/*
First tries setting a variable to better value. If feasible then
tries setting others. If not feasible then tries swaps
Returns 1 if solution, 0 if not
The main body of this routine implements an O((q^2)/2) brute force search
around the current solution, for q = number of integer variables. Call this
the inc/dec heuristic. For each integer variable x<i>, first decrement the
value. Then, for integer variables x<i+1>, ..., x<q-1>, try increment and
decrement. If one of these permutations produces a better solution,
remember it. Then repeat, with x<i> incremented. If we find a better
solution, update our notion of current solution and continue.
The net effect is a greedy walk: As each improving pair is found, the
current solution is updated and the search continues from this updated
solution.
Way down at the end, we call solutionFix, which will create a drastically
restricted problem based on variables marked as used, then do mini-BaC on
the restricted problem. This can occur even if we don't try the inc/dec
heuristic. This would be more obvious if the inc/dec heuristic were broken
out as a separate routine and solutionFix had a name that reflected where
it was headed.
The return code of 0 is grossly overloaded, because it maps to a return
code of 0 from solutionFix, which is itself grossly overloaded. See
comments in solutionFix and in CbcHeuristic::smallBranchAndBound.
*/
int
CbcHeuristicLocal::solution(double & solutionValue,
double * betterSolution)
{
/*
Execute only if a new solution has been discovered since the last time we
were called.
*/
numCouldRun_++;
// See if frequency kills off idea
int swap = swap_%100;
int skip = swap_/100;
int nodeCount = model_->getNodeCount();
if (nodeCount<lastRunDeep_+skip && nodeCount != lastRunDeep_+1)
return 0;
if (numberSolutions_ == model_->getSolutionCount() &&
(numberSolutions_ == howOftenShallow_ ||
nodeCount < lastRunDeep_+2*skip))
return 0;
howOftenShallow_ = numberSolutions_;
numberSolutions_ = model_->getSolutionCount();
if (nodeCount<lastRunDeep_+skip )
return 0;
lastRunDeep_ = nodeCount;
howOftenShallow_ = numberSolutions_;
if ((swap%10) == 2) {
// try merge
return solutionFix( solutionValue, betterSolution, NULL);
}
/*
Exclude long (column), thin (row) systems.
Given the n^2 nature of the search, more than 100,000 columns could get
expensive. But I don't yet see the rationale for the second part of the
condition (cols > 10*rows). And cost is proportional to number of integer
variables --- shouldn't we use that?
Why wait until we have more than one solution?
*/
if ((model_->getNumCols() > 100000 && model_->getNumCols() >
10*model_->getNumRows()) || numberSolutions_ <= 1)
return 0; // probably not worth it
// worth trying
OsiSolverInterface * solver = model_->solver();
const double * rowLower = solver->getRowLower();
const double * rowUpper = solver->getRowUpper();
const double * solution = model_->bestSolution();
/*
Shouldn't this test be redundant if we've already checked that
numberSolutions_ > 1? Stronger: shouldn't this be an assertion?
*/
if (!solution)
return 0; // No solution found yet
const double * objective = solver->getObjCoefficients();
double primalTolerance;
solver->getDblParam(OsiPrimalTolerance, primalTolerance);
int numberRows = matrix_.getNumRows();
int numberIntegers = model_->numberIntegers();
const int * integerVariable = model_->integerVariable();
int i;
double direction = solver->getObjSense();
double newSolutionValue = model_->getObjValue() * direction;
int returnCode = 0;
numRuns_++;
// Column copy
const double * element = matrix_.getElements();
const int * row = matrix_.getIndices();
const CoinBigIndex * columnStart = matrix_.getVectorStarts();
const int * columnLength = matrix_.getVectorLengths();
// Get solution array for heuristic solution
int numberColumns = solver->getNumCols();
double * newSolution = new double [numberColumns];
memcpy(newSolution, solution, numberColumns*sizeof(double));
#ifdef LOCAL_FIX_CONTINUOUS
// mark continuous used
const double * columnLower = solver->getColLower();
for (int iColumn = 0; iColumn < numberColumns; iColumn++) {
if (!solver->isInteger(iColumn)) {
if (solution[iColumn] > columnLower[iColumn] + 1.0e-8)
used_[iColumn] = numberSolutions_;
}
}
#endif
// way is 1 if down possible, 2 if up possible, 3 if both possible
char * way = new char[numberIntegers];
// corrected costs
double * cost = new double[numberIntegers];
// for array to mark infeasible rows after iColumn branch
char * mark = new char[numberRows];
memset(mark, 0, numberRows);
// space to save values so we don't introduce rounding errors
double * save = new double[numberRows];
/*
Force variables within their original bounds, then to the nearest integer.
Overall, we seem to be prepared to cope with noninteger bounds. Is this
necessary? Seems like we'd be better off to force the bounds to integrality
as part of preprocessing. More generally, why do we need to do this? This
solution should have been cleaned and checked when it was accepted as a
solution!
Once the value is set, decide whether we can move up or down.
The only place that used_ is used is in solutionFix; if a variable is not
flagged as used, it will be fixed (at lower bound). Why the asymmetric
treatment? This makes some sense for binary variables (for which there are
only two options). But for general integer variables, why not make a similar
test against the original upper bound?
*/
// clean solution
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double value = newSolution[iColumn];
if (value < originalLower) {
value = originalLower;
newSolution[iColumn] = value;
} else if (value > originalUpper) {
value = originalUpper;
newSolution[iColumn] = value;
}
double nearest = floor(value + 0.5);
//assert(fabs(value-nearest)<10.0*primalTolerance);
value = nearest;
newSolution[iColumn] = nearest;
// if away from lower bound mark that fact
if (nearest > originalLower) {
used_[iColumn] = numberSolutions_;
}
cost[i] = direction * objective[iColumn];
/*
Given previous computation we're checking that value is at least 1 away
from the original bounds.
*/
int iway = 0;
if (value > originalLower + 0.5)
iway = 1;
if (value < originalUpper - 0.5)
iway |= 2;
way[i] = static_cast<char>(iway);
}
/*
Calculate lhs of each constraint for groomed solution.
*/
// get row activities
double * rowActivity = new double[numberRows];
memset(rowActivity, 0, numberRows*sizeof(double));
for (i = 0; i < numberColumns; i++) {
int j;
double value = newSolution[i];
if (value) {
for (j = columnStart[i];
j < columnStart[i] + columnLength[i]; j++) {
int iRow = row[j];
rowActivity[iRow] += value * element[j];
}
}
}
/*
Check that constraints are satisfied. For small infeasibility, force the
activity within bound. Again, why is this necessary if the current solution
was accepted as a valid solution?
Why are we scanning past the first unacceptable constraint?
*/
// check was feasible - if not adjust (cleaning may move)
// if very infeasible then give up
bool tryHeuristic = true;
for (i = 0; i < numberRows; i++) {
if (rowActivity[i] < rowLower[i]) {
if (rowActivity[i] < rowLower[i] - 10.0*primalTolerance)
tryHeuristic = false;
rowActivity[i] = rowLower[i];
} else if (rowActivity[i] > rowUpper[i]) {
if (rowActivity[i] < rowUpper[i] + 10.0*primalTolerance)
tryHeuristic = false;
rowActivity[i] = rowUpper[i];
}
}
/*
This bit of code is not quite totally redundant: it'll bail at 10,000
instead of 100,000. Potentially we can do a lot of work to get here, only
to abandon it.
*/
// Switch off if may take too long
if (model_->getNumCols() > 10000 && model_->getNumCols() >
10*model_->getNumRows())
tryHeuristic = false;
/*
Try the inc/dec heuristic?
*/
if (tryHeuristic) {
// total change in objective
double totalChange = 0.0;
// local best change in objective
double bestChange = 0.0;
// maybe just do 1000
int maxIntegers = numberIntegers;
if (((swap/10) &1) != 0) {
maxIntegers = CoinMin(1000,numberIntegers);
}
/*
Outer loop to walk integer variables. Call the current variable x<i>. At the
end of this loop, bestChange will contain the best (negative) change in the
objective for any single pair.
The trouble is, we're limited to monotonically increasing improvement.
Suppose we discover an improvement of 10 for some pair. If, later in the
search, we discover an improvement of 9 for some other pair, we will not use
it. That seems wasteful.
*/
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
bestChange = 0.0;
int endInner = CoinMin(numberIntegers,i+maxIntegers);
double objectiveCoefficient = cost[i];
int k;
int j;
int goodK = -1;
int wayK = -1, wayI = -1;
/*
Try decrementing x<i>.
*/
if ((way[i]&1) != 0) {
int numberInfeasible = 0;
/*
Adjust row activities where x<i> has a nonzero coefficient. Save the old
values for restoration. Mark any rows that become infeasible as a result
of the decrement.
*/
// save row activities and adjust
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
save[iRow] = rowActivity[iRow];
rowActivity[iRow] -= element[j];
if (rowActivity[iRow] < rowLower[iRow] - primalTolerance ||
rowActivity[iRow] > rowUpper[iRow] + primalTolerance) {
// mark row
mark[iRow] = 1;
numberInfeasible++;
}
}
/*
Run through the remaining integer variables. Try increment and decrement on
each one. If the potential objective change is better than anything we've
seen so far, do a full evaluation of x<k> in that direction. If we can
repair all infeasibilities introduced by pushing x<i> down, we have a
winner. Remember the best variable, and the direction for x<i> and x<k>.
*/
// try down
for (k = i + 1; k < endInner; k++) {
if ((way[k]&1) != 0) {
// try down
if (-objectiveCoefficient - cost[k] < bestChange) {
// see if feasible down
bool good = true;
int numberMarked = 0;
int kColumn = integerVariable[k];
for (j = columnStart[kColumn];
j < columnStart[kColumn] + columnLength[kColumn]; j++) {
int iRow = row[j];
double newValue = rowActivity[iRow] - element[j];
if (newValue < rowLower[iRow] - primalTolerance ||
newValue > rowUpper[iRow] + primalTolerance) {
good = false;
break;
} else if (mark[iRow]) {
// made feasible
numberMarked++;
}
}
if (good && numberMarked == numberInfeasible) {
// better solution
goodK = k;
wayK = -1;
wayI = -1;
bestChange = -objectiveCoefficient - cost[k];
}
}
}
if ((way[k]&2) != 0) {
// try up
if (-objectiveCoefficient + cost[k] < bestChange) {
// see if feasible up
bool good = true;
int numberMarked = 0;
int kColumn = integerVariable[k];
for (j = columnStart[kColumn];
j < columnStart[kColumn] + columnLength[kColumn]; j++) {
int iRow = row[j];
double newValue = rowActivity[iRow] + element[j];
if (newValue < rowLower[iRow] - primalTolerance ||
newValue > rowUpper[iRow] + primalTolerance) {
good = false;
break;
} else if (mark[iRow]) {
// made feasible
numberMarked++;
}
}
if (good && numberMarked == numberInfeasible) {
// better solution
goodK = k;
wayK = 1;
wayI = -1;
bestChange = -objectiveCoefficient + cost[k];
}
}
}
}
/*
Remove effect of decrementing x<i> by restoring original lhs values.
*/
// restore row activities
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] = save[iRow];
mark[iRow] = 0;
}
}
/*
Try to increment x<i>. Actions as for decrement.
*/
if ((way[i]&2) != 0) {
int numberInfeasible = 0;
// save row activities and adjust
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
save[iRow] = rowActivity[iRow];
rowActivity[iRow] += element[j];
if (rowActivity[iRow] < rowLower[iRow] - primalTolerance ||
rowActivity[iRow] > rowUpper[iRow] + primalTolerance) {
// mark row
mark[iRow] = 1;
numberInfeasible++;
}
}
// try up
for (k = i + 1; k < endInner; k++) {
if ((way[k]&1) != 0) {
// try down
if (objectiveCoefficient - cost[k] < bestChange) {
// see if feasible down
bool good = true;
int numberMarked = 0;
int kColumn = integerVariable[k];
for (j = columnStart[kColumn];
j < columnStart[kColumn] + columnLength[kColumn]; j++) {
int iRow = row[j];
double newValue = rowActivity[iRow] - element[j];
if (newValue < rowLower[iRow] - primalTolerance ||
newValue > rowUpper[iRow] + primalTolerance) {
good = false;
break;
} else if (mark[iRow]) {
// made feasible
numberMarked++;
}
}
if (good && numberMarked == numberInfeasible) {
// better solution
goodK = k;
wayK = -1;
wayI = 1;
bestChange = objectiveCoefficient - cost[k];
}
}
}
if ((way[k]&2) != 0) {
// try up
if (objectiveCoefficient + cost[k] < bestChange) {
// see if feasible up
bool good = true;
int numberMarked = 0;
int kColumn = integerVariable[k];
for (j = columnStart[kColumn];
j < columnStart[kColumn] + columnLength[kColumn]; j++) {
int iRow = row[j];
double newValue = rowActivity[iRow] + element[j];
if (newValue < rowLower[iRow] - primalTolerance ||
newValue > rowUpper[iRow] + primalTolerance) {
good = false;
break;
} else if (mark[iRow]) {
// made feasible
numberMarked++;
}
}
if (good && numberMarked == numberInfeasible) {
// better solution
goodK = k;
wayK = 1;
wayI = 1;
bestChange = objectiveCoefficient + cost[k];
}
}
}
}
// restore row activities
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] = save[iRow];
mark[iRow] = 0;
}
}
/*
We've found a pair x<i> and x<k> which produce a better solution. Update our
notion of current solution to match.
Why does this not update newSolutionValue?
*/
if (goodK >= 0) {
// we found something - update solution
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] += wayI * element[j];
}
newSolution[iColumn] += wayI;
int kColumn = integerVariable[goodK];
for (j = columnStart[kColumn];
j < columnStart[kColumn] + columnLength[kColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] += wayK * element[j];
}
newSolution[kColumn] += wayK;
/*
Adjust motion range for x<k>. We may have banged up against a bound with that
last move.
*/
// See if k can go further ?
const OsiObject * object = model_->object(goodK);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double value = newSolution[kColumn];
int iway = 0;
if (value > originalLower + 0.5)
iway = 1;
if (value < originalUpper - 0.5)
iway |= 2;
way[goodK] = static_cast<char>(iway);
totalChange += bestChange;
}
}
/*
End of loop to try increment/decrement of integer variables.
newSolutionValue does not necessarily match the current newSolution, and
bestChange simply reflects the best single change. Still, that's sufficient
to indicate that there's been at least one change. Check that we really do
have a valid solution.
*/
if (totalChange + newSolutionValue < solutionValue) {
// paranoid check
memset(rowActivity, 0, numberRows*sizeof(double));
for (i = 0; i < numberColumns; i++) {
int j;
double value = newSolution[i];
if (value) {
for (j = columnStart[i];
j < columnStart[i] + columnLength[i]; j++) {
int iRow = row[j];
rowActivity[iRow] += value * element[j];
}
}
}
int numberBad = 0;
double sumBad = 0.0;
// check was approximately feasible
for (i = 0; i < numberRows; i++) {
if (rowActivity[i] < rowLower[i]) {
sumBad += rowLower[i] - rowActivity[i];
if (rowActivity[i] < rowLower[i] - 10.0*primalTolerance)
numberBad++;
} else if (rowActivity[i] > rowUpper[i]) {
sumBad += rowUpper[i] - rowActivity[i];
if (rowActivity[i] > rowUpper[i] + 10.0*primalTolerance)
numberBad++;
}
}
if (!numberBad) {
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double value = newSolution[iColumn];
// if away from lower bound mark that fact
if (value > originalLower) {
used_[iColumn] = numberSolutions_;
}
}
/*
Copy the solution to the array returned to the client. Grab a basis from
the solver (which, if it exists, is almost certainly infeasible, but it
should be ok for a dual start). The value returned as solutionValue is
conservative because of handling of newSolutionValue and bestChange, as
described above.
*/
// new solution
memcpy(betterSolution, newSolution, numberColumns*sizeof(double));
CoinWarmStartBasis * basis =
dynamic_cast<CoinWarmStartBasis *>(solver->getWarmStart()) ;
if (basis) {
model_->setBestSolutionBasis(* basis);
delete basis;
}
returnCode = 1;
solutionValue = newSolutionValue + bestChange;
} else {
// bad solution - should not happen so debug if see message
COIN_DETAIL_PRINT(printf("Local search got bad solution with %d infeasibilities summing to %g\n",
numberBad, sumBad));
}
}
}
/*
We're done. Clean up.
*/
delete [] newSolution;
delete [] rowActivity;
delete [] way;
delete [] cost;
delete [] save;
delete [] mark;
/*
Do we want to try swapping values between solutions?
swap_ is set elsewhere; it's not adjusted during heuristic execution.
Again, redundant test. We shouldn't be here if numberSolutions_ = 1.
*/
if (numberSolutions_ > 1 && (swap%10) == 1) {
// try merge
int returnCode2 = solutionFix( solutionValue, betterSolution, NULL);
if (returnCode2)
returnCode = 1;
}
return returnCode;
}
/*
First tries setting a variable to better value. If feasible then
tries setting others. If not feasible then tries swaps
Returns 1 if solution, 0 if not */
int
CbcHeuristicVND::solution(double & solutionValue,
double * betterSolution)
{
numCouldRun_++;
int returnCode = 0;
const double * bestSolution = model_->bestSolution();
if (!bestSolution)
return 0; // No solution found yet
#ifdef HEURISTIC_INFORM
printf("Entering heuristic %s - nRuns %d numCould %d when %d\n",
heuristicName(),numRuns_,numCouldRun_,when_);
#endif
if (numberSolutions_ < model_->getSolutionCount()) {
// new solution - add info
numberSolutions_ = model_->getSolutionCount();
int numberIntegers = model_->numberIntegers();
const int * integerVariable = model_->integerVariable();
int i;
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double value = bestSolution[iColumn];
if (value < originalLower) {
value = originalLower;
} else if (value > originalUpper) {
value = originalUpper;
}
}
}
int numberNodes = model_->getNodeCount();
if (howOften_ == 100) {
if (numberNodes < lastNode_ + 12)
return 0;
// Do at 50 and 100
if ((numberNodes > 40 && numberNodes <= 50) || (numberNodes > 90 && numberNodes < 100))
numberNodes = howOften_;
}
if ((numberNodes % howOften_) == 0 && (model_->getCurrentPassNumber() <= 1 ||
model_->getCurrentPassNumber() == 999999)) {
lastNode_ = model_->getNodeCount();
OsiSolverInterface * solver = model_->solver();
int numberIntegers = model_->numberIntegers();
const int * integerVariable = model_->integerVariable();
const double * currentSolution = solver->getColSolution();
OsiSolverInterface * newSolver = cloneBut(3); // was model_->continuousSolver()->clone();
//const double * colLower = newSolver->getColLower();
//const double * colUpper = newSolver->getColUpper();
double primalTolerance;
solver->getDblParam(OsiPrimalTolerance, primalTolerance);
// Sort on distance
double * distance = new double [numberIntegers];
int * which = new int [numberIntegers];
int i;
int nFix = 0;
double tolerance = 10.0 * primalTolerance;
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double valueInt = bestSolution[iColumn];
if (valueInt < originalLower) {
valueInt = originalLower;
} else if (valueInt > originalUpper) {
valueInt = originalUpper;
}
baseSolution_[iColumn] = currentSolution[iColumn];
distance[i] = fabs(currentSolution[iColumn] - valueInt);
which[i] = i;
if (fabs(currentSolution[iColumn] - valueInt) < tolerance)
nFix++;
}
CoinSort_2(distance, distance + numberIntegers, which);
nDifferent_ = numberIntegers - nFix;
stepSize_ = nDifferent_ / 10;
k_ = stepSize_;
//nFix = numberIntegers-stepSize_;
for (i = 0; i < nFix; i++) {
int j = which[i];
int iColumn = integerVariable[j];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
double valueInt = bestSolution[iColumn];
if (valueInt < originalLower) {
valueInt = originalLower;
} else if (valueInt > originalUpper) {
valueInt = originalUpper;
}
double nearest = floor(valueInt + 0.5);
newSolver->setColLower(iColumn, nearest);
newSolver->setColUpper(iColumn, nearest);
}
delete [] distance;
delete [] which;
if (nFix > numberIntegers / 5) {
//printf("%d integers have samish value\n",nFix);
returnCode = smallBranchAndBound(newSolver, numberNodes_, betterSolution, solutionValue,
model_->getCutoff(), "CbcHeuristicVND");
if (returnCode < 0)
returnCode = 0; // returned on size
else
numRuns_++;
if ((returnCode&1) != 0)
numberSuccesses_++;
//printf("return code %d",returnCode);
if ((returnCode&2) != 0) {
// could add cut
returnCode &= ~2;
//printf("could add cut with %d elements (if all 0-1)\n",nFix);
} else {
//printf("\n");
}
numberTries_++;
if ((numberTries_ % 10) == 0 && numberSuccesses_*3 < numberTries_)
howOften_ += static_cast<int> (howOften_ * decayFactor_);
}
delete newSolver;
}
return returnCode;
}
// This version fixes stuff and does IP
int
CbcHeuristicLocal::solutionFix(double & objectiveValue,
double * newSolution,
const int * /*keep*/)
{
/*
If when is set to off (0), or set to root (1) and we're not at the root,
return. If this heuristic discovered the current solution, don't continue.
*/
numCouldRun_++;
// See if to do
if (!when() || (when() == 1 && model_->phase() != 1))
return 0; // switched off
// Don't do if it was this heuristic which found solution!
if (this == model_->lastHeuristic())
return 0;
/*
Load up a new solver with the solution.
Why continuousSolver(), as opposed to solver()?
*/
OsiSolverInterface * newSolver = model_->continuousSolver()->clone();
const double * colLower = newSolver->getColLower();
//const double * colUpper = newSolver->getColUpper();
int numberIntegers = model_->numberIntegers();
const int * integerVariable = model_->integerVariable();
/*
The net effect here is that anything that hasn't moved from its lower bound
will be fixed at lower bound.
See comments in solution() w.r.t. asymmetric treatment of upper and lower
bounds.
*/
int i;
int nFix = 0;
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
const OsiObject * object = model_->object(i);
// get original bounds
double originalLower;
double originalUpper;
getIntegerInformation( object, originalLower, originalUpper);
newSolver->setColLower(iColumn, CoinMax(colLower[iColumn], originalLower));
if (!used_[iColumn]) {
newSolver->setColUpper(iColumn, colLower[iColumn]);
nFix++;
}
}
/*
Try a `small' branch-and-bound search. The notion here is that we've fixed a
lot of variables and reduced the amount of `free' problem to a point where a
small BaB search will suffice to fully explore the remaining problem. This
routine will execute integer presolve, then call branchAndBound to do the
actual search.
*/
int returnCode = 0;
#ifdef CLP_INVESTIGATE2
printf("Fixing %d out of %d (%d continuous)\n",
nFix, numberIntegers, newSolver->getNumCols() - numberIntegers);
#endif
if (nFix*10 <= numberIntegers) {
// see if we can fix more
int * which = new int [2*(numberIntegers-nFix)];
int * sort = which + (numberIntegers - nFix);
int n = 0;
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
if (used_[iColumn]) {
which[n] = iColumn;
sort[n++] = used_[iColumn];
}
}
CoinSort_2(sort, sort + n, which);
// only half fixed in total
n = CoinMin(n, numberIntegers / 2 - nFix);
int allow = CoinMax(numberSolutions_ - 2, sort[0]);
int nFix2 = 0;
for (i = 0; i < n; i++) {
int iColumn = integerVariable[i];
if (used_[iColumn] <= allow) {
newSolver->setColUpper(iColumn, colLower[iColumn]);
nFix2++;
} else {
break;
}
}
delete [] which;
nFix += nFix2;
#ifdef CLP_INVESTIGATE2
printf("Number fixed increased from %d to %d\n",
nFix - nFix2, nFix);
#endif
}
if (nFix*10 > numberIntegers) {
returnCode = smallBranchAndBound(newSolver, numberNodes_, newSolution, objectiveValue,
objectiveValue, "CbcHeuristicLocal");
/*
-2 is return due to user event, and -1 is overloaded with what look to be
two contradictory meanings.
*/
if (returnCode < 0) {
returnCode = 0; // returned on size
int numberColumns = newSolver->getNumCols();
int numberContinuous = numberColumns - numberIntegers;
if (numberContinuous > 2*numberIntegers &&
nFix*10 < numberColumns) {
#define LOCAL_FIX_CONTINUOUS
#ifdef LOCAL_FIX_CONTINUOUS
//const double * colUpper = newSolver->getColUpper();
const double * colLower = newSolver->getColLower();
int nAtLb = 0;
//double sumDj=0.0;
const double * dj = newSolver->getReducedCost();
double direction = newSolver->getObjSense();
for (int iColumn = 0; iColumn < numberColumns; iColumn++) {
if (!newSolver->isInteger(iColumn)) {
if (!used_[iColumn]) {
//double djValue = dj[iColumn]*direction;
nAtLb++;
//sumDj += djValue;
}
}
}
if (nAtLb) {
// fix some continuous
double * sort = new double[nAtLb];
int * which = new int [nAtLb];
//double threshold = CoinMax((0.01*sumDj)/static_cast<double>(nAtLb),1.0e-6);
int nFix2 = 0;
for (int iColumn = 0; iColumn < numberColumns; iColumn++) {
if (!newSolver->isInteger(iColumn)) {
if (!used_[iColumn]) {
double djValue = dj[iColumn] * direction;
if (djValue > 1.0e-6) {
sort[nFix2] = -djValue;
which[nFix2++] = iColumn;
}
}
}
}
CoinSort_2(sort, sort + nFix2, which);
int divisor = 2;
nFix2 = CoinMin(nFix2, (numberColumns - nFix) / divisor);
for (int i = 0; i < nFix2; i++) {
int iColumn = which[i];
newSolver->setColUpper(iColumn, colLower[iColumn]);
}
delete [] sort;
delete [] which;
#ifdef CLP_INVESTIGATE2
printf("%d integers have zero value, and %d continuous fixed at lb\n",
nFix, nFix2);
#endif
returnCode = smallBranchAndBound(newSolver,
numberNodes_, newSolution,
objectiveValue,
objectiveValue, "CbcHeuristicLocal");
if (returnCode < 0)
returnCode = 0; // returned on size
}
#endif
}
}
}
/*
If the result is complete exploration with a solution (3) or proven
infeasibility (2), we could generate a cut (the AI folks would call it a
nogood) to prevent us from going down this route in the future.
*/
if ((returnCode&2) != 0) {
// could add cut
returnCode &= ~2;
}
delete newSolver;
return returnCode;
}
