/** initialization method of event handler (called after problem was transformed) */
static
SCIP_DECL_EVENTINIT(eventInitSofttimelimit)
{ /*lint --e{715}*/
SCIP_EVENTHDLRDATA* eventhdlrdata;
assert(scip != NULL);
assert(eventhdlr != NULL);
assert(strcmp(SCIPeventhdlrGetName(eventhdlr), EVENTHDLR_NAME) == 0);
eventhdlrdata = SCIPeventhdlrGetData(eventhdlr);
assert(eventhdlrdata != NULL);
if( eventhdlrdata->filterpos < 0 && !SCIPisNegative(scip, eventhdlrdata->softtimelimit) )
{
/* notify SCIP that your event handler wants to react on the event type best solution found */
SCIP_CALL( SCIPcatchEvent(scip, SCIP_EVENTTYPE_BESTSOLFOUND, eventhdlr, NULL, &eventhdlrdata->filterpos) );
}
return SCIP_OKAY;
}
/** perform dual presolving */
static
SCIP_RETCODE performDualfix(
SCIP* scip, /**< SCIP data structure */
int* nfixedvars, /**< pointer to store number of fixed variables */
SCIP_Bool* unbounded, /**< pointer to store if an unboundness was detected */
SCIP_Bool* cutoff /**< pointer to store if a cutoff was detected */
)
{
SCIP_VAR** vars;
int nvars;
int v;
/* get active problem variables */
vars = SCIPgetVars(scip);
nvars = SCIPgetNVars(scip);
/* look for fixable variables
* loop backwards, since a variable fixing can change the current and the subsequent slots in the vars array
*/
for( v = nvars - 1; v >= 0; --v )
{
SCIP_VAR* var;
SCIP_Real bound;
SCIP_Real obj;
SCIP_Bool infeasible;
SCIP_Bool fixed;
var = vars[v];
assert(var != NULL);
/* don't perform dual presolving operations on deleted variables */
if( SCIPvarIsDeleted(var) )
continue;
/* ignore already fixed variables (use feasibility tolerance since this is used in SCIPfixVar() */
if( SCIPisFeasEQ(scip, SCIPvarGetLbLocal(var), SCIPvarGetUbLocal(var)) )
continue;
obj = SCIPvarGetObj(var);
/* if the objective coefficient of the variable is 0 and it may be rounded both
* up and down, then fix it to the closest feasible value to 0 */
if( SCIPisZero(scip, obj) && SCIPvarMayRoundDown(var) && SCIPvarMayRoundUp(var) )
{
SCIP_Real roundbound;
bound = SCIPvarGetLbGlobal(var);
if( SCIPisLT(scip, bound, 0.0) )
{
if( SCIPisLE(scip, 0.0, SCIPvarGetUbGlobal(var)) )
bound = 0.0;
else
{
/* try to take an integer value, only for polishing */
roundbound = SCIPfloor(scip, SCIPvarGetUbGlobal(var));
if( roundbound < bound )
bound = SCIPvarGetUbGlobal(var);
else
bound = roundbound;
}
}
else
{
/* try to take an integer value, only for polishing */
roundbound = SCIPceil(scip, bound);
if( roundbound < SCIPvarGetUbGlobal(var) )
bound = roundbound;
}
SCIPdebugMessage("fixing variable <%s> with objective 0 to %g\n", SCIPvarGetName(var), bound);
}
else
{
/* if it is always possible to round variable in direction of objective value, fix it to its proper bound */
if( SCIPvarMayRoundDown(var) && !SCIPisNegative(scip, obj) )
{
bound = SCIPvarGetLbGlobal(var);
if ( SCIPisInfinity(scip, -bound) )
{
/* variable can be fixed to -infinity */
if ( SCIPgetStage(scip) > SCIP_STAGE_PRESOLVING )
{
/* Fixing variables to infinity is not allowed after presolving, since LP-solvers cannot handle this
* consistently. We thus have to ignore this (should better be handled in presolving). */
continue;
}
if ( SCIPisZero(scip, obj) && SCIPvarGetNLocksUp(var) == 1 )
{
/* Variable is only contained in one constraint: we hope that the corresponding constraint handler is
* clever enough to set/aggregate the variable to something more useful than -infinity and do nothing
* here. */
continue;
}
}
SCIPdebugMessage("fixing variable <%s> with objective %g and %d uplocks to lower bound %g\n",
SCIPvarGetName(var), SCIPvarGetObj(var), SCIPvarGetNLocksUp(var), bound);
}
else if( SCIPvarMayRoundUp(var) && !SCIPisPositive(scip, obj) )
{
bound = SCIPvarGetUbGlobal(var);
if ( SCIPisInfinity(scip, bound) )
{
/* variable can be fixed to infinity */
if ( SCIPgetStage(scip) > SCIP_STAGE_PRESOLVING )
{
/* Fixing variables to infinity is not allowed after presolving, since LP-solvers cannot handle this
* consistently. We thus have to ignore this (should better be handled in presolving). */
continue;
}
if ( SCIPisZero(scip, obj) && SCIPvarGetNLocksDown(var) == 1 )
{
/* Variable is only contained in one constraint: we hope that the corresponding constraint handler is
* clever enough to set/aggregate the variable to something more useful than +infinity and do nothing
* here */
continue;
}
}
SCIPdebugMessage("fixing variable <%s> with objective %g and %d downlocks to upper bound %g\n",
SCIPvarGetName(var), SCIPvarGetObj(var), SCIPvarGetNLocksDown(var), bound);
}
else
continue;
}
if( SCIPisInfinity(scip, REALABS(bound)) && !SCIPisZero(scip, obj) )
{
SCIPdebugMessage(" -> unbounded fixing\n");
SCIPverbMessage(scip, SCIP_VERBLEVEL_NORMAL, NULL,
"problem infeasible or unbounded: variable <%s> with objective %.15g can be made infinitely %s\n",
SCIPvarGetName(var), SCIPvarGetObj(var), bound < 0.0 ? "small" : "large");
*unbounded = TRUE;
return SCIP_OKAY;
}
/* apply the fixing */
SCIPdebugMessage("apply fixing of variable %s to %g\n", SCIPvarGetName(var), bound);
SCIP_CALL( SCIPfixVar(scip, var, bound, &infeasible, &fixed) );
if( infeasible )
{
SCIPdebugMessage(" -> infeasible fixing\n");
*cutoff = TRUE;
return SCIP_OKAY;
}
assert(fixed || (SCIPgetStage(scip) == SCIP_STAGE_SOLVING && SCIPisFeasEQ(scip, bound, SCIPvarGetLbLocal(var))
&& SCIPisFeasEQ(scip, bound, SCIPvarGetUbLocal(var))));
(*nfixedvars)++;
}
return SCIP_OKAY;
}
/** method for either Farkas or Redcost pricing */
static
SCIP_RETCODE pricing(
SCIP* scip, /**< SCIP data structure */
SCIP_PRICER* pricer, /**< pricer */
SCIP_Real* lowerbound, /**< lowerbound pointer */
SCIP_Bool farkas /**< TRUE: Farkas pricing; FALSE: Redcost pricing */
)
{
SCIP_PRICERDATA* pricerdata; /* the data of the pricer */
SCIP_PROBDATA* probdata;
GRAPH* graph;
SCIP_VAR* var;
PATH* path;
SCIP_Real* edgecosts; /* edgecosts of the current subproblem */
char varname[SCIP_MAXSTRLEN];
SCIP_Real newlowerbound = -SCIPinfinity(scip);
SCIP_Real redcost; /* reduced cost */
int tail;
int e;
int t;
int i;
assert(scip != NULL);
assert(pricer != NULL);
/* get pricer data */
pricerdata = SCIPpricerGetData(pricer);
assert(pricerdata != NULL);
/* get problem data */
probdata = SCIPgetProbData(scip);
assert(probdata != NULL);
SCIPdebugMessage("solstat=%d\n", SCIPgetLPSolstat(scip));
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
newlowerbound = SCIPgetSolTransObj(scip, NULL);
SCIPdebug( SCIP_CALL( SCIPprintSol(scip, NULL, NULL, FALSE) ) );
# if 0
if ( pricerdata->lowerbound <= 4 )
{
char label[SCIP_MAXSTRLEN];
(void)SCIPsnprintf(label, SCIP_MAXSTRLEN, "X%g.gml", pricerdata->lowerbound);
SCIP_CALL( SCIPprobdataPrintGraph(scip, label , NULL, TRUE) );
pricerdata->lowerbound++;
}
#endif
/* get the graph*/
graph = SCIPprobdataGetGraph(probdata);
/* get dual solutions and save them in mi and pi */
for( t = 0; t < pricerdata->realnterms; ++t )
{
if( farkas )
{
pricerdata->mi[t] = SCIPgetDualfarkasLinear(scip, pricerdata->pathcons[t]);
}
else
{
pricerdata->mi[t] = SCIPgetDualsolLinear(scip, pricerdata->pathcons[t]);
assert(!SCIPisNegative(scip, pricerdata->mi[t]));
}
}
for( e = 0; e < pricerdata->nedges; ++e )
{
if( !pricerdata->bigt )
{
for( t = 0; t < pricerdata->realnterms; ++t )
{
if( farkas )
{
pricerdata->pi[t * pricerdata->nedges + e] = SCIPgetDualfarkasLinear(
scip, pricerdata->edgecons[t * pricerdata->nedges + e]);
}
else
{
pricerdata->pi[t * pricerdata->nedges + e] = SCIPgetDualsolLinear(
scip, pricerdata->edgecons[t * pricerdata->nedges + e]);
}
}
}
else
{
if( farkas )
{
pricerdata->pi[e] = SCIPgetDualfarkasLinear(
scip, pricerdata->edgecons[e]);
}
else
{
pricerdata->pi[e] = SCIPgetDualsolLinear(
scip, pricerdata->edgecons[e]);
}
}
}
SCIP_CALL( SCIPallocMemoryArray(scip, &path, graph->knots) );
SCIP_CALL( SCIPallocMemoryArray(scip, &edgecosts, pricerdata->nedges) );
if( pricerdata->bigt )
{
for( e = 0; e < pricerdata->nedges; ++e )
{
edgecosts[e] = (-pricerdata->pi[e]);
}
}
/* find shortest r-t (r root, t terminal) paths and create corresponding variables iff reduced cost < 0 */
for( t = 0; t < pricerdata->realnterms; ++t )
{
for( e = 0; e < pricerdata->nedges; ++e )
{
if( !pricerdata->bigt )
{
edgecosts[e] = (-pricerdata->pi[t * pricerdata->nedges + e]);
}
assert(!SCIPisNegative(scip, edgecosts[e]));
}
for( i = 0; i < graph->knots; i++ )
graph->mark[i] = 1;
graph_path_exec(scip, graph, FSP_MODE, pricerdata->root, edgecosts, path);
/* compute reduced cost of shortest path to terminal t */
redcost = 0.0;
tail = pricerdata->realterms[t];
while( tail != pricerdata->root )
{
redcost += edgecosts[path[tail].edge];
tail = graph->tail[path[tail].edge];
}
redcost -= pricerdata->mi[t];
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
{
newlowerbound += redcost;
}
/* check if reduced cost < 0 */
if( SCIPisNegative(scip, redcost) )
{
/* create variable to the shortest path (having reduced cost < 0) */
var = NULL;
sprintf(varname, "PathVar%d_%d", t, pricerdata->ncreatedvars[t]);
++(pricerdata->ncreatedvars[t]);
SCIP_CALL( SCIPcreateVarBasic(scip, &var, varname, 0.0, SCIPinfinity(scip), 0.0, SCIP_VARTYPE_CONTINUOUS) );
SCIP_CALL( SCIPaddPricedVar(scip, var, -redcost) );
tail = pricerdata->realterms[t];
while( tail != pricerdata->root )
{
/* add variable to constraints */
if( !pricerdata->bigt )
{
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->edgecons[t * pricerdata->nedges + path[tail].edge], var, 1.0) );
}
else
{
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->edgecons[path[tail].edge], var, 1.0) );
}
tail = graph->tail[path[tail].edge];
}
SCIP_CALL( SCIPaddCoefLinear(scip, pricerdata->pathcons[t], var, 1.0) );
}
}
if( !farkas && SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
*lowerbound = newlowerbound;
SCIPfreeMemoryArray(scip, &edgecosts);
SCIPfreeMemoryArray(scip, &path);
return SCIP_OKAY;
}
/** checks if a given branching candidate is better than a previous one and updates the best branching candidate accordingly */
static
SCIP_RETCODE updateBestCandidate(
SCIP* scip, /**< SCIP data structure */
SCIP_BRANCHRULEDATA* branchruledata, /**< branching rule data */
SCIP_VAR** bestvar, /**< best branching candidate */
SCIP_Real* bestbrpoint, /**< branching point for best branching candidate */
SCIP_Real* bestscore, /**< score of best branching candidate */
SCIP_VAR* cand, /**< branching candidate to consider */
SCIP_Real candscoremin, /**< minimal score of branching candidate */
SCIP_Real candscoremax, /**< maximal score of branching candidate */
SCIP_Real candscoresum, /**< sum of scores of branching candidate */
SCIP_Real candsol /**< proposed branching point of branching candidate */
)
{
SCIP_Real candbrpoint;
SCIP_Real branchscore;
SCIP_Real deltaminus;
SCIP_Real deltaplus;
SCIP_Real pscostdown;
SCIP_Real pscostup;
char strategy;
assert(scip != NULL);
assert(branchruledata != NULL);
assert(bestvar != NULL);
assert(bestbrpoint != NULL);
assert(bestscore != NULL);
assert(cand != NULL);
/* a branching variable candidate should either be an active problem variable or a multi-aggregated variable */
assert(SCIPvarIsActive(SCIPvarGetProbvar(cand)) ||
SCIPvarGetStatus(SCIPvarGetProbvar(cand)) == SCIP_VARSTATUS_MULTAGGR);
if( SCIPvarGetStatus(SCIPvarGetProbvar(cand)) == SCIP_VARSTATUS_MULTAGGR )
{
/* for a multi-aggregated variable, we call updateBestCandidate function recursively with all variables in the multi-aggregation */
SCIP_VAR** multvars;
int nmultvars;
int i;
SCIP_Bool success;
SCIP_Real multvarlb;
SCIP_Real multvarub;
cand = SCIPvarGetProbvar(cand);
multvars = SCIPvarGetMultaggrVars(cand);
nmultvars = SCIPvarGetMultaggrNVars(cand);
/* if we have a candidate branching point, then first register only aggregation variables
* for which we can compute a corresponding branching point too (see also comments below)
* if this fails, then register all (unfixed) aggregation variables, thereby forgetting about candsol
*/
success = FALSE;
if( candsol != SCIP_INVALID ) /*lint !e777*/
{
SCIP_Real* multscalars;
SCIP_Real minact;
SCIP_Real maxact;
SCIP_Real aggrvarsol;
SCIP_Real aggrvarsol1;
SCIP_Real aggrvarsol2;
multscalars = SCIPvarGetMultaggrScalars(cand);
/* for computing the branching point, we need the current bounds of the multi-aggregated variable */
minact = SCIPcomputeVarLbLocal(scip, cand);
maxact = SCIPcomputeVarUbLocal(scip, cand);
for( i = 0; i < nmultvars; ++i )
{
/* skip fixed variables */
multvarlb = SCIPcomputeVarLbLocal(scip, multvars[i]);
multvarub = SCIPcomputeVarUbLocal(scip, multvars[i]);
if( SCIPisEQ(scip, multvarlb, multvarub) )
continue;
assert(multscalars != NULL);
assert(multscalars[i] != 0.0);
/* we cannot ensure that both the upper bound in the left node and the lower bound in the right node
* will be candsol by a clever choice for the branching point of multvars[i],
* but we can try to ensure that at least one of them will be at candsol
*/
if( multscalars[i] > 0.0 )
{
/* cand >= candsol
* if multvars[i] >= (candsol - (maxact - multscalars[i] * ub(multvars[i]))) / multscalars[i]
* = (candsol - maxact) / multscalars[i] + ub(multvars[i])
*/
aggrvarsol1 = (candsol - maxact) / multscalars[i] + multvarub;
/* cand <= candsol
* if multvars[i] <= (candsol - (minact - multscalar[i] * lb(multvars[i]))) / multscalars[i]
* = (candsol - minact) / multscalars[i] + lb(multvars[i])
*/
aggrvarsol2 = (candsol - minact) / multscalars[i] + multvarlb;
}
else
{
/* cand >= candsol
* if multvars[i] <= (candsol - (maxact - multscalars[i] * lb(multvars[i]))) / multscalars[i]
* = (candsol - maxact) / multscalars[i] + lb(multvars[i])
*/
aggrvarsol2 = (candsol - maxact) / multscalars[i] + multvarlb;
/* cand <= candsol
* if multvars[i] >= (candsol - (minact - multscalar[i] * ub(multvars[i]))) / multscalars[i]
* = (candsol - minact) / multscalars[i] + ub(multvars[i])
*/
aggrvarsol1 = (candsol - minact) / multscalars[i] + multvarub;
}
/* by the above choice, aggrvarsol1 <= ub(multvars[i]) and aggrvarsol2 >= lb(multvars[i])
* if aggrvarsol1 <= lb(multvars[i]) or aggrvarsol2 >= ub(multvars[i]), then choose the other one
* if both are out of bounds, then give up
* if both are inside bounds, then choose the one closer to 0.0 (someone has better idea???)
*/
if( SCIPisFeasLE(scip, aggrvarsol1, multvarlb) )
{
if( SCIPisFeasGE(scip, aggrvarsol2, multvarub) )
continue;
else
aggrvarsol = aggrvarsol2;
}
else
{
if( SCIPisFeasGE(scip, aggrvarsol2, multvarub) )
aggrvarsol = aggrvarsol1;
else
aggrvarsol = REALABS(aggrvarsol1) < REALABS(aggrvarsol2) ? aggrvarsol1 : aggrvarsol2;
}
success = TRUE;
SCIP_CALL( updateBestCandidate(scip, branchruledata, bestvar, bestbrpoint, bestscore,
multvars[i], candscoremin, candscoremax, candscoresum, aggrvarsol) );
}
}
if( !success )
for( i = 0; i < nmultvars; ++i )
{
/* skip fixed variables */
multvarlb = SCIPcomputeVarLbLocal(scip, multvars[i]);
multvarub = SCIPcomputeVarUbLocal(scip, multvars[i]);
if( SCIPisEQ(scip, multvarlb, multvarub) )
continue;
SCIP_CALL( updateBestCandidate(scip, branchruledata, bestvar, bestbrpoint, bestscore,
multvars[i], candscoremin, candscoremax, candscoresum, SCIP_INVALID) );
}
assert(*bestvar != NULL); /* if all variables were fixed, something is strange */
return SCIP_OKAY;
}
/* select branching point for this variable */
candbrpoint = SCIPgetBranchingPoint(scip, cand, candsol);
assert(candbrpoint >= SCIPvarGetLbLocal(cand));
assert(candbrpoint <= SCIPvarGetUbLocal(cand));
/* we cannot branch on a huge value for a discrete variable, because we simply cannot enumerate such huge integer values in floating point
* arithmetics
*/
if( SCIPvarGetType(cand) != SCIP_VARTYPE_CONTINUOUS && (SCIPisHugeValue(scip, candbrpoint) || SCIPisHugeValue(scip, -candbrpoint)) )
return SCIP_OKAY;
assert(SCIPvarGetType(cand) == SCIP_VARTYPE_CONTINUOUS || !SCIPisIntegral(scip, candbrpoint));
if( SCIPvarGetType(cand) == SCIP_VARTYPE_CONTINUOUS )
strategy = (branchruledata->strategy == 'u' ? branchruledata->updatestrategy : branchruledata->strategy);
else
strategy = (branchruledata->strategy == 'u' ? 'l' : branchruledata->strategy);
switch( strategy )
{
case 'l':
if( SCIPisInfinity(scip, SCIPgetSolVal(scip, NULL, cand)) || SCIPgetSolVal(scip, NULL, cand) <= SCIPadjustedVarUb(scip, cand, candbrpoint) )
deltaminus = 0.0;
else
deltaminus = SCIPgetSolVal(scip, NULL, cand) - SCIPadjustedVarUb(scip, cand, candbrpoint);
if( SCIPisInfinity(scip, -SCIPgetSolVal(scip, NULL, cand)) || SCIPgetSolVal(scip, NULL, cand) >= SCIPadjustedVarLb(scip, cand, candbrpoint) )
deltaplus = 0.0;
else
deltaplus = SCIPadjustedVarLb(scip, cand, candbrpoint) - SCIPgetSolVal(scip, NULL, cand);
break;
case 'd':
if( SCIPisInfinity(scip, -SCIPvarGetLbLocal(cand)) )
deltaminus = SCIPisInfinity(scip, candscoremax) ? SCIPinfinity(scip) : WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum);
else
deltaminus = SCIPadjustedVarUb(scip, cand, candbrpoint) - SCIPvarGetLbLocal(cand);
if( SCIPisInfinity(scip, SCIPvarGetUbLocal(cand)) )
deltaplus = SCIPisInfinity(scip, candscoremax) ? SCIPinfinity(scip) : WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum);
else
deltaplus = SCIPvarGetUbLocal(cand) - SCIPadjustedVarLb(scip, cand, candbrpoint);
break;
case 's':
if( SCIPisInfinity(scip, -SCIPvarGetLbLocal(cand)) )
deltaplus = SCIPisInfinity(scip, candscoremax) ? SCIPinfinity(scip) : WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum);
else
deltaplus = SCIPadjustedVarUb(scip, cand, candbrpoint) - SCIPvarGetLbLocal(cand);
if( SCIPisInfinity(scip, SCIPvarGetUbLocal(cand)) )
deltaminus = SCIPisInfinity(scip, candscoremax) ? SCIPinfinity(scip) : WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum);
else
deltaminus = SCIPvarGetUbLocal(cand) - SCIPadjustedVarLb(scip, cand, candbrpoint);
break;
case 'v':
deltaplus = SCIPisInfinity(scip, candscoremax) ? SCIPinfinity(scip) : WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum);
deltaminus = deltaplus;
break;
default :
SCIPerrorMessage("branching strategy %c unknown\n", strategy);
SCIPABORT();
return SCIP_INVALIDDATA; /*lint !e527*/
}
if( SCIPisInfinity(scip, deltaminus) || SCIPisInfinity(scip, deltaplus) )
{
branchscore = SCIPinfinity(scip);
}
else
{
pscostdown = SCIPgetVarPseudocostVal(scip, cand, -deltaminus);
pscostup = SCIPgetVarPseudocostVal(scip, cand, deltaplus);
branchscore = SCIPgetBranchScore(scip, cand, pscostdown, pscostup);
assert(!SCIPisNegative(scip, branchscore));
}
SCIPdebugMessage("branching score variable <%s>[%g,%g] = %g; wscore = %g; type=%d bestbrscore=%g\n",
SCIPvarGetName(cand), SCIPvarGetLbLocal(cand), SCIPvarGetUbLocal(cand), branchscore, WEIGHTEDSCORING(branchruledata, candscoremin, candscoremax, candscoresum),
SCIPvarGetType(cand), *bestscore);
if( SCIPisInfinity(scip, branchscore) )
branchscore = 0.9*SCIPinfinity(scip);
if( SCIPisSumGT(scip, branchscore, *bestscore) )
{
(*bestscore) = branchscore;
(*bestvar) = cand;
(*bestbrpoint) = candbrpoint;
}
else if( SCIPisSumEQ(scip, branchscore, *bestscore)
&& !(SCIPisInfinity(scip, -SCIPvarGetLbLocal(*bestvar)) && SCIPisInfinity(scip, SCIPvarGetUbLocal(*bestvar))) )
{
/* if best candidate so far is not unbounded to both sides, maybe take new candidate */
if( (SCIPisInfinity(scip, -SCIPvarGetLbLocal(cand)) || SCIPisInfinity(scip, SCIPvarGetUbLocal(cand))) &&
(SCIPisInfinity(scip, -SCIPvarGetLbLocal(*bestvar)) || SCIPisInfinity(scip, SCIPvarGetUbLocal(*bestvar))) )
{
/* if both variables are unbounded but one of them is bounded on one side, take the one with the larger bound on this side (hope that this avoids branching on always the same variable) */
if( SCIPvarGetUbLocal(cand) > SCIPvarGetUbLocal(*bestvar) || SCIPvarGetLbLocal(cand) < SCIPvarGetLbLocal(*bestvar) )
{
(*bestscore) = branchscore;
(*bestvar) = cand;
(*bestbrpoint) = candbrpoint;
}
}
else if( SCIPvarGetType(*bestvar) == SCIPvarGetType(cand) )
{
/* if both have the same type, take the one with larger diameter */
if( SCIPvarGetUbLocal(*bestvar) - SCIPvarGetLbLocal(*bestvar) < SCIPvarGetUbLocal(cand) - SCIPvarGetLbLocal(cand) )
{
(*bestscore) = branchscore;
(*bestvar) = cand;
(*bestbrpoint) = candbrpoint;
}
}
else if( SCIPvarGetType(*bestvar) > SCIPvarGetType(cand) )
{
/* take the one with better type ("more discrete") */
(*bestscore) = branchscore;
(*bestvar) = cand;
(*bestbrpoint) = candbrpoint;
}
}
return SCIP_OKAY;
}
/** execution method of presolver */
static
SCIP_DECL_PRESOLEXEC(presolExecDualfix)
{ /*lint --e{715}*/
SCIP_VAR** vars;
SCIP_Real bound;
SCIP_Real roundbound;
SCIP_Real obj;
SCIP_Bool infeasible;
SCIP_Bool fixed;
int nvars;
int v;
assert(presol != NULL);
assert(strcmp(SCIPpresolGetName(presol), PRESOL_NAME) == 0);
assert(result != NULL);
*result = SCIP_DIDNOTFIND;
/* get active problem variables */
vars = SCIPgetVars(scip);
nvars = SCIPgetNVars(scip);
/* look for fixable variables
* loop backwards, since a variable fixing can change the current and the subsequent slots in the vars array
*/
for( v = nvars - 1; v >= 0; --v )
{
/* don't perform dual presolving operations on deleted variables */
if( SCIPvarIsDeleted(vars[v]) )
continue;
obj = SCIPvarGetObj(vars[v]);
/* if the objective coefficient of the variable is 0 and it may be rounded both
* up and down, then fix it to the closest feasible value to 0 */
if( SCIPisZero(scip, obj) && SCIPvarMayRoundDown(vars[v]) && SCIPvarMayRoundUp(vars[v]) )
{
bound = SCIPvarGetLbGlobal(vars[v]);
if( SCIPisLT(scip, bound, 0.0) )
{
if( SCIPisLE(scip, 0.0, SCIPvarGetUbGlobal(vars[v])) )
bound = 0.0;
else
{
/* try to take an integer value, only for polishing */
roundbound = SCIPfloor(scip, SCIPvarGetUbGlobal(vars[v]));
if( roundbound < bound )
bound = SCIPvarGetUbGlobal(vars[v]);
else
bound = roundbound;
}
}
else
{
/* try to take an integer value, only for polishing */
roundbound = SCIPceil(scip, bound);
if( roundbound < SCIPvarGetUbGlobal(vars[v]) )
bound = roundbound;
}
SCIPdebugMessage("variable <%s> with objective 0 fixed to %g\n",
SCIPvarGetName(vars[v]), bound);
}
else
{
/* if it is always possible to round variable in direction of objective value,
* fix it to its proper bound
*/
if( SCIPvarMayRoundDown(vars[v]) && !SCIPisNegative(scip, obj) )
{
bound = SCIPvarGetLbGlobal(vars[v]);
if( SCIPisZero(scip, obj) && SCIPvarGetNLocksUp(vars[v]) == 1 && SCIPisInfinity(scip, -bound) )
{
/* variable can be set to -infinity, and it is only contained in one constraint:
* we hope that the corresponding constraint handler is clever enough to set/aggregate the variable
* to something more useful than -infinity and do nothing here
*/
continue;
}
SCIPdebugMessage("variable <%s> with objective %g and %d uplocks fixed to lower bound %g\n",
SCIPvarGetName(vars[v]), SCIPvarGetObj(vars[v]), SCIPvarGetNLocksUp(vars[v]), bound);
}
else if( SCIPvarMayRoundUp(vars[v]) && !SCIPisPositive(scip, obj) )
{
bound = SCIPvarGetUbGlobal(vars[v]);
if( SCIPisZero(scip, obj) && SCIPvarGetNLocksDown(vars[v]) == 1 && SCIPisInfinity(scip, bound) )
{
/* variable can be set to +infinity, and it is only contained in one constraint:
* we hope that the corresponding constraint handler is clever enough to set/aggregate the variable
* to something more useful than +infinity and do nothing here
*/
continue;
}
SCIPdebugMessage("variable <%s> with objective %g and %d downlocks fixed to upper bound %g\n",
SCIPvarGetName(vars[v]), SCIPvarGetObj(vars[v]), SCIPvarGetNLocksDown(vars[v]), bound);
}
else
continue;
}
/* apply the fixing */
if( SCIPisInfinity(scip, REALABS(bound)) && !SCIPisZero(scip, obj) )
{
SCIPdebugMessage(" -> unbounded fixing\n");
SCIPverbMessage(scip, SCIP_VERBLEVEL_NORMAL, NULL,
"problem infeasible or unbounded: variable <%s> with objective %.15g can be made infinitely %s\n",
SCIPvarGetName(vars[v]), SCIPvarGetObj(vars[v]), bound < 0.0 ? "small" : "large");
*result = SCIP_UNBOUNDED;
return SCIP_OKAY;
}
SCIP_CALL( SCIPfixVar(scip, vars[v], bound, &infeasible, &fixed) );
if( infeasible )
{
SCIPdebugMessage(" -> infeasible fixing\n");
*result = SCIP_CUTOFF;
return SCIP_OKAY;
}
assert(fixed);
(*nfixedvars)++;
*result = SCIP_SUCCESS;
}
return SCIP_OKAY;
}
/** calculate the branching score of a variable, depending on the chosen score parameter */
static
SCIP_RETCODE calcBranchScore(
SCIP* scip, /**< current SCIP */
SCIP_HEURDATA* heurdata, /**< branch rule data */
SCIP_VAR* var, /**< candidate variable */
SCIP_Real lpsolval, /**< current fractional LP-relaxation solution value */
SCIP_Real* upscore, /**< pointer to store the variable score when branching on it in upward direction */
SCIP_Real* downscore, /**< pointer to store the variable score when branching on it in downward direction */
char scoreparam /**< the score parameter of this heuristic */
)
{
SCIP_COL* varcol;
SCIP_ROW** colrows;
SCIP_Real* rowvals;
SCIP_Real varlb;
SCIP_Real varub;
SCIP_Real squaredbounddiff; /* current squared difference of variable bounds (ub - lb)^2 */
SCIP_Real newub; /* new upper bound if branching downwards */
SCIP_Real newlb; /* new lower bound if branching upwards */
SCIP_Real squaredbounddiffup; /* squared difference after branching upwards (ub - lb')^2 */
SCIP_Real squaredbounddiffdown; /* squared difference after branching downwards (ub' - lb)^2 */
SCIP_Real currentmean; /* current mean value of variable uniform distribution */
SCIP_Real meanup; /* mean value of variable uniform distribution after branching up */
SCIP_Real meandown; /* mean value of variable uniform distribution after branching down*/
SCIP_VARTYPE vartype;
int ncolrows;
int i;
SCIP_Bool onlyactiverows; /* should only rows which are active at the current node be considered? */
assert(scip != NULL);
assert(var != NULL);
assert(upscore != NULL);
assert(downscore != NULL);
assert(!SCIPisIntegral(scip, lpsolval) || SCIPvarIsBinary(var));
assert(SCIPvarGetStatus(var) == SCIP_VARSTATUS_COLUMN);
varcol = SCIPvarGetCol(var);
assert(varcol != NULL);
colrows = SCIPcolGetRows(varcol);
rowvals = SCIPcolGetVals(varcol);
ncolrows = SCIPcolGetNNonz(varcol);
varlb = SCIPvarGetLbLocal(var);
varub = SCIPvarGetUbLocal(var);
assert(SCIPisFeasLT(scip, varlb, varub));
vartype = SCIPvarGetType(var);
/* calculate mean and variance of variable uniform distribution before and after branching */
currentmean = 0.0;
squaredbounddiff = 0.0;
SCIPvarCalcDistributionParameters(scip, varlb, varub, vartype, ¤tmean, &squaredbounddiff);
/* unfixed binary variables may have an integer solution value in the LP solution, eg, at the presence of indicator constraints */
if( !SCIPvarIsBinary(var) )
{
newlb = SCIPfeasCeil(scip, lpsolval);
newub = SCIPfeasFloor(scip, lpsolval);
}
else
{
newlb = 1.0;
newub = 0.0;
}
/* calculate the variable's uniform distribution after branching up and down, respectively. */
squaredbounddiffup = 0.0;
meanup = 0.0;
SCIPvarCalcDistributionParameters(scip, newlb, varub, vartype, &meanup, &squaredbounddiffup);
/* calculate the distribution mean and variance for a variable with finite lower bound */
squaredbounddiffdown = 0.0;
meandown = 0.0;
SCIPvarCalcDistributionParameters(scip, varlb, newub, vartype, &meandown, &squaredbounddiffdown);
/* initialize the variable's up and down score */
*upscore = 0.0;
*downscore = 0.0;
onlyactiverows = FALSE;
/* loop over the variable rows and calculate the up and down score */
for( i = 0; i < ncolrows; ++i )
{
SCIP_ROW* row;
SCIP_Real changedrowmean;
SCIP_Real rowmean;
SCIP_Real rowvariance;
SCIP_Real changedrowvariance;
SCIP_Real currentrowprob;
SCIP_Real newrowprobup;
SCIP_Real newrowprobdown;
SCIP_Real squaredcoeff;
SCIP_Real rowval;
int rowinfinitiesdown;
int rowinfinitiesup;
int rowpos;
row = colrows[i];
rowval = rowvals[i];
assert(row != NULL);
/* we access the rows by their index */
rowpos = SCIProwGetIndex(row);
/* skip non-active rows if the user parameter was set this way */
if( onlyactiverows && SCIPisSumPositive(scip, SCIPgetRowLPFeasibility(scip, row)) )
continue;
/* call method to ensure sufficient data capacity */
SCIP_CALL( heurdataEnsureArraySize(scip, heurdata, rowpos) );
/* calculate row activity distribution if this is the first candidate to appear in this row */
if( heurdata->rowmeans[rowpos] == SCIP_INVALID ) /*lint !e777 doesn't like comparing floats for equality */
{
rowCalculateGauss(scip, heurdata, row, &heurdata->rowmeans[rowpos], &heurdata->rowvariances[rowpos],
&heurdata->rowinfinitiesdown[rowpos], &heurdata->rowinfinitiesup[rowpos]);
}
/* retrieve the row distribution parameters from the branch rule data */
rowmean = heurdata->rowmeans[rowpos];
rowvariance = heurdata->rowvariances[rowpos];
rowinfinitiesdown = heurdata->rowinfinitiesdown[rowpos];
rowinfinitiesup = heurdata->rowinfinitiesup[rowpos];
assert(!SCIPisNegative(scip, rowvariance));
currentrowprob = SCIProwCalcProbability(scip, row, rowmean, rowvariance,
rowinfinitiesdown, rowinfinitiesup);
/* get variable's current expected contribution to row activity */
squaredcoeff = SQUARED(rowval);
/* first, get the probability change for the row if the variable is branched on upwards. The probability
* can only be affected if the variable upper bound is finite
*/
if( !SCIPisInfinity(scip, varub) )
{
int rowinftiesdownafterbranch;
int rowinftiesupafterbranch;
/* calculate how branching would affect the row parameters */
changedrowmean = rowmean + rowval * (meanup - currentmean);
changedrowvariance = rowvariance + squaredcoeff * (squaredbounddiffup - squaredbounddiff);
changedrowvariance = MAX(0.0, changedrowvariance);
rowinftiesdownafterbranch = rowinfinitiesdown;
rowinftiesupafterbranch = rowinfinitiesup;
/* account for changes of the row's infinite bound contributions */
if( SCIPisInfinity(scip, -varlb) && rowval < 0.0 )
rowinftiesupafterbranch--;
if( SCIPisInfinity(scip, -varlb) && rowval > 0.0 )
rowinftiesdownafterbranch--;
assert(rowinftiesupafterbranch >= 0);
assert(rowinftiesdownafterbranch >= 0);
newrowprobup = SCIProwCalcProbability(scip, row, changedrowmean, changedrowvariance, rowinftiesdownafterbranch,
rowinftiesupafterbranch);
}
else
newrowprobup = currentrowprob;
/* do the same for the other branching direction */
if( !SCIPisInfinity(scip, varlb) )
{
int rowinftiesdownafterbranch;
int rowinftiesupafterbranch;
changedrowmean = rowmean + rowval * (meandown - currentmean);
changedrowvariance = rowvariance + squaredcoeff * (squaredbounddiffdown - squaredbounddiff);
changedrowvariance = MAX(0.0, changedrowvariance);
rowinftiesdownafterbranch = rowinfinitiesdown;
rowinftiesupafterbranch = rowinfinitiesup;
/* account for changes of the row's infinite bound contributions */
if( SCIPisInfinity(scip, varub) && rowval > 0.0 )
rowinftiesupafterbranch -= 1;
if( SCIPisInfinity(scip, varub) && rowval < 0.0 )
rowinftiesdownafterbranch -= 1;
assert(rowinftiesdownafterbranch >= 0);
assert(rowinftiesupafterbranch >= 0);
newrowprobdown = SCIProwCalcProbability(scip, row, changedrowmean, changedrowvariance, rowinftiesdownafterbranch,
rowinftiesupafterbranch);
}
else
newrowprobdown = currentrowprob;
/* update the up and down score depending on the chosen scoring parameter */
SCIP_CALL( SCIPupdateDistributionScore(scip, currentrowprob, newrowprobup, newrowprobdown, upscore, downscore, scoreparam) );
SCIPdebugMessage(" Variable %s changes probability of row %s from %g to %g (branch up) or %g;\n",
SCIPvarGetName(var), SCIProwGetName(row), currentrowprob, newrowprobup, newrowprobdown);
SCIPdebugMessage(" --> new variable score: %g (for branching up), %g (for branching down)\n",
*upscore, *downscore);
}
return SCIP_OKAY;
}
/** computes a disjunctive cut inequality based on two simplex taubleau rows */
static
SCIP_RETCODE generateDisjCutSOS1(
SCIP* scip, /**< SCIP pointer */
SCIP_SEPA* sepa, /**< separator */
SCIP_ROW** rows, /**< LP rows */
int nrows, /**< number of LP rows */
SCIP_COL** cols, /**< LP columns */
int ncols, /**< number of LP columns */
int ndisjcuts, /**< number of disjunctive cuts found so far */
SCIP_Bool scale, /**< should cut be scaled */
SCIP_Bool strengthen, /**< should cut be strengthened if integer variables are present */
SCIP_Real cutlhs1, /**< left hand side of the first simplex row */
SCIP_Real cutlhs2, /**< left hand side of the second simplex row */
SCIP_Real bound1, /**< bound of first simplex row */
SCIP_Real bound2, /**< bound of second simplex row */
SCIP_Real* simplexcoefs1, /**< simplex coefficients of first row */
SCIP_Real* simplexcoefs2, /**< simplex coefficients of second row */
SCIP_Real* cutcoefs, /**< pointer to store cut coefficients (length: nscipvars) */
SCIP_ROW** row, /**< pointer to store disjunctive cut inequality */
SCIP_Bool* madeintegral /**< pointer to store whether cut has been scaled to integral values */
)
{
char cutname[SCIP_MAXSTRLEN];
SCIP_COL** rowcols;
SCIP_COL* col;
SCIP_Real* rowvals;
SCIP_Real lhsrow;
SCIP_Real rhsrow;
SCIP_Real cutlhs;
SCIP_Real sgn;
SCIP_Real lb;
SCIP_Real ub;
int nonbasicnumber = 0;
int rownnonz;
int ind;
int r;
int c;
assert( scip != NULL );
assert( row != NULL );
assert( rows != NULL );
assert( cols != NULL );
assert( simplexcoefs1 != NULL );
assert( simplexcoefs2 != NULL );
assert( cutcoefs != NULL );
assert( sepa != NULL );
assert( madeintegral != NULL );
*madeintegral = FALSE;
/* check signs */
if ( SCIPisFeasPositive(scip, cutlhs1) == SCIPisFeasPositive(scip, cutlhs2) )
sgn = 1.0;
else
sgn = -1.0;
/* check bounds */
if ( SCIPisInfinity(scip, REALABS(bound1)) || SCIPisInfinity(scip, REALABS(bound2)) )
strengthen = FALSE;
/* compute left hand side of row (a later update is possible, see below) */
cutlhs = sgn * cutlhs1 * cutlhs2;
/* add cut-coefficients of the non-basic non-slack variables */
for (c = 0; c < ncols; ++c)
{
col = cols[c];
assert( col != NULL );
ind = SCIPcolGetLPPos(col);
assert( ind >= 0 );
if ( SCIPcolGetBasisStatus(col) == SCIP_BASESTAT_LOWER )
{
lb = SCIPcolGetLb(col);
/* for integer variables we may obtain stronger coefficients */
if ( strengthen && SCIPcolIsIntegral(col) )
{
SCIP_Real mval;
SCIP_Real mvalfloor;
SCIP_Real mvalceil;
mval = (cutlhs2 * simplexcoefs1[nonbasicnumber] - cutlhs1 * simplexcoefs2[nonbasicnumber]) / (cutlhs2 * bound1 + cutlhs1 * bound2);
mvalfloor = SCIPfloor(scip, mval);
mvalceil = SCIPceil(scip, mval);
cutcoefs[ind] = MIN(sgn * cutlhs2 * (simplexcoefs1[nonbasicnumber] - mvalfloor * bound1), sgn * cutlhs1 * (simplexcoefs2[nonbasicnumber] + mvalceil * bound2));
assert( SCIPisFeasLE(scip, cutcoefs[ind], MAX(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber])) );
}
else
cutcoefs[ind] = MAX(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber]);
cutlhs += cutcoefs[ind] * lb;
++nonbasicnumber;
}
else if ( SCIPcolGetBasisStatus(col) == SCIP_BASESTAT_UPPER )
{
ub = SCIPcolGetUb(col);
/* for integer variables we may obtain stronger coefficients */
if ( strengthen && SCIPcolIsIntegral(col) )
{
SCIP_Real mval;
SCIP_Real mvalfloor;
SCIP_Real mvalceil;
mval = (cutlhs2 * simplexcoefs1[nonbasicnumber] - cutlhs1 * simplexcoefs2[nonbasicnumber]) / (cutlhs2 * bound1 + cutlhs1 * bound2);
mvalfloor = SCIPfloor(scip, -mval);
mvalceil = SCIPceil(scip, -mval);
cutcoefs[ind] = MAX(sgn * cutlhs2 * (simplexcoefs1[nonbasicnumber] + mvalfloor * bound1), sgn * cutlhs1 * (simplexcoefs2[nonbasicnumber] - mvalceil * bound2));
assert( SCIPisFeasLE(scip, -cutcoefs[ind], -MIN(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber])) );
}
else
cutcoefs[ind] = MIN(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber]);
cutlhs += cutcoefs[ind] * ub;
++nonbasicnumber;
}
else
{
assert( SCIPcolGetBasisStatus(col) != SCIP_BASESTAT_ZERO );
cutcoefs[ind] = 0.0;
}
}
/* add cut-coefficients of the non-basic slack variables */
for (r = 0; r < nrows; ++r)
{
rhsrow = SCIProwGetRhs(rows[r]) - SCIProwGetConstant(rows[r]);
lhsrow = SCIProwGetLhs(rows[r]) - SCIProwGetConstant(rows[r]);
assert( SCIProwGetBasisStatus(rows[r]) != SCIP_BASESTAT_ZERO );
assert( SCIPisFeasZero(scip, lhsrow - rhsrow) || SCIPisNegative(scip, lhsrow - rhsrow) );
assert( SCIProwIsInLP(rows[r]) );
if ( SCIProwGetBasisStatus(rows[r]) != SCIP_BASESTAT_BASIC )
{
SCIP_Real cutcoef;
if ( SCIProwGetBasisStatus(rows[r]) == SCIP_BASESTAT_UPPER )
{
assert( SCIPisFeasZero(scip, SCIPgetRowLPActivity(scip, rows[r]) - SCIProwGetRhs(rows[r])) );
cutcoef = MAX(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber]);
cutlhs -= cutcoef * rhsrow;
++nonbasicnumber;
}
else /* SCIProwGetBasisStatus(rows[r]) == SCIP_BASESTAT_LOWER */
{
assert( SCIProwGetBasisStatus(rows[r]) == SCIP_BASESTAT_LOWER );
assert( SCIPisFeasZero(scip, SCIPgetRowLPActivity(scip, rows[r]) - SCIProwGetLhs(rows[r])) );
cutcoef = MIN(sgn * cutlhs2 * simplexcoefs1[nonbasicnumber], sgn * cutlhs1 * simplexcoefs2[nonbasicnumber]);
cutlhs -= cutcoef * lhsrow;
++nonbasicnumber;
}
rownnonz = SCIProwGetNNonz(rows[r]);
rowvals = SCIProwGetVals(rows[r]);
rowcols = SCIProwGetCols(rows[r]);
for (c = 0; c < rownnonz; ++c)
{
ind = SCIPcolGetLPPos(rowcols[c]);
/* if column is not in LP, then return without generating cut */
if ( ind < 0 )
{
*row = NULL;
return SCIP_OKAY;
}
cutcoefs[ind] -= cutcoef * rowvals[c];
}
}
}
/* create cut */
(void) SCIPsnprintf(cutname, SCIP_MAXSTRLEN, "%s_%d_%d", SCIPsepaGetName(sepa), SCIPgetNLPs(scip), ndisjcuts);
if ( SCIPgetDepth(scip) == 0 )
SCIP_CALL( SCIPcreateEmptyRowSepa(scip, row, sepa, cutname, cutlhs, SCIPinfinity(scip), FALSE, FALSE, TRUE) );
else
SCIP_CALL( SCIPcreateEmptyRowSepa(scip, row, sepa, cutname, cutlhs, SCIPinfinity(scip), TRUE, FALSE, TRUE) );
SCIP_CALL( SCIPcacheRowExtensions(scip, *row) );
for (c = 0; c < ncols; ++c)
{
ind = SCIPcolGetLPPos(cols[c]);
assert( ind >= 0 );
if ( ! SCIPisFeasZero(scip, cutcoefs[ind]) )
{
SCIP_CALL( SCIPaddVarToRow(scip, *row, SCIPcolGetVar(cols[c]), cutcoefs[ind] ) );
}
}
SCIP_CALL( SCIPflushRowExtensions(scip, *row) );
/* try to scale the cut to integral values
* @todo find better but still stable disjunctive cut settings
*/
if ( scale )
{
int maxdepth;
int depth;
SCIP_Longint maxdnom;
SCIP_Real maxscale;
depth = SCIPgetDepth(scip);
assert( depth >= 0 );
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;
}
SCIP_CALL( SCIPmakeRowIntegral(scip, *row, -SCIPepsilon(scip), SCIPsumepsilon(scip), maxdnom, maxscale, TRUE, madeintegral) );
}
return SCIP_OKAY;
}
/** returns a variable, that pushes activity of the row in the given direction with minimal negative impact on other rows;
* if variables have equal impact, chooses the one with best objective value improvement in corresponding direction;
* prefer fractional integers over other variables in order to become integral during the process;
* shifting in a direction is forbidden, if this forces the objective value over the upper bound, or if the variable
* was already shifted in the opposite direction
*/
static
SCIP_RETCODE selectShifting(
SCIP* scip, /**< SCIP data structure */
SCIP_SOL* sol, /**< primal solution */
SCIP_ROW* row, /**< LP row */
SCIP_Real rowactivity, /**< activity of LP row */
int direction, /**< should the activity be increased (+1) or decreased (-1)? */
SCIP_Real* nincreases, /**< array with weighted number of increasings per variables */
SCIP_Real* ndecreases, /**< array with weighted number of decreasings per variables */
SCIP_Real increaseweight, /**< current weight of increase/decrease updates */
SCIP_VAR** shiftvar, /**< pointer to store the shifting variable, returns NULL if impossible */
SCIP_Real* oldsolval, /**< pointer to store old solution value of shifting variable */
SCIP_Real* newsolval /**< pointer to store new (shifted) solution value of shifting variable */
)
{
SCIP_COL** rowcols;
SCIP_Real* rowvals;
int nrowcols;
SCIP_Real activitydelta;
SCIP_Real bestshiftscore;
SCIP_Real bestdeltaobj;
int c;
assert(direction == +1 || direction == -1);
assert(nincreases != NULL);
assert(ndecreases != NULL);
assert(shiftvar != NULL);
assert(oldsolval != NULL);
assert(newsolval != NULL);
/* get row entries */
rowcols = SCIProwGetCols(row);
rowvals = SCIProwGetVals(row);
nrowcols = SCIProwGetNLPNonz(row);
/* calculate how much the activity must be shifted in order to become feasible */
activitydelta = (direction == +1 ? SCIProwGetLhs(row) - rowactivity : SCIProwGetRhs(row) - rowactivity);
assert((direction == +1 && SCIPisPositive(scip, activitydelta))
|| (direction == -1 && SCIPisNegative(scip, activitydelta)));
/* select shifting variable */
bestshiftscore = SCIP_REAL_MAX;
bestdeltaobj = SCIPinfinity(scip);
*shiftvar = NULL;
*newsolval = 0.0;
*oldsolval = 0.0;
for( c = 0; c < nrowcols; ++c )
{
SCIP_COL* col;
SCIP_VAR* var;
SCIP_Real val;
SCIP_Real solval;
SCIP_Real shiftval;
SCIP_Real shiftscore;
SCIP_Bool isinteger;
SCIP_Bool isfrac;
SCIP_Bool increase;
col = rowcols[c];
var = SCIPcolGetVar(col);
val = rowvals[c];
assert(!SCIPisZero(scip, val));
solval = SCIPgetSolVal(scip, sol, var);
isinteger = (SCIPvarGetType(var) == SCIP_VARTYPE_BINARY || SCIPvarGetType(var) == SCIP_VARTYPE_INTEGER);
isfrac = isinteger && !SCIPisFeasIntegral(scip, solval);
increase = (direction * val > 0.0);
/* calculate the score of the shifting (prefer smaller values) */
if( isfrac )
shiftscore = increase ? -1.0 / (SCIPvarGetNLocksUp(var) + 1.0) :
-1.0 / (SCIPvarGetNLocksDown(var) + 1.0);
else
{
int probindex;
probindex = SCIPvarGetProbindex(var);
if( increase )
shiftscore = ndecreases[probindex]/increaseweight;
else
shiftscore = nincreases[probindex]/increaseweight;
if( isinteger )
shiftscore += 1.0;
}
if( shiftscore <= bestshiftscore )
{
SCIP_Real deltaobj;
if( !increase )
{
/* shifting down */
assert(direction * val < 0.0);
if( isfrac )
shiftval = SCIPfeasFloor(scip, solval);
else
{
SCIP_Real lb;
assert(activitydelta/val < 0.0);
shiftval = solval + activitydelta/val;
assert(shiftval <= solval); /* may be equal due to numerical digit erasement in the subtraction */
if( SCIPvarIsIntegral(var) )
shiftval = SCIPfeasFloor(scip, shiftval);
lb = SCIPvarGetLbGlobal(var);
shiftval = MAX(shiftval, lb);
}
}
else
{
/* shifting up */
assert(direction * val > 0.0);
if( isfrac )
shiftval = SCIPfeasCeil(scip, solval);
else
{
SCIP_Real ub;
assert(activitydelta/val > 0.0);
shiftval = solval + activitydelta/val;
assert(shiftval >= solval); /* may be equal due to numerical digit erasement in the subtraction */
if( SCIPvarIsIntegral(var) )
shiftval = SCIPfeasCeil(scip, shiftval);
ub = SCIPvarGetUbGlobal(var);
shiftval = MIN(shiftval, ub);
}
}
if( SCIPisEQ(scip, shiftval, solval) )
continue;
deltaobj = SCIPvarGetObj(var) * (shiftval - solval);
if( shiftscore < bestshiftscore || deltaobj < bestdeltaobj )
{
bestshiftscore = shiftscore;
bestdeltaobj = deltaobj;
*shiftvar = var;
*oldsolval = solval;
*newsolval = shiftval;
}
}
}
return SCIP_OKAY;
}