/** depending on the LP's solution status, calls reduced cost or Farkas pricing method of variable pricer */
SCIP_RETCODE SCIPpricerExec(
SCIP_PRICER* pricer, /**< variable pricer */
SCIP_SET* set, /**< global SCIP settings */
SCIP_PROB* prob, /**< transformed problem */
SCIP_LP* lp, /**< LP data */
SCIP_PRICESTORE* pricestore, /**< pricing storage */
SCIP_Real* lowerbound, /**< local lower bound computed by the pricer */
SCIP_RESULT* result /**< result of the pricing process */
)
{
assert(pricer != NULL);
assert(lowerbound != NULL);
assert(result != NULL);
/* set lowerbound and result pointer */
*lowerbound = - SCIPsetInfinity(set);
*result = SCIP_SUCCESS;
/* check if pricer should be delayed */
if( pricer->delay && SCIPpricestoreGetNVars(pricestore) > 0 )
return SCIP_OKAY;
if( SCIPlpGetSolstat(lp) == SCIP_LPSOLSTAT_INFEASIBLE )
{
SCIP_CALL( SCIPpricerFarkas(pricer, set, prob) );
}
else
{
*result = SCIP_DIDNOTRUN;
SCIP_CALL( SCIPpricerRedcost(pricer, set, prob, lowerbound, result) );
}
return SCIP_OKAY;
}
/** adds problem variables with negative reduced costs to pricing storage */
SCIP_RETCODE SCIPpricestoreAddProbVars(
SCIP_PRICESTORE* pricestore, /**< pricing storage */
BMS_BLKMEM* blkmem, /**< block memory buffers */
SCIP_SET* set, /**< global SCIP settings */
SCIP_STAT* stat, /**< dynamic problem statistics */
SCIP_PROB* prob, /**< transformed problem after presolve */
SCIP_TREE* tree, /**< branch and bound tree */
SCIP_LP* lp, /**< LP data */
SCIP_BRANCHCAND* branchcand, /**< branching candidate storage */
SCIP_EVENTQUEUE* eventqueue /**< event queue */
)
{
SCIP_VAR* var;
SCIP_COL* col;
SCIP_Bool root;
SCIP_Bool added;
int v;
int abortpricevars;
int maxpricevars;
int nfoundvars;
assert(pricestore != NULL);
assert(set != NULL);
assert(stat != NULL);
assert(prob != NULL);
assert(lp != NULL);
assert(lp->solved);
assert(tree != NULL);
assert(SCIPtreeHasCurrentNodeLP(tree));
assert(prob->nvars >= SCIPlpGetNCols(lp));
/* if all problem variables of status COLUMN are already in the LP, nothing has to be done */
if( prob->ncolvars == SCIPlpGetNCols(lp) )
return SCIP_OKAY;
root = (SCIPtreeGetCurrentDepth(tree) == 0);
maxpricevars = SCIPsetGetPriceMaxvars(set, root);
assert(maxpricevars >= 1);
abortpricevars = (int)(set->price_abortfac * maxpricevars);
assert(abortpricevars >= maxpricevars);
/**@todo test pricing: is abortpricevars a good idea? -> like strong branching, lookahead, ... */
pricestore->nprobpricings++;
/* start timing */
SCIPclockStart(pricestore->probpricingtime, set);
/* price already existing problem variables */
nfoundvars = 0;
for( v = 0; v < prob->nvars && nfoundvars < abortpricevars; ++v )
{
var = prob->vars[v];
if( SCIPvarGetStatus(var) == SCIP_VARSTATUS_COLUMN )
{
col = SCIPvarGetCol(var);
assert(col != NULL);
assert(col->var == var);
assert(col->len >= 0);
assert(col->lppos >= -1);
assert(col->lpipos >= -1);
assert(SCIPcolIsInLP(col) == (col->lpipos >= 0));
if( !SCIPcolIsInLP(col) )
{
SCIPdebugMessage("price column variable <%s> in bounds [%g,%g]\n",
SCIPvarGetName(var), SCIPvarGetLbLocal(var), SCIPvarGetUbLocal(var));
/* add variable to pricing storage, if zero is not best bound w.r.t. objective function */
SCIP_CALL( addBoundViolated(pricestore, blkmem, set, stat, tree, lp, branchcand, eventqueue, var, &added) );
if( added )
{
pricestore->nprobvarsfound++;
nfoundvars++;
}
else if( SCIPcolGetNNonz(col) > 0 )
{
SCIP_Real feasibility;
/* a column not in LP that doesn't have zero in its bounds was added by bound checking above */
assert(!SCIPsetIsPositive(set, SCIPvarGetLbLocal(col->var)));
assert(!SCIPsetIsNegative(set, SCIPvarGetUbLocal(col->var)));
if( SCIPlpGetSolstat(lp) == SCIP_LPSOLSTAT_INFEASIBLE )
{
/* The LP was proven infeasible, so we have an infeasibility proof by the dual Farkas multipliers y.
* The valid inequality y^T A x >= y^T b is violated by all x, especially by the (for this
* inequality most feasible solution) x' defined by
* x'_i = ub_i, if y^T A_i > 0
* x'_i = lb_i, if y^T A_i <= 0.
* Pricing in this case means to add variables i with positive Farkas value, i.e. y^T A_i x'_i > 0
*/
feasibility = -SCIPcolGetFarkasValue(col, stat, lp);
SCIPdebugMessage(" <%s> Farkas feasibility: %e\n", SCIPvarGetName(col->var), feasibility);
}
else
{
/* The dual LP is feasible, and we have a feasible dual solution. Pricing in this case means to
* add variables with negative feasibility, that is
* - positive reduced costs for variables with negative lower bound
* - negative reduced costs for variables with positive upper bound
*/
feasibility = SCIPcolGetFeasibility(col, set, stat, lp);
SCIPdebugMessage(" <%s> reduced cost feasibility: %e\n", SCIPvarGetName(col->var), feasibility);
}
/* the score is -feasibility / (#nonzeros in column + 1) to prefer short columns
* we must add variables with negative feasibility, but in order to not get a too large lower bound
* due to missing columns, we better also add variables, that have a very small feasibility
*/
if( !SCIPsetIsPositive(set, feasibility) )
{
SCIP_CALL( SCIPpricestoreAddVar(pricestore, blkmem, set, eventqueue, lp, var, -feasibility / (col->len+1), root) );
pricestore->nprobvarsfound++;
nfoundvars++;
}
}
}
}
}
/* stop timing */
SCIPclockStop(pricestore->probpricingtime, set);
return SCIP_OKAY;
}
