i] > 0 ){
solvals[ct] = 1.0;
}else{
solvals[ct] = 0.0;
}
ct++;
}
/* ep */
for(i=0; i<n; ++i){
vars[ct] = var_ep[i];
solvals[ct] = ep[i];
ct++;
}
vars[ct] = var_rss;
solvals[ct] = myddot_( ep, ep, n);
ct++;
vars[ct] = var_log;
solvals[ct] = log(myddot_( ep, ep, n));
ct++;
if( ct!=nvars ){
SCIPerrorMessage("It is unexpected error in set sol,");
printf("( ct, nvars) = ( %d, %d)", ct, nvars);
stop();
}
SCIP_CALL( SCIPcreateSol(scip, &sol, heur));
SCIP_CALL( SCIPsetSolVals(scip, sol, nvars, vars, solvals));
SCIP_CALL( SCIPtrySolFree(scip, &sol, TRUE, FALSE, TRUE, TRUE, &success));
/* free */
SCIPfreeBufferArray(scip, &ep);
SCIPfreeBufferArray(scip, &solvals);
SCIPfreeBufferArray(scip, &vars);
}
/* free */
SCIPfreeBufferArray(scip, &d);
SCIPfreeBufferArray(scip, &X_);
SCIPfreeBufferArray(scip, &Xy);
SCIPfreeBufferArray(scip, &a_old);
SCIPfreeBufferArray(scip, &list);
SCIPfreeBufferArray(scip, &Branchz);
*result = SCIP_FOUNDSOL;
return SCIP_OKAY;
}
/** 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 capacity;
SCIP_Real timelimit;
SCIP_Real memorylimit;
assert(scip != NULL);
assert(pricer != NULL);
(*result) = SCIP_DIDNOTRUN;
/* get the pricer data */
pricerdata = SCIPpricerGetData(pricer);
assert(pricerdata != NULL);
capacity = pricerdata->capacity;
conss = pricerdata->conss;
ids = pricerdata->ids;
nitems = pricerdata->nitems;
/* 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) );
/* create problem in sub SCIP */
SCIP_CALL( SCIPcreateProbBasic(subscip, "pricing") );
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) );
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(scip, "solution in pricing problem (capacity <%d>) is infeasible\n", capacity);
continue;
}
/* check if the solution has a value greater than 1.0 */
if( SCIPisFeasGT(subscip, SCIPgetSolOrigObj(subscip, sol), 1.0) )
{
SCIP_VAR* var;
SCIP_VARDATA* vardata;
int* consids;
char strtmp[SCIP_MAXSTRLEN];
char name[SCIP_MAXSTRLEN];
int nconss;
int o;
int v;
SCIPdebug( SCIP_CALL( SCIPprintSol(subscip, sol, NULL, FALSE) ) );
nconss = 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;
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, 1.0, 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) );
}
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;
}
/** LP solution separation method of separator */
static
SCIP_DECL_SEPAEXECLP(sepaExeclpStrongcg)
{ /*lint --e{715}*/
SCIP_SEPADATA* sepadata;
SCIP_VAR** vars;
SCIP_COL** cols;
SCIP_ROW** rows;
SCIP_Real* varsolvals;
SCIP_Real* binvrow;
SCIP_Real* cutcoefs;
SCIP_Real cutrhs;
SCIP_Real cutact;
SCIP_Real maxscale;
SCIP_Longint maxdnom;
int* basisind;
int* inds;
int ninds;
int nvars;
int ncols;
int nrows;
int ncalls;
int depth;
int maxdepth;
int maxsepacuts;
int ncuts;
int c;
int i;
int cutrank;
SCIP_Bool success;
SCIP_Bool cutislocal;
char normtype;
assert(sepa != NULL);
assert(strcmp(SCIPsepaGetName(sepa), SEPA_NAME) == 0);
assert(scip != NULL);
assert(result != NULL);
*result = SCIP_DIDNOTRUN;
sepadata = SCIPsepaGetData(sepa);
assert(sepadata != NULL);
depth = SCIPgetDepth(scip);
ncalls = SCIPsepaGetNCallsAtNode(sepa);
/* only call separator, if we are not close to terminating */
if( SCIPisStopped(scip) )
return SCIP_OKAY;
/* only call the strong CG cut separator a given number of times at each node */
if( (depth == 0 && sepadata->maxroundsroot >= 0 && ncalls >= sepadata->maxroundsroot)
|| (depth > 0 && sepadata->maxrounds >= 0 && ncalls >= sepadata->maxrounds) )
return SCIP_OKAY;
/* only call separator, if an optimal LP solution is at hand */
if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
return SCIP_OKAY;
/* only call separator, if the LP solution is basic */
if( !SCIPisLPSolBasic(scip) )
return SCIP_OKAY;
/* only call separator, if there are fractional variables */
if( SCIPgetNLPBranchCands(scip) == 0 )
return SCIP_OKAY;
/* get variables data */
SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
/* get LP data */
SCIP_CALL( SCIPgetLPColsData(scip, &cols, &ncols) );
SCIP_CALL( SCIPgetLPRowsData(scip, &rows, &nrows) );
if( ncols == 0 || nrows == 0 )
return SCIP_OKAY;
#if 0 /* if too many columns, separator is usually very slow: delay it until no other cuts have been found */
if( ncols >= 50*nrows )
return SCIP_OKAY;
if( ncols >= 5*nrows )
{
int ncutsfound;
ncutsfound = SCIPgetNCutsFound(scip);
if( ncutsfound > sepadata->lastncutsfound || !SCIPsepaWasLPDelayed(sepa) )
{
sepadata->lastncutsfound = ncutsfound;
*result = SCIP_DELAYED;
return SCIP_OKAY;
}
}
#endif
/* get the type of norm to use for efficacy calculations */
SCIP_CALL( SCIPgetCharParam(scip, "separating/efficacynorm", &normtype) );
/* set the maximal denominator in rational representation of strong CG cut and the maximal scale factor to
* scale resulting cut to integral values to avoid numerical instabilities
*/
/**@todo find better but still stable strong CG cut settings: look at dcmulti, gesa3, khb0525, misc06, p2756 */
maxdepth = SCIPgetMaxDepth(scip);
if( depth == 0 )
{
maxdnom = 1000;
maxscale = 1000.0;
}
else if( depth <= maxdepth/4 )
{
maxdnom = 1000;
maxscale = 1000.0;
}
else if( depth <= maxdepth/2 )
{
maxdnom = 100;
maxscale = 100.0;
}
else
{
maxdnom = 10;
maxscale = 10.0;
}
*result = SCIP_DIDNOTFIND;
/* allocate temporary memory */
SCIP_CALL( SCIPallocBufferArray(scip, &cutcoefs, nvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &basisind, nrows) );
SCIP_CALL( SCIPallocBufferArray(scip, &binvrow, nrows) );
SCIP_CALL( SCIPallocBufferArray(scip, &inds, nrows) );
varsolvals = NULL; /* allocate this later, if needed */
/* get basis indices */
SCIP_CALL( SCIPgetLPBasisInd(scip, basisind) );
/* get the maximal number of cuts allowed in a separation round */
if( depth == 0 )
maxsepacuts = sepadata->maxsepacutsroot;
else
maxsepacuts = sepadata->maxsepacuts;
SCIPdebugMessage("searching strong CG cuts: %d cols, %d rows, maxdnom=%" SCIP_LONGINT_FORMAT ", maxscale=%g, maxcuts=%d\n",
ncols, nrows, maxdnom, maxscale, maxsepacuts);
/* for all basic columns belonging to integer variables, try to generate a strong CG cut */
ncuts = 0;
for( i = 0; i < nrows && ncuts < maxsepacuts && !SCIPisStopped(scip) && *result != SCIP_CUTOFF; ++i )
{
SCIP_Bool tryrow;
tryrow = FALSE;
c = basisind[i];
if( c >= 0 )
{
SCIP_VAR* var;
assert(c < ncols);
var = SCIPcolGetVar(cols[c]);
if( SCIPvarGetType(var) != SCIP_VARTYPE_CONTINUOUS )
{
SCIP_Real primsol;
primsol = SCIPcolGetPrimsol(cols[c]);
assert(SCIPgetVarSol(scip, var) == primsol); /*lint !e777*/
if( SCIPfeasFrac(scip, primsol) >= MINFRAC )
{
SCIPdebugMessage("trying strong CG cut for col <%s> [%g]\n", SCIPvarGetName(var), primsol);
tryrow = TRUE;
}
}
}
#ifdef SEPARATEROWS
else
{
SCIP_ROW* row;
assert(0 <= -c-1 && -c-1 < nrows);
row = rows[-c-1];
if( SCIProwIsIntegral(row) && !SCIProwIsModifiable(row) )
{
SCIP_Real primsol;
primsol = SCIPgetRowActivity(scip, row);
if( SCIPfeasFrac(scip, primsol) >= MINFRAC )
{
SCIPdebugMessage("trying strong CG cut for row <%s> [%g]\n", SCIProwGetName(row), primsol);
tryrow = TRUE;
}
}
}
#endif
if( tryrow )
{
/* get the row of B^-1 for this basic integer variable with fractional solution value */
SCIP_CALL( SCIPgetLPBInvRow(scip, i, binvrow, inds, &ninds) );
#ifdef SCIP_DEBUG
/* initialize variables, that might not have been initialized in SCIPcalcMIR if success == FALSE */
cutact = 0.0;
cutrhs = SCIPinfinity(scip);
#endif
/* create a strong CG cut out of the weighted LP rows using the B^-1 row as weights */
SCIP_CALL( SCIPcalcStrongCG(scip, BOUNDSWITCH, USEVBDS, ALLOWLOCAL, (int) MAXAGGRLEN(nvars), sepadata->maxweightrange, MINFRAC, MAXFRAC,
binvrow, inds, ninds, 1.0, cutcoefs, &cutrhs, &cutact, &success, &cutislocal, &cutrank) );
assert(ALLOWLOCAL || !cutislocal);
SCIPdebugMessage(" -> success=%u: %g <= %g\n", success, cutact, cutrhs);
/* if successful, convert dense cut into sparse row, and add the row as a cut */
if( success && SCIPisFeasGT(scip, cutact, cutrhs) )
{
SCIP_VAR** cutvars;
SCIP_Real* cutvals;
SCIP_Real cutnorm;
int cutlen;
/* if this is the first successful cut, get the LP solution for all COLUMN variables */
if( varsolvals == NULL )
{
int v;
SCIP_CALL( SCIPallocBufferArray(scip, &varsolvals, nvars) );
for( v = 0; v < nvars; ++v )
{
if( SCIPvarGetStatus(vars[v]) == SCIP_VARSTATUS_COLUMN )
varsolvals[v] = SCIPvarGetLPSol(vars[v]);
}
}
assert(varsolvals != NULL);
/* get temporary memory for storing the cut as sparse row */
SCIP_CALL( SCIPallocBufferArray(scip, &cutvars, nvars) );
SCIP_CALL( SCIPallocBufferArray(scip, &cutvals, nvars) );
/* store the cut as sparse row, calculate activity and norm of cut */
SCIP_CALL( storeCutInArrays(scip, nvars, vars, cutcoefs, varsolvals, normtype,
cutvars, cutvals, &cutlen, &cutact, &cutnorm) );
SCIPdebugMessage(" -> strong CG cut for <%s>: act=%f, rhs=%f, norm=%f, eff=%f, rank=%d\n",
c >= 0 ? SCIPvarGetName(SCIPcolGetVar(cols[c])) : SCIProwGetName(rows[-c-1]),
cutact, cutrhs, cutnorm, (cutact - cutrhs)/cutnorm, cutrank);
if( SCIPisPositive(scip, cutnorm) && SCIPisEfficacious(scip, (cutact - cutrhs)/cutnorm) )
{
SCIP_ROW* cut;
char cutname[SCIP_MAXSTRLEN];
/* create the cut */
if( c >= 0 )
(void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "scg%d_x%d", SCIPgetNLPs(scip), c);
else
(void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "scg%d_s%d", SCIPgetNLPs(scip), -c-1);
SCIP_CALL( SCIPcreateEmptyRowSepa(scip, &cut, sepa, cutname, -SCIPinfinity(scip), cutrhs, cutislocal, FALSE, sepadata->dynamiccuts) );
SCIP_CALL( SCIPaddVarsToRow(scip, cut, cutlen, cutvars, cutvals) );
/*SCIPdebug( SCIP_CALL(SCIPprintRow(scip, cut, NULL)) );*/
SCIProwChgRank(cut, cutrank);
assert(success);
#ifdef MAKECUTINTEGRAL
/* try to scale the cut to integral values */
SCIP_CALL( SCIPmakeRowIntegral(scip, cut, -SCIPepsilon(scip), SCIPsumepsilon(scip),
maxdnom, maxscale, MAKECONTINTEGRAL, &success) );
#else
#ifdef MAKEINTCUTINTEGRAL
/* try to scale the cut to integral values if there are no continuous variables
* -> leads to an integral slack variable that can later be used for other cuts
*/
{
int k = 0;
while ( k < cutlen && SCIPvarIsIntegral(cutvars[k]) )
++k;
if( k == cutlen )
{
SCIP_CALL( SCIPmakeRowIntegral(scip, cut, -SCIPepsilon(scip), SCIPsumepsilon(scip),
maxdnom, maxscale, MAKECONTINTEGRAL, &success) );
}
}
#endif
#endif
#ifndef FORCECUTINTEGRAL
success = TRUE;
#endif
if( success )
{
if( !SCIPisCutEfficacious(scip, NULL, cut) )
{
SCIPdebugMessage(" -> strong CG cut <%s> no longer efficacious: act=%f, rhs=%f, norm=%f, eff=%f\n",
cutname, SCIPgetRowLPActivity(scip, cut), SCIProwGetRhs(cut), SCIProwGetNorm(cut),
SCIPgetCutEfficacy(scip, NULL, cut));
/*SCIPdebug( SCIP_CALL(SCIPprintRow(scip, cut, NULL)) );*/
success = FALSE;
}
else
{
SCIP_Bool infeasible;
SCIPdebugMessage(" -> found strong CG cut <%s>: act=%f, rhs=%f, norm=%f, eff=%f, min=%f, max=%f (range=%f)\n",
cutname, SCIPgetRowLPActivity(scip, cut), SCIProwGetRhs(cut), SCIProwGetNorm(cut),
SCIPgetCutEfficacy(scip, NULL, cut),
SCIPgetRowMinCoef(scip, cut), SCIPgetRowMaxCoef(scip, cut),
SCIPgetRowMaxCoef(scip, cut)/SCIPgetRowMinCoef(scip, cut));
/*SCIPdebug( SCIP_CALL(SCIPprintRow(scip, cut, NULL)) );*/
SCIP_CALL( SCIPaddCut(scip, NULL, cut, FALSE, &infeasible) );
if ( infeasible )
*result = SCIP_CUTOFF;
else
{
if( !cutislocal )
{
SCIP_CALL( SCIPaddPoolCut(scip, cut) );
}
*result = SCIP_SEPARATED;
}
ncuts++;
}
}
else
{
SCIPdebugMessage(" -> strong CG cut <%s> couldn't be scaled to integral coefficients: act=%f, rhs=%f, norm=%f, eff=%f\n",
cutname, cutact, cutrhs, cutnorm, SCIPgetCutEfficacy(scip, NULL, cut));
}
/* release the row */
SCIP_CALL( SCIPreleaseRow(scip, &cut) );
}
/* free temporary memory */
SCIPfreeBufferArray(scip, &cutvals);
SCIPfreeBufferArray(scip, &cutvars);
}
}
}
/* free temporary memory */
SCIPfreeBufferArrayNull(scip, &varsolvals);
SCIPfreeBufferArray(scip, &inds);
SCIPfreeBufferArray(scip, &binvrow);
SCIPfreeBufferArray(scip, &basisind);
SCIPfreeBufferArray(scip, &cutcoefs);
SCIPdebugMessage("end searching strong CG cuts: found %d cuts\n", ncuts);
sepadata->lastncutsfound = SCIPgetNCutsFound(scip);
return SCIP_OKAY;
}
/** evalutate solution */
SCIP_RETCODE LOPevalSolution(
SCIP* scip /**< SCIP data structure */
)
{
SCIP_PROBDATA* probdata;
SCIP_VAR*** vars;
SCIP_SOL* sol;
int* outDegree;
int* indices;
int i;
int j;
int n;
/* get problem data */
probdata = SCIPgetProbData(scip);
assert( probdata != NULL );
assert( probdata->vars != NULL );
n = probdata->n;
vars = probdata->vars;
sol = SCIPgetBestSol(scip);
if ( sol == NULL )
printf("\nNo solution found.\n");
else
{
SCIP_CALL( SCIPallocBufferArray(scip, &outDegree, n) );
SCIP_CALL( SCIPallocBufferArray(scip, &indices, n) );
/* compute out-degree */
for (i = 0; i < n; ++i)
{
int deg = 0;
for (j = 0; j < n; ++j)
{
SCIP_Real val;
if (j == i)
continue;
val = SCIPgetSolVal(scip, sol, vars[i][j]);
assert( SCIPisIntegral(scip, val) );
if ( val < 0.5 )
++deg;
}
outDegree[i] = deg;
indices[i] = i;
}
/* sort such that degrees are non-decreasing */
SCIPsortIntInt(outDegree, indices, n);
/* output */
printf("\nFinal order:\n");
for (i = 0; i < n; ++i)
printf("%d ", indices[i]);
printf("\n");
SCIPfreeBufferArray(scip, &indices);
SCIPfreeBufferArray(scip, &outDegree);
}
return SCIP_OKAY;
}
/** problem reading method of reader */
static
SCIP_DECL_READERREAD(readerReadCip)
{ /*lint --e{715}*/
CIPINPUT cipinput;
SCIP_Real objscale;
SCIP_Real objoffset;
SCIP_Bool dynamicconss;
SCIP_Bool dynamiccols;
SCIP_Bool dynamicrows;
SCIP_Bool initialvar;
SCIP_Bool removablevar;
SCIP_Bool initialcons;
SCIP_Bool removablecons;
if( NULL == (cipinput.file = SCIPfopen(filename, "r")) )
{
SCIPerrorMessage("cannot open file <%s> for reading\n", filename);
SCIPprintSysError(filename);
return SCIP_NOFILE;
}
cipinput.len = 131071;
SCIP_CALL( SCIPallocBufferArray(scip, &(cipinput.strbuf), cipinput.len) );
cipinput.linenumber = 0;
cipinput.section = CIP_START;
cipinput.haserror = FALSE;
cipinput.endfile = FALSE;
cipinput.readingsize = 65535;
SCIP_CALL( SCIPcreateProb(scip, filename, NULL, NULL, NULL, NULL, NULL, NULL, NULL) );
SCIP_CALL( SCIPgetBoolParam(scip, "reading/"READER_NAME"/dynamiccols", &dynamiccols) );
SCIP_CALL( SCIPgetBoolParam(scip, "reading/"READER_NAME"/dynamicconss", &dynamicconss) );
SCIP_CALL( SCIPgetBoolParam(scip, "reading/"READER_NAME"/dynamicrows", &dynamicrows) );
initialvar = !dynamiccols;
removablevar = dynamiccols;
initialcons = !dynamicrows;
removablecons = dynamicrows;
objscale = 1.0;
objoffset = 0.0;
while( cipinput.section != CIP_END && !cipinput.haserror )
{
/* get next input string */
SCIP_CALL( getInputString(scip, &cipinput) );
if( cipinput.endfile )
break;
switch( cipinput.section )
{
case CIP_START:
SCIP_CALL( getStart(scip, &cipinput) );
break;
case CIP_STATISTIC:
SCIP_CALL( getStatistic(scip, &cipinput) );
break;
case CIP_OBJECTIVE:
SCIP_CALL( getObjective(scip, &cipinput, &objscale, &objoffset) );
break;
case CIP_VARS:
SCIP_CALL( getVariable(scip, &cipinput, initialvar, removablevar, objscale) );
break;
case CIP_FIXEDVARS:
SCIP_CALL( getFixedVariables(scip, &cipinput) );
break;
case CIP_CONSTRAINTS:
SCIP_CALL( getConstraints(scip, &cipinput, initialcons, dynamicconss, removablecons) );
break;
default:
SCIPerrorMessage("invalid CIP state\n");
SCIPABORT();
} /*lint !e788*/
}
if( !SCIPisZero(scip, objoffset) && !cipinput.haserror )
{
SCIP_VAR* objoffsetvar;
objoffset *= objscale;
SCIP_CALL( SCIPcreateVar(scip, &objoffsetvar, "objoffset", objoffset, objoffset, 1.0, SCIP_VARTYPE_CONTINUOUS,
TRUE, TRUE, NULL, NULL, NULL, NULL, NULL) );
SCIP_CALL( SCIPaddVar(scip, objoffsetvar) );
SCIP_CALL( SCIPreleaseVar(scip, &objoffsetvar) );
SCIPdebugMessage("added variables <objoffset> for objective offset of <%g>\n", objoffset);
}
/* close file stream */
SCIPfclose(cipinput.file);
if( cipinput.section != CIP_END && !cipinput.haserror )
{
SCIPerrorMessage("unexpected EOF\n");
}
SCIPfreeBufferArray(scip, &cipinput.strbuf);
if( cipinput.haserror )
return SCIP_READERROR;
/* successfully parsed cip format */
*result = SCIP_SUCCESS;
return SCIP_OKAY;
}
