// Create result
void OsiSolverResult::createResult(const OsiSolverInterface &solver, const double *lowerBefore,
const double *upperBefore)
{
delete[] primalSolution_;
delete[] dualSolution_;
if (solver.isProvenOptimal() && !solver.isDualObjectiveLimitReached()) {
objectiveValue_ = solver.getObjValue() * solver.getObjSense();
CoinWarmStartBasis *basis = dynamic_cast< CoinWarmStartBasis * >(solver.getWarmStart());
assert(basis);
basis_ = *basis;
int numberRows = basis_.getNumArtificial();
int numberColumns = basis_.getNumStructural();
assert(numberColumns == solver.getNumCols());
assert(numberRows == solver.getNumRows());
primalSolution_ = CoinCopyOfArray(solver.getColSolution(), numberColumns);
dualSolution_ = CoinCopyOfArray(solver.getRowPrice(), numberRows);
fixed_.addBranch(-1, numberColumns, lowerBefore, solver.getColLower(),
upperBefore, solver.getColUpper());
} else {
// infeasible
objectiveValue_ = COIN_DBL_MAX;
basis_ = CoinWarmStartBasis();
;
primalSolution_ = NULL;
dualSolution_ = NULL;
}
}
int
main(void)
{
// Create a problem pointer. We use the base class here.
OsiSolverInterface *si;
// When we instantiate the object, we need a specific derived class.
si = new OsiClpSolverInterface;
// Read in an mps file. This one's from the MIPLIB library.
si->readMps("../../Data/Sample/p0033");
// Solve the (relaxation of the) problem
si->initialSolve();
// Check the solution
if ( si->isProvenOptimal() ) {
std::cout << "Found optimal solution!" << std::endl;
std::cout << "Objective value is " << si->getObjValue() << std::endl;
int n = si->getNumCols();
const double *solution;
solution = si->getColSolution();
// We could then print the solution or examine it.
} else {
std::cout << "Didn't find optimal solution." << std::endl;
// Could then check other status functions.
}
return 0;
}
void SmpsIO(const char * const name )
{
SmiScnModel smi;
// read SMPS model from files
// <name>.core, <name>.time, and <name>.stoch
smi.readSmps(name);
// generate OSI solver object
// here we use OsiClp
OsiClpSolverInterface *clp = new OsiClpSolverInterface();
// set solver object for SmiScnModel
smi.setOsiSolverHandle(*clp);
// load solver data
// this step generates the deterministic equivalent
// and returns an OsiSolver object
OsiSolverInterface *osiStoch = smi.loadOsiSolverData();
// set some nice Hints to the OSI solver
osiStoch->setHintParam(OsiDoPresolveInInitial,true);
osiStoch->setHintParam(OsiDoScale,true);
osiStoch->setHintParam(OsiDoCrash,true);
// solve
osiStoch->initialSolve();
// print results
printf("Solved stochastic program %s\n", name);
printf("Number of rows: %d\n",osiStoch->getNumRows());
printf("Number of cols: %d\n",osiStoch->getNumCols());
printf("Optimal value: %g\n",osiStoch->getObjValue());
// print solution to file
string outfilename(name);
const string suffix(".out");
outfilename = outfilename + suffix;
FILE *fp = fopen(outfilename.c_str(),"w");
int numScenarios=smi.getNumScenarios();
for (int i=0 ; i<numScenarios; ++i) {
double *dsoln=NULL;
int numCols=0;
fprintf(fp,"Scenario %d \n",i);
dsoln = smi.getColSolution(i,&numCols);
for (int j=0; j<numCols; j++)
fprintf(fp,"%g \n",dsoln[j]);
free(dsoln);
}
fclose(fp);
}
//--------------------------------------------------------------------------
void
CglKnapsackCoverUnitTest(
const OsiSolverInterface * baseSiP,
const std::string mpsDir )
{
int i;
CoinRelFltEq eq(0.000001);
// Test default constructor
{
CglKnapsackCover kccGenerator;
}
// Test copy & assignment
{
CglKnapsackCover rhs;
{
CglKnapsackCover kccGenerator;
CglKnapsackCover cgC(kccGenerator);
rhs=kccGenerator;
}
}
// test exactSolveKnapsack
{
CglKnapsackCover kccg;
const int n=7;
double c=50;
double p[n] = {70,20,39,37,7,5,10};
double w[n] = {31, 10, 20, 19, 4, 3, 6};
double z;
int x[n];
int exactsol = kccg.exactSolveKnapsack(n, c, p, w, z, x);
assert(exactsol==1);
assert (z == 107);
assert (x[0]==1);
assert (x[1]==0);
assert (x[2]==0);
assert (x[3]==1);
assert (x[4]==0);
assert (x[5]==0);
assert (x[6]==0);
}
/*
// Testcase /u/rlh/osl2/mps/scOneInt.mps
// Model has 3 continous, 2 binary, and 1 general
// integer variable.
{
OsiSolverInterface * siP = baseSiP->clone();
int * complement=NULL;
double * xstar=NULL;
siP->readMps("../Mps/scOneInt","mps");
CglKnapsackCover kccg;
int nCols=siP->getNumCols();
// Test the siP methods for detecting
// variable type
int numCont=0, numBinary=0, numIntNonBinary=0, numInt=0;
for (int thisCol=0; thisCol<nCols; thisCol++) {
if ( siP->isContinuous(thisCol) ) numCont++;
if ( siP->isBinary(thisCol) ) numBinary++;
if ( siP->isIntegerNonBinary(thisCol) ) numIntNonBinary++;
if ( siP->isInteger(thisCol) ) numInt++;
}
assert(numCont==3);
assert(numBinary==2);
assert(numIntNonBinary==1);
assert(numInt==3);
// Test initializeCutGenerator
siP->initialSolve();
assert(xstar !=NULL);
for (i=0; i<nCols; i++){
assert(complement[i]==0);
}
int nRows=siP->getNumRows();
for (i=0; i<nRows; i++){
int vectorsize = siP->getMatrixByRow()->vectorSize(i);
assert(vectorsize==2);
}
kccg.cleanUpCutGenerator(complement,xstar);
delete siP;
}
*/
// Testcase /u/rlh/osl2/mps/tp3.mps
// Models has 3 cols, 3 rows
// Row 0 yields a knapsack, others do not.
{
// setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"tp3");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
OsiCuts cs;
CoinPackedVector krow;
double b=0;
int nCols=siP->getNumCols();
int * complement=new int [nCols];
double * xstar=new double [nCols];
CglKnapsackCover kccg;
// solve LP relaxation
// a "must" before calling initialization
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelaxBefore<<std::endl;
assert( eq(siP->getObjValue(), 97.185) );
double mycs[] = {.627, .667558333333, .038};
siP->setColSolution(mycs);
const double *colsol = siP->getColSolution();
int k;
for (k=0; k<nCols; k++){
xstar[k]=colsol[k];
complement[k]=0;
}
// test deriveAKnapsack
int rind = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
const CoinShallowPackedVector reqdBySunCC = siP->getMatrixByRow()->getVector(rind) ;
int deriveaknap = kccg.deriveAKnapsack(*siP, cs, krow,b,complement,xstar,rind,reqdBySunCC);
assert(deriveaknap ==1);
assert(complement[0]==0);
assert(complement[1]==1);
assert(complement[2]==1);
int inx[3] = {0,1,2};
double el[3] = {161, 120, 68};
CoinPackedVector r;
r.setVector(3,inx,el);
assert (krow == r);
//assert (b == 183.0); ????? but x1 and x2 at 1 is valid
// test findGreedyCover
CoinPackedVector cover,remainder;
#if 0
int findgreedy = kccg.findGreedyCover( 0, krow, b, xstar, cover, remainder );
assert( findgreedy == 1 );
int coveri = cover.getNumElements();
assert( cover.getNumElements() == 2);
coveri = cover.getIndices()[0];
assert( cover.getIndices()[0] == 0);
assert( cover.getIndices()[1] == 1);
assert( cover.getElements()[0] == 161.0);
assert( cover.getElements()[1] == 120.0);
assert( remainder.getNumElements() == 1);
assert( remainder.getIndices()[0] == 2);
assert( remainder.getElements()[0] == 68.0);
// test liftCoverCut
CoinPackedVector cut;
double * rowupper = ekk_rowupper(model);
double cutRhs = cover.getNumElements() - 1.0;
kccg.liftCoverCut(b, krow.getNumElements(),
cover, remainder,
cut);
assert ( cut.getNumElements() == 3 );
assert ( cut.getIndices()[0] == 0 );
assert ( cut.getIndices()[1] == 1 );
assert ( cut.getIndices()[2] == 2 );
assert( cut.getElements()[0] == 1 );
assert( cut.getElements()[1] == 1 );
assert( eq(cut.getElements()[2], 0.087719) );
// test liftAndUncomplementAndAdd
OsiCuts cuts;
kccg.liftAndUncomplementAndAdd(*siP.getRowUpper()[0],krow,b,complement,0,
cover,remainder,cuts);
int sizerowcuts = cuts.sizeRowCuts();
assert ( sizerowcuts== 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 3;
int sampleCols[sampleSize]={0,1,2};
double sampleElems[sampleSize]={1.0,-1.0,-0.087719};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-DBL_MAX);
sampleRowCut.setUb(-0.087719);
bool equiv = testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) );
assert ( equiv );
#endif
// test find PseudoJohnAndEllisCover
cover.setVector(0,NULL, NULL);
remainder.setVector(0,NULL,NULL);
rind = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
int findPJE = kccg.findPseudoJohnAndEllisCover( rind, krow,
b, xstar, cover, remainder );
assert( findPJE == 1 );
assert ( cover.getIndices()[0] == 0 );
assert ( cover.getIndices()[1] == 2 );
assert ( cover.getElements()[0] == 161 );
assert ( cover.getElements()[1] == 68 );
assert ( remainder.getIndices()[0] == 1 );
assert ( remainder.getElements()[0] == 120 );
OsiCuts cuts;
kccg.liftAndUncomplementAndAdd((*siP).getRowUpper()[rind],krow,b, complement, rind,
cover,remainder,cuts);
assert (cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
const int sampleSize = 3;
int sampleCols[sampleSize]={0,1,2};
double sampleElems[sampleSize]={1.0, -1.0, -1.0};
OsiRowCut sampleRowCut;
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-COIN_DBL_MAX);
sampleRowCut.setUb(-1.0);
// test for 'close enough'
assert( testRowPV.isEquivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) ) );
// Reset complement & test next row
for (i=0; i<nCols; i++){
complement[i]=0;
}
rind++;
const CoinShallowPackedVector reqdBySunCC2 = siP->getMatrixByRow()->getVector(rind) ;
deriveaknap = kccg.deriveAKnapsack(*siP,cuts,krow,b,complement,xstar,rind,reqdBySunCC2);
assert(deriveaknap==0);
// Reset complement & test next row
for (i=0; i<nCols; i++){
complement[i]=0;
}
const CoinShallowPackedVector reqdBySunCC3 = siP->getMatrixByRow()->getVector(2) ;
deriveaknap = kccg.deriveAKnapsack(*siP,cuts,krow,b,complement,xstar,2,
reqdBySunCC3);
assert(deriveaknap == 0);
// Clean up
delete [] complement;
delete [] xstar;
delete siP;
}
#if 0
// Testcase /u/rlh/osl2/mps/tp4.mps
// Models has 6 cols, 1 knapsack row and
// 3 rows explicily bounding variables
// Row 0 yields a knapsack cover cut
// using findGreedyCover which moves the
// LP objective function value.
{
// Setup
EKKContext * env=ekk_initializeContext();
EKKModel * model = ekk_newModel(env,"");
OsiSolverInterface si(model);
ekk_importModel(model, "tp4.mps");
CglKnapsackCover kccg;
kccg.ekk_validateIntType(si);
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
ekk_allSlackBasis(model);
ekk_crash(model,1);
ekk_primalSimplex(model,1);
double lpRelaxBefore=ekk_getRobjvalue(model);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
// Determine if lp sol is ip optimal
// Note: no ekk_function to do this
int nCols=ekk_getInumcols(model);
double * optLpSol = ekk_colsol(model);
int ipOpt = 1;
i=0;
while (i++<nCols && ipOpt){
if(optLpSol[i] < 1.0-1.0e-08 && optLpSol[i]> 1.0e-08) ipOpt = 0;
}
if (ipOpt){
#ifdef CGL_DEBUG
printf("Lp solution is within ip optimality tolerance\n");
#endif
}
else {
OsiSolverInterface iModel(model);
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(iModel,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = iModel.applyCuts(cuts);
ekk_mergeBlocks(model,1);
ekk_dualSimplex(model);
double lpRelaxAfter=ekk_getRobjvalue(model);
#ifdef CGL_DEBUG
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// This may need to be updated as other
// minimal cover finders are added
assert( cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 6;
int sampleCols[sampleSize]={0,1,2,3,4,5};
double sampleElems[sampleSize]={1.0,1.0,1.0,1.0,0.5, 2.0};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-DBL_MAX);
sampleRowCut.setUb(3.0);
bool equiv = testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) );
assert( testRowPV.equivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05) ) );
}
// Exit out of OSL
ekk_deleteModel(model);
ekk_endContext(env);
}
#endif
// Testcase /u/rlh/osl2/mps/tp5.mps
// Models has 6 cols, 1 knapsack row and
// 3 rows explicily bounding variables
// Row 0 yields a knapsack cover cut
// using findGreedyCover which moves the
// LP objective function value.
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"tp5");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, -51.66666666667) );
double mycs[] = {.8999999999, .899999999999, .89999999999, 1.110223e-16, .5166666666667, 0};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
// Determine if lp sol is 0/1 optimal
int nCols=siP->getNumCols();
const double * optLpSol = siP->getColSolution();
bool ipOpt = true;
i=0;
while (i++<nCols && ipOpt){
if(optLpSol[i] > kccg.epsilon_ && optLpSol[i] < kccg.onetol_) ipOpt = false;
}
if (ipOpt){
#ifdef CGL_DEBUG
printf("Lp solution is within ip optimality tolerance\n");
#endif
}
else {
// set up
OsiCuts cuts;
CoinPackedVector krow;
double b=0.0;
int * complement=new int[nCols];
double * xstar=new double[nCols];
// initialize cut generator
const double *colsol = siP->getColSolution();
for (i=0; i<nCols; i++){
xstar[i]=colsol[i];
complement[i]=0;
}
int row = ( siP->getRowSense()[0] == 'N' ) ? 1 : 0;
// transform row into canonical knapsack form
const CoinShallowPackedVector reqdBySunCC = siP->getMatrixByRow()->getVector(row) ;
if (kccg.deriveAKnapsack(*siP, cuts, krow, b, complement, xstar, row,reqdBySunCC)){
CoinPackedVector cover, remainder;
// apply greedy logic to detect violated minimal cover inequalities
if (kccg.findGreedyCover(row, krow, b, xstar, cover, remainder) == 1){
// lift, uncomplements, and add cut to cut set
kccg.liftAndUncomplementAndAdd((*siP).getRowUpper()[row],krow, b, complement, row, cover, remainder, cuts);
}
// reset optimal column solution (xstar) information in OSL
const double * rowupper = siP->getRowUpper();
int k;
if (fabs(b-rowupper[row]) > 1.0e-05) {
for(k=0; k<krow.getNumElements(); k++) {
if (complement[krow.getIndices()[k]]){
xstar[krow.getIndices()[k]]= 1.0-xstar[krow.getIndices()[k]];
complement[krow.getIndices()[k]]=0;
}
}
}
// clean up
delete [] complement;
delete [] xstar;
}
// apply the cuts
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, -30.0) );
#ifdef CGL_DEBUG
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
// test that expected cut was detected
assert( lpRelaxBefore < lpRelaxAfter );
assert( cuts.sizeRowCuts() == 1 );
OsiRowCut testRowCut = cuts.rowCut(0);
CoinPackedVector testRowPV = testRowCut.row();
OsiRowCut sampleRowCut;
const int sampleSize = 6;
int sampleCols[sampleSize]={0,1,2,3,4,5};
double sampleElems[sampleSize]={1.0,1.0,1.0,0.25,1.0,2.0};
sampleRowCut.setRow(sampleSize,sampleCols,sampleElems);
sampleRowCut.setLb(-COIN_DBL_MAX);
sampleRowCut.setUb(3.0);
assert(testRowPV.isEquivalent(sampleRowCut.row(),CoinRelFltEq(1.0e-05)));
}
delete siP;
}
// Testcase /u/rlh/osl2/mps/p0033
// Miplib3 problem p0033
// Test that no cuts chop off the optimal solution
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
int nCols=siP->getNumCols();
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
double mycs[] = {0, 1, 0, 0, -2.0837010502455788e-19, 1, 0, 0, 1,
0.021739130434782594, 0.35652173913043478,
-6.7220534694101275e-18, 5.3125906451789717e-18,
1, 0, 1.9298798670241979e-17, 0, 0, 0,
7.8875708048320448e-18, 0.5, 0,
0.85999999999999999, 1, 1, 0.57999999999999996,
1, 0, 1, 0, 0.25, 0, 0.67500000000000004};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 2829.0597826086955) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// the CoinPackedVector p0033 is the optimal
// IP solution to the miplib problem p0033
int objIndices[14] = {
0, 6, 7, 9, 13, 17, 18,
22, 24, 25, 26, 27, 28, 29 };
CoinPackedVector p0033(14,objIndices,1.0);
// Sanity check
const double * objective=siP->getObjCoefficients();
double ofv =0 ;
int r;
for (r=0; r<nCols; r++){
ofv=ofv + p0033[r]*objective[r];
}
CoinRelFltEq eq;
assert( eq(ofv,3089.0) );
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
double p0033Sum = (rpv*p0033).sum();
assert (p0033Sum <= rcut.ub() );
}
delete siP;
}
// if a debug file is there then look at it
{
FILE * fp = fopen("knapsack.debug","r");
if (fp) {
int ncol,nel;
double up;
int x = fscanf(fp,"%d %d %lg",&ncol,&nel,&up);
if (x<=0)
throw("bad fscanf");
printf("%d columns, %d elements, upper %g\n",ncol,nel,up);
double * sol1 = new double[nel];
double * el1 = new double[nel];
int * col1 = new int[nel];
CoinBigIndex * start = new CoinBigIndex [ncol+1];
memset(start,0,ncol*sizeof(CoinBigIndex ));
int * row = new int[nel];
int i;
for (i=0;i<nel;i++) {
x=fscanf(fp,"%d %lg %lg",col1+i,el1+i,sol1+i);
if (x<=0)
throw("bad fscanf");
printf("[%d, e=%g, v=%g] ",col1[i],el1[i],sol1[i]);
start[col1[i]]=1;
row[i]=0;
}
printf("\n");
// Setup
OsiSolverInterface * siP = baseSiP->clone();
double lo=-1.0e30;
double * upper = new double[ncol];
start[ncol]=nel;
int last=0;
for (i=0;i<ncol;i++) {
upper[i]=1.0;
int marked=start[i];
start[i]=last;
if (marked)
last++;
}
siP->loadProblem(ncol,1,start,row,el1,NULL,upper,NULL,&lo,&up);
// use upper for solution
memset(upper,0,ncol*sizeof(double));
for (i=0;i<nel;i++) {
int icol=col1[i];
upper[icol]=sol1[i];
siP->setInteger(icol);
}
siP->setColSolution(upper);
delete [] sol1;
delete [] el1;
delete [] col1;
delete [] start;
delete [] row;
delete [] upper;
CglKnapsackCover kccg;
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
// print out and compare to known cuts
int numberCuts = cuts.sizeRowCuts();
if (numberCuts) {
for (i=0;i<numberCuts;i++) {
OsiRowCut * thisCut = cuts.rowCutPtr(i);
int n=thisCut->row().getNumElements();
printf("Cut %d has %d entries, rhs %g %g =>",i,n,thisCut->lb(),
thisCut->ub());
int j;
const int * index = thisCut->row().getIndices();
const double * element = thisCut->row().getElements();
for (j=0;j<n;j++) {
printf(" (%d,%g)",index[j],element[j]);
}
printf("\n");
}
}
fclose(fp);
}
}
// Testcase /u/rlh/osl2/mps/p0201
// Miplib3 problem p0282
// Test that no cuts chop off the optimal ip solution
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"p0201");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
const int nCols=siP->getNumCols();
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparisn
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 6875.) );
double mycs[] =
{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5,
0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0,
0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0.5, 0, 0, 0, 0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1};
siP->setColSolution(mycs);
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 7125) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
// Optimal IP solution to p0201
int objIndices[22] = { 8, 10, 21, 38, 39, 56,
60, 74, 79, 92, 94, 110, 111, 128, 132, 146,
151,164, 166, 182,183, 200 };
CoinPackedVector p0201(22,objIndices,1.0);
// Sanity check
const double * objective=siP->getObjCoefficients();
double ofv =0 ;
int r;
for (r=0; r<nCols; r++){
ofv=ofv + p0201[r]*objective[r];
}
CoinRelFltEq eq;
assert( eq(ofv,7615.0) );
//printf("p0201 optimal ofv = %g\n",ofv);
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
double p0201Sum = (rpv*p0201).sum();
assert (p0201Sum <= rcut.ub() );
}
delete siP;
}
// see if I get the same covers that N&W get
{
OsiSolverInterface * siP=baseSiP->clone();
std::string fn(mpsDir+"nw460");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, -225.68951787852194) );
double mycs[] = {0.7099213482046447, 0, 0.34185802225477174, 1, 1, 0, 1, 1, 0};
siP->setColSolution(mycs);
OsiCuts cuts;
// Test generateCuts method
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, -176) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
#ifdef MJS
assert( lpRelaxBefore < lpRelaxAfter );
#endif
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
int j;
printf("Row cut number %i has rhs = %g\n",i,rcut.ub());
for (j=0; j<rpv.getNumElements(); j++){
printf("index %i, element %g\n", rpv.getIndices()[j], rpv.getElements()[j]);
}
printf("\n");
}
delete siP;
}
// Debugging: try "exmip1.mps"
{
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"exmip1");
siP->readMps(fn.c_str(),"mps");
// All integer variables should be binary.
// Assert that this is true.
for ( i = 0; i < siP->getNumCols(); i++ )
if ( siP->isInteger(i) )
assert(siP->getColUpper()[i]==1.0 && siP->isBinary(i));
CglKnapsackCover kccg;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 3.2368421052631575) );
double mycs[] = {2.5, 0, 0, 0.6428571428571429, 0.5, 4, 0, 0.26315789473684253};
siP->setColSolution(mycs);
// Test generateCuts method
OsiCuts cuts;
kccg.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
assert( eq(lpRelaxAfter, 3.2368421052631575) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore <= lpRelaxAfter );
delete siP;
}
#ifdef CGL_DEBUG
// See what findLPMostViolatedMinCover for knapsack with 2 elements does
{
int nCols = 2;
int row = 1;
CoinPackedVector krow;
double e[2] = {5,10};
int ii[2] = {0,1};
krow.setVector(nCols,ii,e);
double b=11;
double xstar[2] = {.2,.9};
CoinPackedVector cover;
CoinPackedVector remainder;
CglKnapsackCover kccg;
kccg.findLPMostViolatedMinCover(nCols, row, krow, b, xstar, cover, remainder);
printf("num in cover = %i\n",cover.getNumElements());
int j;
for (j=0; j<cover.getNumElements(); j++){
printf(" index %i element % g\n", cover.getIndices()[j], cover.getElements()[j]);
}
}
#endif
#ifdef CGL_DEBUG
// see what findLPMostViolatedMinCover does
{
int nCols = 5;
int row = 1;
CoinPackedVector krow;
double e[5] = {1,1,1,1,10};
int ii[5] = {0,1,2,3,4};
krow.setVector(nCols,ii,e);
double b=11;
double xstar[5] = {.9,.9,1,1,.1};
CoinPackedVector cover;
CoinPackedVector remainder;
CglKnapsackCover kccg;
kccg.findLPMostViolatedMinCover(nCols, row, krow, b, xstar, cover, remainder);
printf("num in cover = %i\n",cover.getNumElements());
int j;
for (j=0; j<cover.getNumElements(); j++){
printf(" index %i element % g\n", cover.getIndices()[j], cover.getElements()[j]);
}
}
#endif
}
int main( int argc, char **argv )
{
if ( argc < 2 )
{
printf("Invalid number of parameters!\n");
exit( EXIT_FAILURE );
}
char problemName[ 256 ];
getFileName( problemName, argv[1] );
clock_t start = clock();
OsiClpSolverInterface *realSolver = new OsiClpSolverInterface();
realSolver->getModelPtr()->setPerturbation(50); /* makes CLP faster for hard instances */
OsiSolverInterface *solver = (OsiSolverInterface*) realSolver;
parseParameters( argc, argv );
readLP( solver, argv[1] );
FILE *log = NULL;
if(!output.empty())
{
log = fopen(output.c_str(), "a");
if(!log)
{
printf("Could not open the file!\n");
exit(EXIT_FAILURE);
}
}
const int numCols = solver->getNumCols(), numRows = solver->getNumRows();
int pass = 0, newCuts = 0, totalCuts = 0;
double pTime, opt, cgTime;
CGraph *cgraph = NULL;
if(sepMethod == Npsep)
cgraph = build_cgraph_osi( solver );
if(!optFile.empty())
{
getOptimals();
if(optimals.find(problemName) == optimals.end())
{
fprintf(stderr, "ERROR: optimal value not found!\n");
exit(EXIT_FAILURE);
}
opt = optimals[problemName];
}
solver->initialSolve();
if (!solver->isProvenOptimal())
{
if (solver->isAbandoned())
{
fprintf( stderr, "LP solver abandoned due to numerical dificulties.\n" );
exit( EXIT_FAILURE );
}
if (solver->isProvenPrimalInfeasible())
{
fprintf( stderr, "LP solver says PRIMAL INFEASIBLE.\n" );
exit( EXIT_FAILURE );
}
if (solver->isProvenDualInfeasible())
{
fprintf( stderr, "LP solver says DUAL INFEASIBLE.\n" );
exit( EXIT_FAILURE );
}
if (solver->isPrimalObjectiveLimitReached())
{
fprintf( stderr, "LP solver says isPrimalObjectiveLimitReached.\n" );
exit( EXIT_FAILURE );
}
if (solver->isDualObjectiveLimitReached())
{
fprintf( stderr, "LP solver says isDualObjectiveLimitReached.\n" );
exit( EXIT_FAILURE );
}
if (solver->isIterationLimitReached())
{
fprintf( stderr, "LP solver says isIterationLimitReached.\n" );
exit( EXIT_FAILURE );
}
fprintf( stderr, "ERROR: Could not solve LP relaxation to optimality. Checking status...\n" );
exit( EXIT_FAILURE );
}
double initialBound = solver->getObjValue();
printf("%.2lf %d %d %.7lf", ((double)(clock()-start)) / ((double)CLOCKS_PER_SEC), pass, 0, solver->getObjValue());
if(!optFile.empty())
{
printf(" %.7lf %.7lf", opt, abs_mip_gap(solver->getObjValue(), opt));
}
printf("\n");
do
{
clock_t startSep = clock();
newCuts = 0;
switch (sepMethod)
{
case Npsep:
{
CglEClique cliqueGen;
OsiCuts cuts;
CglTreeInfo info;
info.level = 0;
info.pass = 1;
vector<string> varNames = getVarNames(solver->getColNames(), numCols);
cliqueGen.parseParameters( argc, (const char**)argv );
cliqueGen.setCGraph( cgraph );
cliqueGen.setGenOddHoles( true ); //allow (or not) inserting odd hole cuts
cliqueGen.colNames = &varNames;
cliqueGen.generateCuts( *solver, cuts, info );
newCuts = cuts.sizeCuts();
solver->applyCuts( cuts );
}
break;
case CglSepM:
{
CglClique cliqueGen;
OsiCuts cuts;
CglTreeInfo info;
info.level = 0;
info.pass = 1;
cliqueGen.setMinViolation( MIN_VIOLATION );
cliqueGen.setStarCliqueReport(false);
cliqueGen.setRowCliqueReport(false);
cliqueGen.generateCuts( *solver, cuts, info );
newCuts = cuts.sizeCuts();
solver->applyCuts( cuts );
}
break;
Default:
{
fprintf( stderr, "Separation Method does not recognized!\n" );
exit( EXIT_FAILURE );
}
}
pTime = ((double)(clock()-start)) / ((double)CLOCKS_PER_SEC);
if(pTime > MAX_TIME) break;
totalCuts += newCuts;
++pass;
if (newCuts)
{
solver->resolve();
if (!solver->isProvenOptimal())
{
if (solver->isAbandoned())
{
fprintf( stderr, "LP solver abandoned due to numerical dificulties.\n" );
exit( EXIT_FAILURE );
}
if (solver->isProvenPrimalInfeasible())
{
fprintf( stderr, "LP solver says PRIMAL INFEASIBLE.\n" );
exit( EXIT_FAILURE );
}
if (solver->isProvenDualInfeasible())
{
fprintf( stderr, "LP solver says DUAL INFEASIBLE.\n" );
exit( EXIT_FAILURE );
}
if (solver->isPrimalObjectiveLimitReached())
{
fprintf( stderr, "LP solver says isPrimalObjectiveLimitReached.\n" );
exit( EXIT_FAILURE );
}
if (solver->isDualObjectiveLimitReached())
{
fprintf( stderr, "LP solver says isDualObjectiveLimitReached.\n" );
exit( EXIT_FAILURE );
}
if (solver->isIterationLimitReached())
{
fprintf( stderr, "LP solver says isIterationLimitReached.\n" );
exit( EXIT_FAILURE );
}
fprintf( stderr, "ERROR: Could not solve LP relaxation. Exiting.\n" );
exit( EXIT_FAILURE );
}
pTime = ((double)(clock()-start)) / ((double)CLOCKS_PER_SEC);
if(pTime > MAX_TIME) break;
double sepTime = ((double)(clock()-startSep)) / ((double)CLOCKS_PER_SEC);
printf("%.2lf %d %d %.7lf", sepTime, pass, newCuts, solver->getObjValue());
if(!optFile.empty())
printf(" %.7lf %.7lf", opt, abs_mip_gap(solver->getObjValue(), opt));
printf("\n");
}
}
while ( (newCuts>0) && (pass<MAX_PASSES) ) ;
if(log)
{
double totalTime = ((double)(clock()-start)) / ((double)CLOCKS_PER_SEC);
fprintf(log, "%s %.2lf %d %d %.7lf", problemName, totalTime, pass - 1, totalCuts, solver->getObjValue());
if(!optFile.empty())
fprintf(log, " %.7lf", abs_mip_gap(solver->getObjValue(), opt));
fprintf(log, "\n");
}
if(cgraph)
cgraph_free( &cgraph );
delete realSolver;
return EXIT_SUCCESS;
}
//#############################################################################
void
MibSHeuristic::lowerObjHeuristic()
{
/*
optimize wrt to lower-level objective
over current feasible lp feasible region
*/
MibSModel * model = MibSModel_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * hSolver = new OsiCbcSolverInterface();
OsiSolverInterface* hSolver = new OsiSymSolverInterface();
double objSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
//int tCols(lCols + uCols);
int tCols(oSolver->getNumCols());
//assert(tCols == oSolver->getNumCols());
hSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
hSolver->setInteger(j);
}
double * nObjCoeffs = new double[tCols];
int i(0), index(0);
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
nObjCoeffs[index] = lObjCoeffs[i];
}
//MibS objective sense is the opposite of OSI's!
hSolver->setObjSense(objSense);
hSolver->setObjective(nObjCoeffs);
//double cutoff(model->getCutoff());
double cutoff(model->getKnowledgeBroker()->getIncumbentValue());
if(model->getNumSolutions()){
CoinPackedVector objCon;
//double rhs(cutoff * objSense);
//double smlTol(1.0);
double rhs(cutoff);
for(i = 0; i < tCols; i++){
objCon.insert(i, oSolver->getObjCoefficients()[i]
* oSolver->getObjSense());
}
hSolver->addRow(objCon, - hSolver->getInfinity(), rhs);
}
if(0)
hSolver->writeLp("lobjheurstic");
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(hSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("max_active_nodes", 1);
}
hSolver->branchAndBound();
if(hSolver->isProvenOptimal()){
double upperObjVal(0.0);
/*****************NEW ******************/
MibSSolution *mibSol = NULL;
OsiSolverInterface * lSolver = model->bS_->setUpModel(hSolver, true);
if(0){
lSolver->writeLp("tmp");
}
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("max_active_nodes", 1);
}
lSolver->branchAndBound();
if (lSolver->isProvenOptimal()){
const double * sol = hSolver->getColSolution();
double objVal(lSolver->getObjValue() * objSense);
double etol(etol_);
double lowerObj = getLowerObj(sol, objSense);
double * optUpperSolutionOrd = new double[uCols];
double * optLowerSolutionOrd = new double[lCols];
CoinZeroN(optUpperSolutionOrd, uCols);
CoinZeroN(optLowerSolutionOrd, lCols);
if(fabs(objVal - lowerObj) < etol){
/** Current solution is bilevel feasible **/
for(i = 0; i < tCols; i++)
upperObjVal +=
hSolver->getColSolution()[i] * oSolver->getObjCoefficients()[i];
mibSol = new MibSSolution(hSolver->getNumCols(),
hSolver->getColSolution(),
upperObjVal,
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
else{
/* solution is not bilevel feasible, create one that is */
const double * uSol = hSolver->getColSolution();
const double * lSol = lSolver->getColSolution();
int numElements(hSolver->getNumCols());
int i(0), pos(0), index(0);
double * lpSolution = new double[numElements];
double upperObj(0.0);
//FIXME: problem is still here. indices may be wrong.
//also is all this necessary, or can we just paste together uSol and lSol?
//this may be an old comment
for(i = 0; i < numElements; i++){
pos = model->bS_->binarySearch(0, lCols - 1, i, lColIndices);
if(pos < 0){
pos = model->bS_->binarySearch(0, uCols - 1, i, uColIndices);
if (pos >= 0){
optUpperSolutionOrd[pos] = uSol[i];
}
}
else{
optLowerSolutionOrd[pos] = lSol[pos];
}
}
for(i = 0; i < uCols; i++){
index = uColIndices[i];
lpSolution[index] = optUpperSolutionOrd[i];
upperObj +=
optUpperSolutionOrd[i] * oSolver->getObjCoefficients()[index];
}
for(i = 0; i < lCols; i++){
index = lColIndices[i];
lpSolution[index] = optLowerSolutionOrd[i];
upperObj +=
optLowerSolutionOrd[i] * oSolver->getObjCoefficients()[index];
}
if(model->checkUpperFeasibility(lpSolution)){
mibSol = new MibSSolution(hSolver->getNumCols(),
lpSolution,
upperObj * oSolver->getObjSense(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
delete [] lpSolution;
}
}
delete lSolver;
}
delete hSolver;
}
//#############################################################################
mcSol
MibSHeuristic::solveSubproblem(double beta)
{
/*
optimize wrt to weighted upper-level objective
over current feasible lp feasible region
*/
MibSModel * model = MibSModel_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * sSolver = new OsiCbcSolverInterface();
OsiSolverInterface* sSolver = new OsiSymSolverInterface();
//sSolver = oSolver->clone();
//OsiSolverInterface * sSolver = tmpSolver;
//OsiSolverInterface * tmpSolver = new OsiSolverInterface(oSolver);
double uObjSense(oSolver->getObjSense());
double lObjSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
const double * uObjCoeffs = oSolver->getObjCoefficients();
double etol(etol_);
int tCols(uCols + lCols);
assert(tCols == oSolver->getNumCols());
sSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
sSolver->setInteger(j);
}
double * nObjCoeffs = new double[tCols];
int i(0), index(0);
CoinZeroN(nObjCoeffs, tCols);
/* Multiply the UL columns of the UL objective by beta */
for(i = 0; i < uCols; i++){
index = uColIndices[i];
if(fabs(uObjCoeffs[index]) > etol)
nObjCoeffs[index] = beta * uObjCoeffs[index] * uObjSense;
else
nObjCoeffs[index] = 0.0;
}
/* Multiply the LL columns of the UL objective by beta */
for(i = 0; i < lCols; i++){
index = lColIndices[i];
if(fabs(uObjCoeffs[index]) > etol)
nObjCoeffs[index] = beta* uObjCoeffs[index] * uObjSense;
else
nObjCoeffs[index] = 0.0;
}
/* Add the LL columns of the LL objective multiplied by (1 - beta) */
for(i = 0; i < lCols; i++){
index = lColIndices[i];
if(fabs(lObjCoeffs[i]) > etol)
nObjCoeffs[index] += (1 - beta) * lObjCoeffs[i] * lObjSense;
}
sSolver->setObjective(nObjCoeffs);
//int i(0);
if(0){
for(i = 0; i < sSolver->getNumCols(); i++){
std::cout << "betaobj " << sSolver->getObjCoefficients()[i] << std::endl;
}
}
if(0){
sSolver->writeLp("afterbeta");
//sSolver->writeMps("afterbeta");
}
if(0){
for(i = 0; i < sSolver->getNumCols(); i++){
std::cout << "obj " << sSolver->getObjCoefficients()[i] << std::endl;
std::cout << "upper " << sSolver->getColUpper()[i] << std::endl;
std::cout << "lower " << sSolver->getColLower()[i] << std::endl;
}
}
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(sSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(sSolver)->setSymParam("max_active_nodes", 1);
}
//dynamic_cast<OsiSymSolverInterface *> (sSolver)->branchAndBound();
sSolver->branchAndBound();
if(sSolver->isProvenOptimal()){
if(0){
std::cout << "writing lp file." << std::endl;
sSolver->writeLp("afterbeta");
//sSolver->writeMps("afterbeta");
}
double upperObjVal(0.0);
double lowerObjVal(0.0);
for(i = 0; i < tCols; i++){
upperObjVal +=
sSolver->getColSolution()[i] * oSolver->getObjCoefficients()[i];
if(0){
std::cout << "sSolver->getColSolution()[" << i << "] :"
<< sSolver->getColSolution()[i] << std::endl;
}
}
lowerObjVal = getLowerObj(sSolver->getColSolution(), lObjSense);
if(beta == 1.0){
/*
fix upper-level objective to current value and
reoptimize wrt to lower-level objective
*/
//OsiSolverInterface * nSolver = new OsiCbcSolverInterface();
OsiSolverInterface * nSolver = new OsiSymSolverInterface();
nSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
nSolver->setInteger(j);
}
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
nObjCoeffs[index] = lObjCoeffs[i] * lObjSense;
}
nSolver->setObjective(nObjCoeffs);
CoinPackedVector objCon;
for(i = 0; i < tCols; i++){
objCon.insert(i, uObjCoeffs[i] * uObjSense);
}
nSolver->addRow(objCon, upperObjVal, upperObjVal);
nSolver->writeLp("beta1");
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(nSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("max_active_nodes", 1);
}
nSolver->branchAndBound();
double * colsol = new double[tCols];
if(nSolver->isProvenOptimal()){
lowerObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
else{
//just take the current solution
lowerObjVal = sSolver->getObjValue();
CoinCopyN(sSolver->getColSolution(), tCols, colsol);
}
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
delete nSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
else if(beta == 0.0){
/*
fix lower-level objective to current value and
reoptimize wrt to upper-level objective
*/
//OsiSolverInterface * nSolver = new OsiCbcSolverInterface();
OsiSolverInterface * nSolver = new OsiSymSolverInterface();
nSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
nSolver->setInteger(j);
}
CoinZeroN(nObjCoeffs, tCols);
for(i = 0; i < tCols; i++)
nObjCoeffs[i] = uObjCoeffs[i] * uObjSense;
nSolver->setObjective(nObjCoeffs);
CoinPackedVector objCon;
for(i = 0; i < lCols; i++){
index = lColIndices[i];
objCon.insert(index, lObjCoeffs[i] * lObjSense);
}
nSolver->addRow(objCon, lowerObjVal, lowerObjVal);
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(nSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(nSolver)->setSymParam("max_active_nodes", 1);
}
if(0)
nSolver->writeLp("nSolver");
nSolver->branchAndBound();
double * colsol = new double[tCols];
if(nSolver->isProvenOptimal()){
upperObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
else{
upperObjVal = nSolver->getObjValue();
CoinCopyN(nSolver->getColSolution(), tCols, colsol);
}
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
delete nSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
else{
//no optimality cut needed here. all solutions are supported.
double * colsol = new double[tCols];
CoinCopyN(sSolver->getColSolution(), tCols, colsol);
delete[] nObjCoeffs;
nObjCoeffs = 0;
delete sSolver;
return mcSol(std::make_pair(upperObjVal, lowerObjVal), colsol);
}
}
else{
//FIXME:SHOULD JUST TAKE THIS OUT. DELETE sSolver and remove it from above
nObjCoeffs = 0;
delete[] nObjCoeffs;
delete sSolver;
std::cout << "Subproblem is not proven optimal." << std::endl;
//return NULL;
//abort();
}
}
/** Create a set of candidate branching objects. */
int
BlisBranchStrategyPseudo::createCandBranchObjects(int numPassesLeft,
double ub)
{
int bStatus = 0;
int i, pass, colInd;
int preferDir, saveLimit;
int numFirsts = 0;
int numInfs = 0;
int minCount = 0;
int numLowerTightens = 0;
int numUpperTightens = 0;
double lpX, score, infeasibility, downDeg, upDeg, sumDeg = 0.0;
bool roundAgain, downKeep, downGood, upKeep, upGood;
int *lbInd = NULL;
int *ubInd = NULL;
double *newLB = NULL;
double *newUB = NULL;
double *saveUpper = NULL;
double *saveLower = NULL;
double *saveSolution = NULL;
BlisModel *model = dynamic_cast<BlisModel *>(model_);
OsiSolverInterface *solver = model->solver();
int numCols = model->getNumCols();
int numObjects = model->numObjects();
int aveIterations = model->getAveIterations();
//std::cout << "aveIterations = " << aveIterations << std::endl;
//------------------------------------------------------
// Check if max time is reached or no pass is left.
//------------------------------------------------------
double timeLimit = model->AlpsPar()->entry(AlpsParams::timeLimit);
AlpsKnowledgeBroker *broker = model->getKnowledgeBroker();
bool maxTimeReached = (broker->timer().getTime() > timeLimit);
bool selectNow = false;
if (maxTimeReached || !numPassesLeft) {
selectNow = true;
#ifdef BLIS_DEBUG
printf("PSEUDO: CREATE: maxTimeReached %d, numPassesLeft %d\n",
maxTimeReached, numPassesLeft);
#endif
}
// Store first time objects.
std::vector<BlisObjectInt *> firstObjects;
// Store infeasible objects.
std::vector<BlisObjectInt *> infObjects;
// TODO: check if sorting is expensive.
std::multimap<double, BcpsBranchObject*, BlisPseuoGreater> candObjects;
double objValue = solver->getObjSense() * solver->getObjValue();
const double * lower = solver->getColLower();
const double * upper = solver->getColUpper();
saveSolution = new double[numCols];
memcpy(saveSolution, solver->getColSolution(), numCols*sizeof(double));
//--------------------------------------------------
// Find the infeasible objects.
// NOTE: we might go round this loop twice if we are feed in
// a "feasible" solution.
//--------------------------------------------------
for (pass = 0; pass < 2; ++pass) {
numInfs = 0;
BcpsObject * object = NULL;
BlisObjectInt * intObject = NULL;
infObjects.clear();
firstObjects.clear();
for (i = 0; i < numObjects; ++i) {
object = model->objects(i);
infeasibility = object->infeasibility(model, preferDir);
if (infeasibility) {
++numInfs;
intObject = dynamic_cast<BlisObjectInt *>(object);
if (intObject) {
infObjects.push_back(intObject);
if (!selectNow) {
minCount =
ALPS_MIN(intObject->pseudocost().getDownCount(),
intObject->pseudocost().getUpCount());
if (minCount < 1) {
firstObjects.push_back(intObject);
}
}
#ifdef BLIS_DEBUG
if (intObject->columnIndex() == 40) {
std::cout << "x[40] = " << saveSolution[40]
<< std::endl;
}
#endif
intObject = NULL;
}
else {
// TODO: currently all are integer objects.
#ifdef BLIS_DEBU
assert(0);
#endif
}
}
}
if (numInfs) {
#if 0
std::cout << "PSEUDO: numInfs = " << numInfs
<< std::endl;
#endif
break;
}
else if (pass == 0) {
// The first pass and is IP feasible.
#if 1
std::cout << "ERROR: PSEUDO: given a integer feasible sol, no fraction variable" << std::endl;
assert(0);
#endif
roundAgain = false;
CoinWarmStartBasis * ws =
dynamic_cast<CoinWarmStartBasis*>(solver->getWarmStart());
if (!ws) break;
// Force solution values within bounds
for (i = 0; i < numCols; ++i) {
lpX = saveSolution[i];
if (lpX < lower[i]) {
saveSolution[i] = lower[i];
roundAgain = true;
ws->setStructStatus(i, CoinWarmStartBasis::atLowerBound);
}
else if (lpX > upper[i]) {
saveSolution[i] = upper[i];
roundAgain = true;
ws->setStructStatus(i, CoinWarmStartBasis::atUpperBound);
}
}
if (roundAgain) {
// Need resolve and do the second round selection.
solver->setWarmStart(ws);
delete ws;
// Resolve.
solver->resolve();
if (!solver->isProvenOptimal()) {
// Become infeasible, can do nothing.
bStatus = -2;
goto TERM_CREATE;
}
else {
// Save new lp solution.
memcpy(saveSolution, solver->getColSolution(),
numCols * sizeof(double));
objValue = solver->getObjSense() * solver->getObjValue();
}
}
else {
delete ws;
break;
}
}
} // EOF 2 pass
//--------------------------------------------------
// If we have a set of first time object,
// branch up and down to initialize pseudo-cost.
//--------------------------------------------------
numFirsts = static_cast<int> (firstObjects.size());
//std::cout << "PSEUDO: numFirsts = " << numFirsts << std::endl;
if (numFirsts > 0) {
//std::cout << "PSEUDO: numFirsts = " << numFirsts << std::endl;
//--------------------------------------------------
// Backup solver status and mark hot start.
//--------------------------------------------------
saveLower = new double[numCols];
saveUpper = new double[numCols];
memcpy(saveLower, lower, numCols * sizeof(double));
memcpy(saveUpper, upper, numCols * sizeof(double));
CoinWarmStart * ws = solver->getWarmStart();
solver->getIntParam(OsiMaxNumIterationHotStart, saveLimit);
aveIterations = ALPS_MIN(50, aveIterations);
solver->setIntParam(OsiMaxNumIterationHotStart, aveIterations);
solver->markHotStart();
lbInd = new int [numFirsts];
ubInd = new int [numFirsts];
newLB = new double [numFirsts];
newUB = new double [numFirsts];
for (i = 0; i < numFirsts && bStatus != -2; ++i) {
colInd = firstObjects[i]->columnIndex();
lpX = saveSolution[colInd];
BlisStrongBranch(model, objValue, colInd, lpX,
saveLower, saveUpper,
downKeep, downGood, downDeg,
upKeep, upGood, upDeg);
if(!downKeep && !upKeep) {
// Both branch can be fathomed
bStatus = -2;
}
else if (!downKeep) {
// Down branch can be fathomed.
lbInd[numLowerTightens] = colInd;
newLB[numLowerTightens++] = ceil(lpX);
}
else if (!upKeep) {
// Up branch can be fathomed.
ubInd[numUpperTightens] = colInd;
newUB[numUpperTightens++] = floor(lpX);
}
}
//--------------------------------------------------
// Set new bounds in lp solver for resolving
//--------------------------------------------------
if (bStatus != -2) {
if (numUpperTightens > 0) {
bStatus = -1;
for (i = 0; i < numUpperTightens; ++i) {
solver->setColUpper(ubInd[i], newUB[i]);
}
}
if (numLowerTightens > 0) {
bStatus = -1;
for (i = 0; i < numLowerTightens; ++i) {
solver->setColLower(lbInd[i], newLB[i]);
}
}
}
//--------------------------------------------------
// Unmark hotstart and recover LP solver.
//--------------------------------------------------
solver->unmarkHotStart();
solver->setColSolution(saveSolution);
solver->setIntParam(OsiMaxNumIterationHotStart, saveLimit);
solver->setWarmStart(ws);
delete ws;
}
if (bStatus < 0) {
goto TERM_CREATE;
}
else {
// Create a set of candidate branching objects.
numBranchObjects_ = numInfs;
branchObjects_ = new BcpsBranchObject* [numInfs];
// NOTE: it set model->savedLpSolution.
sumDeg = 0.0;
for (i = 0; i < numInfs; ++i) {
if (infObjects[i]->pseudocost().getUpCost() <
infObjects[i]->pseudocost().getDownCost()) {
preferDir = 1;
}
else {
preferDir = -1;
}
branchObjects_[i] = infObjects[i]->createBranchObject(model,
preferDir);
score = infObjects[i]->pseudocost().getScore();
branchObjects_[i]->setUpScore(score);
sumDeg += score;
#ifdef BLIS_DEBUG_MORE
std::cout << "col[" << infObjects[i]->columnIndex() << "]: score="
<< score << ", dir=" << branchObjects_[i]->getDirection()
<< ", up=" << infObjects[i]->pseudocost().getUpCost()
<< ", down=" << infObjects[i]->pseudocost().getDownCost()
<< std::endl;
#endif
}
model->setSolEstimate(objValue + sumDeg);
}
TERM_CREATE:
//------------------------------------------------------
// Cleanup.
//------------------------------------------------------
delete [] lbInd;
delete [] ubInd;
delete [] newLB;
delete [] newUB;
delete [] saveSolution;
delete [] saveLower;
delete [] saveUpper;
return bStatus;
}
//--------------------------------------------------------------------------
// test the simple rounding cut generators methods.
void
CglSimpleRoundingUnitTest(
const OsiSolverInterface * baseSiP,
const std::string mpsDir )
{
// Test default constructor
{
CglSimpleRounding cg;
}
// Test copy & assignment
{
CglSimpleRounding rhs;
{
CglSimpleRounding cg;
CglSimpleRounding cgC(cg);
rhs=cg;
}
}
// Test gcd and gcdn
{
CglSimpleRounding cg;
int v = cg.gcd(122,356);
assert(v==2);
v=cg.gcd(356,122);
assert(v==2);
v=cg.gcd(54,67);
assert(v==1);
v=cg.gcd(67,54);
assert(v==1);
v=cg.gcd(485,485);
assert(v==485);
v=cg.gcd(17*13,17*23);
assert( v==17);
v=cg.gcd(17*13*5,17*23);
assert( v==17);
v=cg.gcd(17*13*23,17*23);
assert(v==17*23);
int a[4] = {12, 20, 32, 400};
v= cg.gcdv(4,a);
assert(v== 4);
int b[4] = {782, 4692, 51, 2754};
v= cg.gcdv(4,b);
assert(v== 17);
int c[4] = {50, 40, 30, 10};
v= cg.gcdv(4,c);
assert(v== 10);
}
// Test generate cuts method on exmip1.5.mps
{
CglSimpleRounding cg;
OsiSolverInterface * siP = baseSiP->clone();
std::string fn = mpsDir+"exmip1.5.mps";
siP->readMps(fn.c_str(),"");
OsiCuts cuts;
cg.generateCuts(*siP,cuts);
// there should be 3 cuts
int nRowCuts = cuts.sizeRowCuts();
assert(nRowCuts==3);
// get the last "sr"=simple rounding cut that was derived
OsiRowCut srRowCut2 = cuts.rowCut(2);
CoinPackedVector srRowCutPV2 = srRowCut2.row();
// this is what the last cut should look like: i.e. the "solution"
const int solSize = 2;
int solCols[solSize]={2,3};
double solCoefs[solSize]={5.0, 4.0};
OsiRowCut solRowCut;
solRowCut.setRow(solSize,solCols,solCoefs);
solRowCut.setLb(-COIN_DBL_MAX);
solRowCut.setUb(2.0);
// Test for equality between the derived cut and the solution cut
// Note: testing two OsiRowCuts are equal invokes testing two
// CoinPackedVectors are equal which invokes testing two doubles
// are equal. Usually not a good idea to test that two doubles are equal,
// but in this cut the "doubles" represent integer values. Also allow that
// different solvers have different orderings in packed vectors, which may
// not match the ordering defined for solRowCut.
assert(srRowCut2.OsiCut::operator==(solRowCut)) ;
assert(srRowCut2.row().isEquivalent(solRowCut.row())) ;
assert(srRowCut2.lb() == solRowCut.lb()) ;
assert(srRowCut2.ub() == solRowCut.ub()) ;
delete siP;
}
// Test generate cuts method on p0033
{
CglSimpleRounding cg;
OsiSolverInterface * siP = baseSiP->clone();
std::string fn = mpsDir+"p0033";
siP->readMps(fn.c_str(),"mps");
OsiCuts cuts;
cg.generateCuts(*siP,cuts);
// p0033 is the optimal solution to p0033
int objIndices[14] = {
0, 6, 7, 9, 13, 17, 18,
22, 24, 25, 26, 27, 28, 29 };
CoinPackedVector p0033(14,objIndices,1.0);
// test that none of the generated cuts
// chops off the optimal solution
int nRowCuts = cuts.sizeRowCuts();
OsiRowCut rcut;
CoinPackedVector rpv;
int i;
for (i=0; i<nRowCuts; i++){
rcut = cuts.rowCut(i);
rpv = rcut.row();
double p0033Sum = (rpv*p0033).sum();
double rcutub = rcut.ub();
assert (p0033Sum <= rcutub);
}
// test that the cuts improve the
// lp objective function value
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("Final LP min=%f\n\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
}
//#############################################################################
void
MibSBilevel::checkBilevelFeasiblity(bool isRoot)
{
int cutStrategy =
model_->MibSPar_->entry(MibSParams::cutStrategy);
bool warmStartLL =
model_->MibSPar_->entry(MibSParams::warmStartLL);
int maxThreadsLL =
model_->MibSPar_->entry(MibSParams::maxThreadsLL);
int whichCutsLL =
model_->MibSPar_->entry(MibSParams::whichCutsLL);
int probType =
model_->MibSPar_->entry(MibSParams::bilevelProblemType);
std::string feasCheckSolver =
model_->MibSPar_->entry(MibSParams::feasCheckSolver);
if (warmStartLL && (feasCheckSolver == "SYMPHONY") && solver_){
solver_ = setUpModel(model_->getSolver(), false);
}else{
if (solver_){
delete solver_;
}
solver_ = setUpModel(model_->getSolver(), true);
}
OsiSolverInterface *lSolver = solver_;
//CoinWarmStart * ws = getWarmStart();
//if (ws != NULL){
// lSolver->setWarmStart(ws);
//}
//delete ws;
if(1)
lSolver->writeLp("lowerlevel");
if (feasCheckSolver == "Cbc"){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}else if (feasCheckSolver == "SYMPHONY"){
//dynamic_cast<OsiSymSolverInterface *>
// (lSolver)->setSymParam("prep_level", -1);
sym_environment *env = dynamic_cast<OsiSymSolverInterface *>
(lSolver)->getSymphonyEnvironment();
if (warmStartLL){
sym_set_int_param(env, "keep_warm_start", TRUE);
if (probType == 1){ //Interdiction
sym_set_int_param(env, "should_use_rel_br", FALSE);
sym_set_int_param(env, "use_hot_starts", FALSE);
sym_set_int_param(env, "should_warmstart_node", TRUE);
sym_set_int_param(env, "sensitivity_analysis", TRUE);
sym_set_int_param(env, "sensitivity_bounds", TRUE);
sym_set_int_param(env, "set_obj_upper_lim", FALSE);
}
}
//Always uncomment for debugging!!
sym_set_int_param(env, "do_primal_heuristic", FALSE);
sym_set_int_param(env, "verbosity", -2);
sym_set_int_param(env, "prep_level", -1);
sym_set_int_param(env, "max_active_nodes", maxThreadsLL);
sym_set_int_param(env, "tighten_root_bounds", FALSE);
sym_set_int_param(env, "max_sp_size", 100);
sym_set_int_param(env, "do_reduced_cost_fixing", FALSE);
if (whichCutsLL == 0){
sym_set_int_param(env, "generate_cgl_cuts", FALSE);
}else{
sym_set_int_param(env, "generate_cgl_gomory_cuts", GENERATE_DEFAULT);
}
if (whichCutsLL == 1){
sym_set_int_param(env, "generate_cgl_knapsack_cuts",
DO_NOT_GENERATE);
sym_set_int_param(env, "generate_cgl_probing_cuts",
DO_NOT_GENERATE);
sym_set_int_param(env, "generate_cgl_clique_cuts",
DO_NOT_GENERATE);
sym_set_int_param(env, "generate_cgl_twomir_cuts",
DO_NOT_GENERATE);
sym_set_int_param(env, "generate_cgl_flowcover_cuts",
DO_NOT_GENERATE);
}
}else if (feasCheckSolver == "CPLEX"){
#ifdef USE_CPLEX
lSolver->setHintParam(OsiDoReducePrint);
lSolver->messageHandler()->setLogLevel(0);
CPXENVptr cpxEnv =
dynamic_cast<OsiCpxSolverInterface*>(lSolver)->getEnvironmentPtr();
assert(cpxEnv);
CPXsetintparam(cpxEnv, CPX_PARAM_SCRIND, CPX_OFF);
CPXsetintparam(cpxEnv, CPX_PARAM_THREADS, maxThreadsLL);
#endif
}
if (warmStartLL && feasCheckSolver == "SYMPHONY"){
lSolver->resolve();
setWarmStart(lSolver->getWarmStart());
}else{
lSolver->branchAndBound();
}
const double * sol = model_->solver()->getColSolution();
double objVal(lSolver->getObjValue() * model_->getLowerObjSense());
MibSTreeNode * node = static_cast<MibSTreeNode *>(model_->activeNode_);
MibSTreeNode * parent =
static_cast<MibSTreeNode *>(model_->activeNode_->getParent());
if((!node->isBoundSet())
&& (node->getIndex() != 0)){
double parentBound = parent->getLowerUB();
node->setLowerUB(parentBound);
node->setIsBoundSet(true);
}
if(objVal > node->getLowerUB()){
node->setLowerUB(objVal);
node->setIsBoundSet(true);
}
double etol(model_->etol_);
double lowerObj = getLowerObj(sol, model_->getLowerObjSense());
int lN(model_->lowerDim_); // lower-level dimension
int uN(model_->upperDim_); // lower-level dimension
if(!optLowerSolution_)
optLowerSolution_ = new double[lN];
if(!optLowerSolutionOrd_)
optLowerSolutionOrd_ = new double[lN];
CoinZeroN(optLowerSolution_, lN);
CoinZeroN(optLowerSolutionOrd_, lN);
int * lowerColInd = model_->getLowerColInd();
int * upperColInd = model_->getUpperColInd();
int index(0);
if(0){
std::cout << "objVal: " << objVal << std::endl;
std::cout << "lowerObj: " << lowerObj << std::endl;
}
if(fabs(objVal - lowerObj) < etol){
/** Current solution is bilevel feasible **/
const double * values = lSolver->getColSolution();
int lN(model_->getLowerDim());
int i(0);
// May want to take out this update and keep current - both optimal
// changed this 7/1 to allow for continuous vars
/*
for(i = 0; i < lN; i++){
lowerSolution_[i] = (double) floor(values[i] + 0.5);
}
*/
for(i = 0; i < lN; i++){
if(lSolver->isInteger(i))
lowerSolution_[i] = (double) floor(values[i] + 0.5);
else
lowerSolution_[i] = (double) values[i];
}
isBilevelFeasible_ = true;
useBilevelBranching_ = false;
}else if (lSolver->isProvenOptimal()){
/** Current solution is not bilevel feasible,
but we may still have a solution **/
//std::cout << "Solution is not bilevel feasible." << std::endl;
const double * values = lSolver->getColSolution();
int lN(model_->getLowerDim());
int i(0);
//added this 7/1 to store y* for val func cut
for(i = 0; i < lN; i++){
if(lSolver->isInteger(i))
optLowerSolution_[i] = (double) floor(values[i] + 0.5);
else
optLowerSolution_[i] = (double) values[i];
}
int numCols = model_->solver()->getNumCols();
int pos(0);
#if 1
for(i = 0; i < numCols; i++){
if ((pos = model_->bS_->binarySearch(0, lN - 1, i, lowerColInd)) >= 0){
optLowerSolutionOrd_[pos] = optLowerSolution_[pos];
}
}
#else
double upperObj(0);
double * newSolution = new double[numCols];
const double * upperObjCoeffs = model_->solver()->getObjCoefficients();
for(i = 0; i < numCols; i++){
pos = model_->bS_->binarySearch(0, lN - 1, i, lowerColInd);
if(pos < 0){
pos = model_->bS_->binarySearch(0, uN - 1, i, upperColInd);
newSolution[i] = sol[i];
}
else{
newSolution[i] = optLowerSolution_[pos];
optLowerSolutionOrd_[pos] = optLowerSolution_[pos];
}
upperObj += newSolution[i] * upperObjCoeffs[i];
}
if(model_->checkUpperFeasibility(newSolution)){
MibSSolution *mibsSol = new MibSSolution(numCols, newSolution,
upperObj,
model_);
model_->storeSolution(BlisSolutionTypeHeuristic, mibsSol);
}
delete [] newSolution;
#endif
/* run a heuristic to find a better feasible solution */
heuristic_->findHeuristicSolutions();
isBilevelFeasible_ = false;
if(cutStrategy != 1)
useBilevelBranching_ = true;
}
//delete lSolver;
}
//--------------------------------------------------------------------------
// ** At present this does not use any solver
void
CglGomoryUnitTest(
const OsiSolverInterface * baseSiP,
const std::string mpsDir )
{
CoinRelFltEq eq(0.000001);
// Test default constructor
{
CglGomory aGenerator;
assert (aGenerator.getLimit()==50);
assert (aGenerator.getAway()==0.05);
}
// Test copy & assignment etc
{
CglGomory rhs;
{
CglGomory bGenerator;
bGenerator.setLimit(99);
bGenerator.setAway(0.2);
CglGomory cGenerator(bGenerator);
rhs=bGenerator;
assert (rhs.getLimit()==99);
assert (rhs.getAway()==0.2);
}
}
// Test explicit form - all integer (pg 125 Wolsey)
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4,7,8,9};
int length[5]={2,3,1,1,1};
int rows[11]={0,2,-1,-1,0,1,2,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0,1,1,1};
CoinPackedMatrix matrix(true,3,5,8,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={14.0,3.0,3.0,1.0e10,1.0e10};
double rowUpper[5]={14.0,3.0,3.0,-1.0e10,-1.0e10};
double colLower[7]={0.0,0.0,0.0,0.0,0.0,0.0,0.0};
double colUpper[7]={100.0,100.0,100.0,100.0,100.0,100.0,100.0};
// integer
char intVar[7]={2,2,2,2,2,2,2};
// basis 1
int rowBasis1[3]={-1,-1,-1};
int colBasis1[5]={1,1,-1,-1,1};
CoinWarmStartBasis warm;
warm.setSize(5,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<5;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[5]={20.0/7.0,3.0,0.0,0.0,23.0/7.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==2);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=-6.0;
double testCut1[5]={0.0,0.0,-1.0,-2.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,6);
rpv.insert(5,1.0*7.0); // to get cut in book
rowLower[3]=ub;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={-1,-1,-1,-1};
int colBasis2[6]={1,1,1,1,-1,-1};
warm.setSize(6,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<6;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[6]={2.0,0.5,1.0,2.5,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts-nOldCuts==2);
// cuts always <=
testCut=0; // test first cut as stronger
rhs=-1.0;
double testCut2[6]={0.0,0.0,0.0,0.0,-1.0,0.0};
cut = testCut2;
colsol = colsol2;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,7);
rpv.insert(6,1.0);
rowLower[4]=ub;
rowUpper[4]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 3
int rowBasis3[5]={-1,-1,-1,-1,-1};
int colBasis3[7]={1,1,1,1,1,-1,-1};
warm.setSize(7,5);
for (i=0;i<5;i++) {
if (rowBasis3[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<7;i++) {
if (colBasis3[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 3
double colsol3[7]={2.0,1.0,2.0,2.0,1.0,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol3,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// Test explicit form - this time with x4 flipped
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4,7,8,9};
int length[5]={2,3,1,1,1};
int rows[11]={0,2,-1,-1,0,1,2,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0,1,-1,1};
CoinPackedMatrix matrix(true,3,5,8,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={14.0,-5.0,3.0,1.0e10,1.0e10};
double rowUpper[5]={14.0,-5.0,3.0,-1.0e10,-1.0e10};
double colLower[7]={0.0,0.0,0.0,0.0,0.0,0.0,0.0};
double colUpper[7]={100.0,100.0,100.0,8.0,100.0,100.0,100.0};
// integer
char intVar[7]={2,2,2,2,2,2,2};
// basis 1
int rowBasis1[3]={-1,-1,-1};
int colBasis1[5]={1,1,-1,-1,1};
CoinWarmStartBasis warm;
warm.setSize(5,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<5;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[5]={20.0/7.0,3.0,0.0,8.0,23.0/7.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==2);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=10.0;
double testCut1[5]={0.0,0.0,-1.0,2.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,6);
rpv.insert(5,1.0*7.0); // to get cut in book
rowLower[3]=ub;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={-1,-1,-1,-1};
int colBasis2[6]={1,1,1,1,-1,-1};
warm.setSize(6,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<6;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[6]={2.0,0.5,1.0,5.5,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts-nOldCuts==2);
// cuts always <=
testCut=0; // test first cut as stronger
rhs=-1.0;
double testCut2[6]={0.0,0.0,0.0,0.0,-1.0,0.0};
cut = testCut2;
colsol = colsol2;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,7);
rpv.insert(6,1.0);
rowLower[4]=ub;
rowUpper[4]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 3
int rowBasis3[5]={-1,-1,-1,-1,-1};
int colBasis3[7]={1,1,1,1,1,-1,-1};
warm.setSize(7,5);
for (i=0;i<5;i++) {
if (rowBasis3[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<7;i++) {
if (colBasis3[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 3
double colsol3[7]={2.0,1.0,2.0,6.0,1.0,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol3,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// Test with slacks
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4};
int length[5]={2,3};
int rows[11]={0,2,-1,-1,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0};
CoinPackedMatrix matrix(true,3,2,5,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={-1.0e10,-1.0e10,-1.0e10,1.0e10,1.0e10};
double rowUpper[5]={14.0,3.0,3.0,-1.0e10,-1.0e10};
double colLower[2]={0.0,0.0};
double colUpper[2]={100.0,100.0};
// integer
char intVar[2]={2,2};
// basis 1
int rowBasis1[3]={-1,-1,1};
int colBasis1[2]={1,1};
CoinWarmStartBasis warm;
warm.setSize(2,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[2]={20.0/7.0,3.0};
test1.generateCuts(NULL, osicuts, matrix,
/* objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=2.0;
double testCut1[2]={1.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[3]=-1.0e100;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={1,1,-1,-1};
int colBasis2[2]={1,1};
warm.setSize(2,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[2]={2.0,0.5};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts-nOldCuts==1);
// cuts always <=
testCut=0; // test first cut as stronger
rhs=1.0;
double testCut2[2]={1.0,-1.0};
cut = testCut2;
colsol = colsol2;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[4]=-1.0e100;
rowUpper[4]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 3
int rowBasis3[5]={1,1,1,-1,-1};
int colBasis3[2]={1,1};
warm.setSize(2,5);
for (i=0;i<5;i++) {
if (rowBasis3[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis3[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 3
double colsol3[2]={2.0,1.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol3,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// swap some rows to G
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4};
int length[5]={2,3};
int rows[11]={0,2,-1,-1,0,1,2};
double elements[11]={-7.0,-2.0,1.0e10,1.0e10,+2.0,1.0,+2.0};
CoinPackedMatrix matrix(true,3,2,5,elements,rows,start,length);
// rim data (objective not used just yet)
double rowUpper[5]={1.0e10,3.0,1.0e10,-1.0e10,-1.0e10};
double rowLower[5]={-14.0,-1.0e10,-3.0,1.0e10,1.0e10};
double colLower[2]={0.0,0.0};
double colUpper[2]={100.0,100.0};
// integer
char intVar[2]={2,2};
// basis 1
int rowBasis1[3]={-1,-1,1};
int colBasis1[2]={1,1};
CoinWarmStartBasis warm;
warm.setSize(2,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[2]={20.0/7.0,3.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=2.0;
double testCut1[2]={1.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[3]=-1.0e100;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={1,1,-1,-1};
int colBasis2[2]={1,1};
warm.setSize(2,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[2]={2.0,0.5};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts-nOldCuts==1);
// cuts always <=
testCut=0; // test first cut as stronger
rhs=1.0;
double testCut2[2]={1.0,-1.0};
cut = testCut2;
colsol = colsol2;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[4]=-1.0e100;
rowUpper[4]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 3
int rowBasis3[5]={1,1,1,-1,-1};
int colBasis3[2]={1,1};
warm.setSize(2,5);
for (i=0;i<5;i++) {
if (rowBasis3[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis3[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 3
double colsol3[2]={2.0,1.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol3,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// NOW mixed integer gomory cuts
// Test explicit form - (pg 130 Wolsey)
// Some arrays left same size as previously although not used in full
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4,7,8,9};
int length[5]={2,3,1,1,1};
int rows[11]={0,2,-1,-1,0,1,2,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0,1,1,1};
CoinPackedMatrix matrix(true,3,5,8,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={14.0,3.0,3.0,1.0e10,1.0e10};
double rowUpper[5]={14.0,3.0,3.0,-1.0e10,-1.0e10};
double colLower[7]={0.0,0.0,0.0,0.0,0.0,0.0,0.0};
double colUpper[7]={100.0,100.0,100.0,100.0,100.0,100.0,100.0};
// integer
char intVar[7]={2,0,0,0,0,0,0};
// basis 1
int rowBasis1[3]={-1,-1,-1};
int colBasis1[5]={1,1,-1,-1,1};
CoinWarmStartBasis warm;
warm.setSize(5,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<5;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[5]={20.0/7.0,3.0,0.0,0.0,23.0/7.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=-6.0/7.0;
double testCut1[5]={0.0,0.0,-1.0/7.0,-2.0/7.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,6);
rpv.insert(5,1.0); // to get cut in book
rowLower[3]=ub;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={-1,-1,-1,-1};
int colBasis2[6]={1,1,1,1,-1,-1};
warm.setSize(6,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<6;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[6]={2.0,0.5,1.0,2.5,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// Test explicit form - this time with x4 flipped
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4,7,8,9};
int length[5]={2,3,1,1,1};
int rows[11]={0,2,-1,-1,0,1,2,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0,1,-1,1};
CoinPackedMatrix matrix(true,3,5,8,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={14.0,-5.0,3.0,1.0e10,1.0e10};
double rowUpper[5]={14.0,-5.0,3.0,-1.0e10,-1.0e10};
double colLower[7]={0.0,0.0,0.0,0.0,0.0,0.0,0.0};
double colUpper[7]={100.0,100.0,100.0,8.0,100.0,100.0,100.0};
// integer
char intVar[7]={2,0,0,0,0,0,0};
// basis 1
int rowBasis1[3]={-1,-1,-1};
int colBasis1[5]={1,1,-1,-1,1};
CoinWarmStartBasis warm;
warm.setSize(5,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<5;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[5]={20.0/7.0,3.0,0.0,8.0,23.0/7.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0;
double rhs=10.0/7.0;
double testCut1[5]={0.0,0.0,-1.0/7.0,2.0/7.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==2);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
// explicit slack
matrix.setDimensions(-1,6);
rpv.insert(5,1.0); // to get cut in book
rowLower[3]=ub;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={-1,-1,-1,-1};
int colBasis2[6]={1,1,1,1,-1,-1};
warm.setSize(6,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<6;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[6]={2.0,0.5,1.0,5.5,0.0,0.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// Test with slacks
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4};
int length[5]={2,3};
int rows[11]={0,2,-1,-1,0,1,2};
double elements[11]={7.0,2.0,1.0e10,1.0e10,-2.0,1.0,-2.0};
CoinPackedMatrix matrix(true,3,2,5,elements,rows,start,length);
// rim data (objective not used just yet)
double rowLower[5]={-1.0e10,-1.0e10,-1.0e10,1.0e10,1.0e10};
double rowUpper[5]={14.0,3.0,3.0,-1.0e10,-1.0e10};
double colLower[2]={0.0,0.0};
double colUpper[2]={100.0,100.0};
// integer
char intVar[2]={2,0};
// basis 1
int rowBasis1[3]={-1,-1,1};
int colBasis1[2]={1,1};
CoinWarmStartBasis warm;
warm.setSize(2,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[2]={20.0/7.0,3.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=2.0;
double testCut1[2]={1.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[3]=-1.0e100;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={1,1,-1,-1};
int colBasis2[2]={1,1};
warm.setSize(2,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[2]={2.0,0.5};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// swap some rows to G
if (1) {
OsiCuts osicuts;
CglGomory test1;
int i;
int nOldCuts=0,nRowCuts;
// matrix data
//deliberate hiccup of 2 between 0 and 1
CoinBigIndex start[5]={0,4};
int length[5]={2,3};
int rows[11]={0,2,-1,-1,0,1,2};
double elements[11]={-7.0,-2.0,1.0e10,1.0e10,+2.0,1.0,+2.0};
CoinPackedMatrix matrix(true,3,2,5,elements,rows,start,length);
// rim data (objective not used just yet)
double rowUpper[5]={1.0e10,3.0,1.0e10,-1.0e10,-1.0e10};
double rowLower[5]={-14.0,-1.0e10,-3.0,1.0e10,1.0e10};
double colLower[2]={0.0,0.0};
double colUpper[2]={100.0,100.0};
// integer
char intVar[2]={2,0};
// basis 1
int rowBasis1[3]={-1,-1,1};
int colBasis1[2]={1,1};
CoinWarmStartBasis warm;
warm.setSize(2,3);
for (i=0;i<3;i++) {
if (rowBasis1[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis1[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 1
double colsol1[2]={20.0/7.0,3.0};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol1,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==1);
// cuts always <=
int testCut=0; // test first cut as stronger
double rhs=2.0;
double testCut1[2]={1.0,0.0};
double * cut = testCut1;
double * colsol = colsol1;
for (i=nOldCuts; i<nRowCuts; i++){
OsiRowCut rcut;
CoinPackedVector rpv;
rcut = osicuts.rowCut(i);
rpv = rcut.row();
const int n = rpv.getNumElements();
const int * indices = rpv.getIndices();
double* elements = rpv.getElements();
double sum2=0.0;
int k=0;
for (k=0; k<n; k++){
int column=indices[k];
sum2 += colsol[column]*elements[k];
}
double ub=rcut.ub();
#ifdef CGL_DEBUG
double lb=rcut.lb();
if (sum2 >ub + 1.0e-7 ||sum2 < lb - 1.0e-7) {
std::cout<<"Cut "<<i<<" lb "<<lb<<" solution "<<sum2<<" ub "<<ub<<std::endl;
for (k=0; k<n; k++){
int column=indices[k];
std::cout<<"(col="<<column<<",el="<<elements[k]<<",sol="<<
colsol[column]<<") ";
}
std::cout <<std::endl;
}
#endif
if (i-nOldCuts==testCut) {
assert( eq(rhs,ub));
assert(n==1);
for (k=0; k<n; k++){
int column=indices[k];
assert (eq(cut[column],elements[k]));
}
// add cut
rowLower[3]=-1.0e100;
rowUpper[3]=ub;
matrix.appendRow(rpv);
}
}
nOldCuts=nRowCuts;
// basis 2
int rowBasis2[4]={1,1,-1,-1};
int colBasis2[2]={1,1};
warm.setSize(2,4);
for (i=0;i<4;i++) {
if (rowBasis2[i]<0) {
warm.setArtifStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setArtifStatus(i,CoinWarmStartBasis::basic);
}
}
for (i=0;i<2;i++) {
if (colBasis2[i]<0) {
warm.setStructStatus(i,CoinWarmStartBasis::atLowerBound);
} else {
warm.setStructStatus(i,CoinWarmStartBasis::basic);
}
}
// solution 2
double colsol2[2]={2.0,0.5};
test1.generateCuts(NULL, osicuts, matrix,
/*objective,*/ colsol2,
colLower, colUpper,
rowLower, rowUpper, intVar, &warm);
nRowCuts = osicuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" gomory cuts"<<std::endl;
assert (nRowCuts==nOldCuts);
}
// Miplib3 problem p0033
if (1) {
// Setup
OsiSolverInterface * siP = baseSiP->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglGomory test;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelaxBefore<<std::endl;
assert( eq(lpRelaxBefore, 2520.5717391304347) );
// Fails with OsiCpx, OsiXpr:
/**********
double mycs[] = {0, 1, 0, 0, -2.0837010502455788e-19, 1, 0, 0, 1,
0.021739130434782594, 0.35652173913043478,
-6.7220534694101275e-18, 5.3125906451789717e-18,
1, 0, 1.9298798670241979e-17, 0, 0, 0,
7.8875708048320448e-18, 0.5, 0,
0.85999999999999999, 1, 1, 0.57999999999999996,
1, 0, 1, 0, 0.25, 0, 0.67500000000000004};
siP->setColSolution(mycs);
****/
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
int nRowCuts = cuts.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" Gomory cuts"<<std::endl;
assert(cuts.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
assert( lpRelaxAfter> 2550.0 );
assert( lpRelaxBefore < lpRelaxAfter );
assert(lpRelaxAfter < 3089.1);
delete siP;
}
}
void
CglRedSplitUnitTest(const OsiSolverInterface *baseSiP,
const std::string mpsDir)
{
// Test default constructor
{
CglRedSplit aGenerator;
}
// Test copy & assignment
{
CglRedSplit rhs;
{
CglRedSplit bGenerator;
CglRedSplit cGenerator(bGenerator);
rhs=bGenerator;
}
}
// Test get/set methods
{
CglRedSplit getset;
CglRedSplitParam gsparam = getset.getParam();
double geps = 10 * gsparam.getEPS();
gsparam.setEPS(geps);
double geps2 = gsparam.getEPS();
assert(geps == geps2);
double gepse = 10 * gsparam.getEPS_ELIM();
gsparam.setEPS_ELIM(gepse);
double gepse2 = gsparam.getEPS_ELIM();
assert(gepse == gepse2);
double gmv = 10 * gsparam.getMINVIOL();
gsparam.setMINVIOL(gmv);
double gmv2 = gsparam.getMINVIOL();
assert(gmv == gmv2);
int gucg = gsparam.getUSE_CG2();
gucg = 1 - gucg;
gsparam.setUSE_CG2(gucg);
int gucg2 = gsparam.getUSE_CG2();
assert(gucg == gucg2);
}
// Test generateCuts
{
CglRedSplit gct;
OsiSolverInterface *siP = baseSiP->clone();
std::string fn = mpsDir+"p0033";
std::string fn2 = mpsDir+"p0033.mps";
FILE *in_f = fopen(fn2.c_str(), "r");
if(in_f == NULL) {
std::cout<<"Can not open file "<<fn2<<std::endl<<"Skip test of CglRedSplit::generateCuts()"<<std::endl;
}
else {
fclose(in_f);
siP->readMps(fn.c_str(),"mps");
siP->initialSolve();
double lpRelax = siP->getObjValue();
OsiCuts cs;
gct.getParam().setMAX_SUPPORT(34);
gct.getParam().setUSE_CG2(1);
// gct.getParam().setUSE_CG2(1);
gct.generateCuts(*siP, cs);
int nRowCuts = cs.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" Reduce-and-Split cuts"<<std::endl;
assert(cs.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cs);
siP->resolve();
double lpRelaxAfter= siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelax<<std::endl;
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
assert( lpRelax < lpRelaxAfter );
assert(lpRelaxAfter < 3089.1);
}
delete siP;
}
}
void
CglLandPUnitTest(
OsiSolverInterface * si,
const std::string &mpsDir)
{
CoinRelFltEq eq(1e-05);
// Test default constructor
{
CglLandP aGenerator;
assert(aGenerator.parameter().pivotLimit==20);
assert(aGenerator.parameter().maxCutPerRound==5000);
assert(aGenerator.parameter().failedPivotLimit==1);
assert(aGenerator.parameter().degeneratePivotLimit==0);
assert(eq(aGenerator.parameter().pivotTol, 1e-04));
assert(eq(aGenerator.parameter().away, 5e-04));
assert(eq(aGenerator.parameter().timeLimit, COIN_DBL_MAX));
assert(eq(aGenerator.parameter().singleCutTimeLimit, COIN_DBL_MAX));
assert(aGenerator.parameter().useTableauRow==true);
assert(aGenerator.parameter().modularize==false);
assert(aGenerator.parameter().strengthen==true);
assert(aGenerator.parameter().perturb==true);
assert(aGenerator.parameter().pivotSelection==CglLandP::mostNegativeRc);
}
// Test copy constructor
{
CglLandP a;
{
CglLandP b;
b.parameter().pivotLimit = 100;
b.parameter().maxCutPerRound = 100;
b.parameter().failedPivotLimit = 10;
b.parameter().degeneratePivotLimit = 10;
b.parameter().pivotTol = 1e-07;
b.parameter().away = 1e-10;
b.parameter().timeLimit = 120;
b.parameter().singleCutTimeLimit = 15;
b.parameter().useTableauRow = true;
b.parameter().modularize = true;
b.parameter().strengthen = false;
b.parameter().perturb = false;
b.parameter().pivotSelection=CglLandP::bestPivot;
//Test Copy
CglLandP c(b);
assert(c.parameter().pivotLimit == 100);
assert(c.parameter().maxCutPerRound == 100);
assert(c.parameter().failedPivotLimit == 10);
assert(c.parameter().degeneratePivotLimit == 10);
assert(c.parameter().pivotTol == 1e-07);
assert(c.parameter().away == 1e-10);
assert(c.parameter().timeLimit == 120);
assert(c.parameter().singleCutTimeLimit == 15);
assert(c.parameter().useTableauRow == true);
assert(c.parameter().modularize == true);
assert(c.parameter().strengthen == false);
assert(c.parameter().perturb == false);
assert(c.parameter().pivotSelection == CglLandP::bestPivot);
a=b;
assert(a.parameter().pivotLimit == 100);
assert(a.parameter().maxCutPerRound == 100);
assert(a.parameter().failedPivotLimit == 10);
assert(a.parameter().degeneratePivotLimit == 10);
assert(a.parameter().pivotTol == 1e-07);
assert(a.parameter().away == 1e-10);
assert(a.parameter().timeLimit == 120);
assert(a.parameter().singleCutTimeLimit == 15);
assert(a.parameter().useTableauRow == true);
assert(a.parameter().modularize == true);
assert(a.parameter().strengthen == false);
assert(a.parameter().perturb == false);
assert(a.parameter().pivotSelection == CglLandP::bestPivot);
}
}
{
// Maximize 2 x2
// s.t.
// 2x1 + 2x2 <= 3
// -2x1 + 2x2 <= 1
// 7x1 + 4x2 <= 8
// -7x1 + 4x2 <= 1
// x1, x2 >= 0 and x1, x2 integer
// Slacks are s1, s2, s3, s4
//Test that problem is correct
// Optimal Basis is x1, x2, s3, s4 with tableau
// x1 0.25 s1 -0.25 s2 = 0.5
// x2 0.25 s1 0.25 s2 = 1
// -2.75 s1 0.75 s2 s3 = 0.5
// 0.75 s1 -2.75 s2 s4 = 0.5
// z= -0.25 s1 -0.25 s2 = -1
// Gomory cut from variable x1 is x2 <= 0.5
// Can be improved by first pivoting s2 in and s4 out, then s1 in and s3 out
// to x2 <= 0.25
{
int start[2] = {0,4};
int length[2] = {4,4};
int rows[8] = {0,1,2,3,0,1,2,3};
double elements[8] = {2.0,-2.0,7.0,-7.0,2.0,2.0,4.0,4.0};
CoinPackedMatrix columnCopy(true,4,2,8,elements,rows,start,length);
double rowLower[4]={-COIN_DBL_MAX,-COIN_DBL_MAX,
-COIN_DBL_MAX,-COIN_DBL_MAX};
double rowUpper[4]={3.,1.,8.,1.};
double colLower[2]={0.0,0.0};
double colUpper[2]={1.0,1.0};
double obj[2]={-1,-1};
int intVar[2]={0,1};
OsiSolverInterface * siP = si->clone();
siP->loadProblem(columnCopy, colLower, colUpper, obj, rowLower, rowUpper);
siP->setInteger(intVar,2);
CglLandP test;
test.setLogLevel(2);
test.parameter().sepSpace = CglLandP::Full;
siP->resolve();
// Test generateCuts method
{
OsiCuts cuts;
test.generateCuts(*siP,cuts);
cuts.printCuts();
assert(cuts.sizeRowCuts()==1);
OsiRowCut aCut = cuts.rowCut(0);
assert(eq(aCut.lb(), -.0714286));
CoinPackedVector row = aCut.row();
if (row.getNumElements() == 1)
{
assert(row.getIndices()[0]==1);
assert(eq(row.getElements()[0], -4*.0714286));
}
else if (row.getNumElements() == 2)
{
assert(row.getIndices()[0]==0);
assert(eq(row.getElements()[0], 0.));
assert(row.getIndices()[1]==1);
assert(eq(row.getElements()[1], -1));
}
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
}
if (0)
{
OsiCuts cuts;
test.generateCuts(*siP,cuts);
cuts.printCuts();
assert(cuts.sizeRowCuts()==1);
OsiRowCut aCut = cuts.rowCut(0);
CoinPackedVector row = aCut.row();
if (row.getNumElements() == 1)
{
assert(row.getIndices()[0]==1);
assert(eq(row.getElements()[0], -1));
}
else if (row.getNumElements() == 2)
{
assert(row.getIndices()[0]==0);
assert(eq(row.getElements()[0], 0.));
assert(row.getIndices()[1]==1);
assert(eq(row.getElements()[1], -1));
}
assert(eq(aCut.lb(), 0.));
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
}
delete siP;
}
}
if (1) //Test on p0033
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //test again with modularization
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
test.parameter().modularize = true;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //test again with alternate pivoting rule
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP test;
test.parameter().pivotSelection = CglLandP::bestPivot;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
test.generateCuts(*siP,cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
if (1) //Finally test code in documentation
{
// Setup
OsiSolverInterface * siP = si->clone();
std::string fn(mpsDir+"p0033");
siP->readMps(fn.c_str(),"mps");
siP->activateRowCutDebugger("p0033");
CglLandP landpGen;
landpGen.parameter().timeLimit = 10.;
landpGen.parameter().pivotLimit = 2;
// Solve the LP relaxation of the model and
// print out ofv for sake of comparison
siP->initialSolve();
double lpRelaxBefore=siP->getObjValue();
assert( eq(lpRelaxBefore, 2520.5717391304347) );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
#endif
OsiCuts cuts;
// Test generateCuts method
landpGen.generateCuts(*siP, cuts);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cuts);
siP->resolve();
double lpRelaxAfter=siP->getObjValue();
//assert( eq(lpRelaxAfter, 2592.1908295194507) );
std::cout<<"Relaxation after "<<lpRelaxAfter<<std::endl;
assert( lpRelaxAfter> 2840. );
#ifdef CGL_DEBUG
printf("\n\nOrig LP min=%f\n",lpRelaxBefore);
printf("\n\nFinal LP min=%f\n",lpRelaxAfter);
#endif
assert( lpRelaxBefore < lpRelaxAfter );
delete siP;
}
}
//Solver function
int sci_rmps(char *fname)
{
//creating a problem pointer using base class of OsiSolverInterface and
//instantiate the object using derived class of ClpSolverInterface
OsiSolverInterface* si = new OsiClpSolverInterface();
// Error management variable
SciErr sciErr;
//data declarations
int *piAddressVarOne = NULL; //pointer used to access argument of the function
char* ptr; //pointer to point to address of file name
double* options_; //options to set maximum iterations
CheckInputArgument(pvApiCtx, 2,2 ); //Check we have exactly two arguments as input or not
CheckOutputArgument(pvApiCtx, 6, 6); //Check we have exactly six arguments on output side or not
//Getting the input arguments from Scilab
//Getting the MPS file path
//Reading mps file
getStringFromScilab(1,&ptr);
std::cout<<ptr;
//get options from Scilab
if(getFixedSizeDoubleMatrixInList(2 , 2 , 1 , 1 , &options_))
{
return 1;
}
//Read the MPS file
si->readMps(ptr);
//setting options for maximum iterations
si->setIntParam(OsiMaxNumIteration,options_[0]);
//Solve the problem
si->initialSolve();
//Quering about the problem
//get number of variables
double numVars_;
numVars_ = si->getNumCols();
//get number of constraint equations
double numCons_;
numCons_ = si->getNumRows();
//Output the solution to Scilab
//get solution for x
const double* xValue = si->getColSolution();
//get objective value
double objValue = si->getObjValue();
//get Status value
double status;
if(si->isProvenOptimal())
status=0;
else if(si->isProvenPrimalInfeasible())
status=1;
else if(si->isProvenDualInfeasible())
status=2;
else if(si->isIterationLimitReached())
status=3;
else if(si->isAbandoned())
status=4;
else if(si->isPrimalObjectiveLimitReached())
status=5;
else if(si->isDualObjectiveLimitReached())
status=6;
//get number of iterations
double iterations = si->getIterationCount();
//get reduced cost
const double* reducedCost = si->getReducedCost();
//get dual vector
const double* dual = si->getRowPrice();
returnDoubleMatrixToScilab(1 , 1 , numVars_ , xValue);
returnDoubleMatrixToScilab(2 , 1 , 1 , &objValue);
returnDoubleMatrixToScilab(3 , 1 , 1 , &status);
returnDoubleMatrixToScilab(4 , 1 , 1 , &iterations);
returnDoubleMatrixToScilab(5 , 1 , numVars_ , reducedCost);
returnDoubleMatrixToScilab(6 , 1 , numCons_ , dual);
free(xValue);
free(dual);
free(reducedCost);
}
BlisReturnStatus
BlisStrongBranch(BlisModel *model, double objValue, int colInd, double x,
const double *saveLower, const double *saveUpper,
bool &downKeep, bool &downFinished, double &downDeg,
bool &upKeep, bool &upFinished, double &upDeg)
{
BlisReturnStatus status = BlisReturnStatusOk;
int lpStatus = 0;
int j, numIntInfDown, numObjInfDown;
double newObjValue;
OsiSolverInterface * solver = model->solver();
int numCols = solver->getNumCols();
const double * lower = solver->getColLower();
const double * upper = solver->getColUpper();
// Restore bounds
int numDiff = 0;
BlisSolution* ksol = NULL;
int ind = model->getIntObjIndices()[colInd];
BlisObjectInt *intObj = dynamic_cast<BlisObjectInt *>(model->objects(ind));
#ifdef BLIS_DEBUG_MORE
for (j = 0; j < numCols; ++j) {
if (saveLower[j] != lower[j]) {
//solver->setColLower(j, saveLower[j]);
++numDiff;
}
if (saveUpper[j] != upper[j]) {
//solver->setColUpper(j, saveUpper[j]);
++numDiff;
}
}
std::cout << "BEFORE: numDiff = " << numDiff << std::endl;
#endif
//------------------------------------------------------
// Branching down.
//------------------------------------------------------
solver->setColUpper(colInd, floor(x));
solver->solveFromHotStart();
newObjValue = solver->getObjSense() * solver->getObjValue();
downDeg = newObjValue - objValue;
if (solver->isProvenOptimal()) {
lpStatus = 0; // optimal
#ifdef BLIS_DEBUG_MORE
printf("STRONG: COL[%d]: downDeg=%g, x=%g\n", colInd, downDeg, x);
#endif
// Update pseudocost
intObj->pseudocost().update(-1, downDeg, x);
model->setSharedObjectMark(ind);
// Check if ip feasible
ksol = model->feasibleSolution(numIntInfDown, numObjInfDown);
if (ksol) {
#ifdef BLIS_DEBUG_MORE
printf("STRONG:Down:found a feasible solution\n");
#endif
model->storeSolution(BlisSolutionTypeStrong, ksol);
downKeep = false;
}
else {
downKeep = true;
}
downFinished = true;
}
else if (solver->isIterationLimitReached() &&
!solver->isDualObjectiveLimitReached()) {
lpStatus = 2; // unknown
downKeep = true;
downFinished = false;
}
else {
downDeg = 1.0e20;
lpStatus = 1; // infeasible
downKeep = false;
downFinished = false;
}
#ifdef BLIS_DEBUG_MORE
std::cout << "Down: lpStatus = " << lpStatus << std::endl;
#endif
// restore bounds
numDiff = 0;
for (j = 0; j < numCols; ++j) {
if (saveLower[j] != lower[j]) {
solver->setColLower(j, saveLower[j]);
++numDiff;
}
if (saveUpper[j] != upper[j]) {
solver->setColUpper(j, saveUpper[j]);
++numDiff;
}
}
#ifdef BLIS_DEBUG
assert(numDiff > 0);
//std::cout << "numDiff = " << numDiff << std::endl;
#endif
//----------------------------------------------
// Branching up.
//----------------------------------------------
solver->setColLower(colInd, ceil(x));
solver->solveFromHotStart();
newObjValue = solver->getObjSense() * solver->getObjValue();
upDeg = newObjValue - objValue;
if (solver->isProvenOptimal()) {
lpStatus = 0; // optimal
#ifdef BLIS_DEBUG_MORE
printf("STRONG: COL[%d]: upDeg=%g, x=%g\n", colInd, upDeg, x);
#endif
// Update pseudocost
intObj->pseudocost().update(1, upDeg, x);
model->setSharedObjectMark(ind);
// Check if IP feasible
ksol = model->feasibleSolution(numIntInfDown, numObjInfDown);
if (ksol) {
#ifdef BLIS_DEBUG_MORE
printf("STRONG:Up:found a feasible solution\n");
#endif
model->storeSolution(BlisSolutionTypeStrong, ksol);
upKeep = false;
}
else {
upKeep = true;
}
upFinished = true;
}
else if (solver->isIterationLimitReached()
&&!solver->isDualObjectiveLimitReached()) {
lpStatus = 2; // unknown
upKeep = true;
upFinished = false;
}
else {
lpStatus = 1; // infeasible
upKeep = false;
upFinished = false;
upDeg = 1.0e20;
}
#ifdef BLIS_DEBUG_MORE
std::cout << "STRONG: Up: lpStatus = " << lpStatus << std::endl;
#endif
// restore bounds
for (j = 0; j < numCols; ++j) {
if (saveLower[j] != lower[j]) {
solver->setColLower(j,saveLower[j]);
}
if (saveUpper[j] != upper[j]) {
solver->setColUpper(j,saveUpper[j]);
}
}
return status;
}
int main (int argc, const char *argv[])
{
OsiClpSolverInterface solver1;
//#define USE_OSI_NAMES
#ifdef USE_OSI_NAMES
// Say we are keeping names (a bit slower this way)
solver1.setIntParam(OsiNameDiscipline,1);
#endif
// Read in model using argv[1]
// and assert that it is a clean model
std::string mpsFileName;
#if defined(SAMPLEDIR)
mpsFileName = SAMPLEDIR "/p0033.mps";
#else
if (argc < 2) {
fprintf(stderr, "Do not know where to find sample MPS files.\n");
exit(1);
}
#endif
if (argc>=2) mpsFileName = argv[1];
int numMpsReadErrors = solver1.readMps(mpsFileName.c_str(),"");
assert(numMpsReadErrors==0);
// Strip off integer information and save
int numberColumns = solver1.getNumCols();
char * integer = new char[numberColumns];
int i;
for (i=0;i<numberColumns;i++) {
if (solver1.isInteger(i)) {
integer[i]=1;
solver1.setContinuous(i);
} else {
integer[i]=0;
}
}
// Pass to Cbc initialize defaults
CbcModel model(solver1);
CbcMain0(model);
// Solve just to show there are no integers
model.branchAndBound();
// Set cutoff etc back in model and solver
model.resetModel();
// Solver was cloned so get it
OsiSolverInterface * solver = model.solver();
// Put back integers. Here the user could do anything really
#define ADD_DIRECTLY
#ifndef ADD_DIRECTLY
for (i=0;i<numberColumns;i++) {
if (integer[i])
solver->setInteger(i);
}
#else
CbcObject ** objects = new CbcObject * [ numberColumns];
int n=0;
for (i=0;i<numberColumns;i++) {
if (integer[i]) {
CbcSimpleIntegerDynamicPseudoCost * newObject =
new CbcSimpleIntegerDynamicPseudoCost(&model,i);
objects[n++]=newObject;
}
}
model.addObjects(n,objects);
for (i=0;i<n;i++)
delete objects[i];
delete [] objects;
#endif
delete [] integer;
/* Now go into code for standalone solver
Could copy arguments and add -quit at end to be safe
but this will do
*/
if (argc>2) {
CbcMain1(argc-1,argv+1,model);
} else {
const char * argv2[]={"driver3","-solve","-quit"};
CbcMain1(3,argv2,model);
}
// Print solution if finished (could get from model.bestSolution() as well
if (solver->getObjValue()*solver->getObjSense()<1.0e50) {
const double * solution = solver->getColSolution();
int iColumn;
std::cout<<std::setiosflags(std::ios::fixed|std::ios::showpoint)<<std::setw(14);
std::cout<<"--------------------------------------"<<std::endl;
#ifdef USE_OSI_NAMES
for (iColumn=0;iColumn<numberColumns;iColumn++) {
double value=solution[iColumn];
if (fabs(value)>1.0e-7&&solver->isInteger(iColumn))
std::cout<<std::setw(6)<<iColumn<<" "<<std::setw(8)<<setiosflags(std::ios::left)<<solver->getColName(iColumn)
<<resetiosflags(std::ios::adjustfield)<<std::setw(14)<<" "<<value<<std::endl;
}
#else
// names may not be in current solver - use original
for (iColumn=0;iColumn<numberColumns;iColumn++) {
double value=solution[iColumn];
if (fabs(value)>1.0e-7&&solver->isInteger(iColumn))
std::cout<<std::setw(6)<<iColumn<<" "<<std::setw(8)<<setiosflags(std::ios::left)<<solver1.getModelPtr()->columnName(iColumn)
<<resetiosflags(std::ios::adjustfield)<<std::setw(14)<<" "<<value<<std::endl;
}
#endif
std::cout<<"--------------------------------------"<<std::endl;
std::cout<<std::resetiosflags(std::ios::fixed|std::ios::showpoint|std::ios::scientific);
}
return 0;
}
void
CglLandP::generateCuts(const OsiSolverInterface & si, OsiCuts & cs,
const CglTreeInfo info )
{
if ((info.pass == 0) && !info.inTree)
{
numrows_ = si.getNumRows();
}
// scanExtraCuts(cs, si.getColSolution());
Parameters params = params_;
params.rhsWeight = numrows_ + 2;
handler_->message(CUT_GAP, messages_)<<info.pass<<si.getObjValue() <<CoinMessageEol;
if (info.inTree) //put lower pivot limit
{
params.pivotLimit = std::min(params.pivotLimit, params.pivotLimitInTree);
params.countMistakenRc = true;
}
if (params.timeLimit < 0)
{
params.pivotLimit = 0;
}
assert(si.basisIsAvailable());
#ifdef APPEND_ROW
OsiSolverInterface * t_si = si.clone();
if (params.modularize)
{
int new_idx = si.getNumCols();
int v_idx[1] = {new_idx};
double v_val[1] = {-1};
CoinPackedVector v(1, v_idx, v_val, false);
t_si->addCol(CoinPackedVector(), 0, 1, 0);
t_si->setInteger(new_idx);
t_si->addRow(v,0, 0);
t_si->resolve();
}
#else
const OsiSolverInterface * t_si = &si;
#endif
cached_.getData(*t_si);
CglLandPSimplex landpSi(*t_si, cached_, params, validator_);
if (params.generateExtraCuts == CglLandP::AllViolatedMigs)
{
landpSi.genThisBasisMigs(cached_, params);
}
landpSi.setLogLevel(handler_->logLevel());
int nCut = 0;
std::vector<int> indices;
getSortedFractionalIndices(indices,cached_, params);
#ifndef NDEBUG
int numrows = si.getNumRows();
#endif
#ifdef DO_STAT
//Get informations on current optimum
{
OsiSolverInterface * gapTester = si.clone();
gapTester->resolve();
roundsStats_.analyseOptimalBasis(gapTester,info.pass, numrows_);
delete gapTester;
}
#endif
params_.timeLimit += CoinCpuTime();
CoinRelFltEq eq(1e-04);
for (unsigned int i = 0; i < indices.size() && nCut < params.maxCutPerRound &&
nCut < cached_.nBasics_ ; i++)
{
//Check for time limit
int iRow = indices[i];
assert(iRow < numrows);
OsiRowCut cut;
int code=1;
OsiSolverInterface * ncSi = NULL;
if (params.pivotLimit != 0)
{
ncSi = t_si->clone();
landpSi.setSi(ncSi);
ncSi->setDblParam(OsiDualObjectiveLimit, COIN_DBL_MAX);
ncSi->messageHandler()->setLogLevel(0);
}
int generated = 0;
if (params.pivotLimit == 0)
{
generated = landpSi.generateMig(iRow, cut, params);
}
else
{
generated = landpSi.optimize(iRow, cut, cached_, params);
if (params.generateExtraCuts == CglLandP::AllViolatedMigs)
{
landpSi.genThisBasisMigs(cached_, params);
}
landpSi.resetSolver(cached_.basis_);
}
code = 0;
if (generated)
code = validator_(cut, cached_.colsol_, si, params, originalColLower_, originalColUpper_);
if (!generated || code)
{
if (params.pivotLimit !=0)
{
handler_->message(LAP_CUT_FAILED_DO_MIG, messages_)<<validator_.failureString(code)<<CoinMessageEol;
landpSi.freeSi();
OsiSolverInterface * ncSi = t_si->clone();
landpSi.setSi(ncSi);
params.pivotLimit = 0;
if (landpSi.optimize(iRow, cut, cached_, params))
{
code = validator_(cut, cached_.colsol_, si, params, originalColLower_, originalColUpper_);
}
params.pivotLimit = params_.pivotLimit;
}
}
if (params.pivotLimit != 0)
{
landpSi.freeSi();
}
if (code)
{
handler_->message(CUT_REJECTED, messages_)<<
validator_.failureString(code)<<CoinMessageEol;
}
else
{
if (canLift_)
{
cut.setGloballyValid(true);
}
cs.insertIfNotDuplicate(cut, eq);
//cs.insert(cut);
{
//std::cout<<"Violation "<<cut.violated(cached_.colsol_)<<std::endl;
nCut++;
}
}
}
Cuts& extra = landpSi.extraCuts();
for (int i = 0 ; i < cached_.nNonBasics_; i++)
{
OsiRowCut * cut = extra.rowCut(i);
if (cut == NULL) continue;
int code = validator_(*cut, cached_.colsol_, si, params,
originalColLower_, originalColUpper_);
if (code)
{
handler_->message(LAP_CUT_FAILED_DO_MIG, messages_)
<<validator_.failureString(code)<<CoinMessageEol;
}
else
{
cs.insertIfNotDuplicate(*cut, eq);
{
nCut++;
}
}
delete cut;
}
landpSi.outPivInfo(nCut);
params_.timeLimit -= CoinCpuTime();
cached_.clean();
#ifdef APPEND_ROW
assert(t_si != &si);
delete t_si;
#endif
}
int
main(void)
{
// Create a problem pointer. We use the base class here.
OsiSolverInterface *si;
// When we instantiate the object, we need a specific derived class.
si = new OSIXXX;
// Build our own instance from scratch
/*
* This section adapted from Matt Galati's example
* on the COIN-OR Tutorial website.
*
* Problem from Bertsimas, Tsitsiklis page 21
*
* optimal solution: x* = (1,1)
*
* minimize -1 x0 - 1 x1
* s.t 1 x0 + 2 x1 <= 3
* 2 x0 + 1 x1 <= 3
* x0 >= 0
* x1 >= 0
*/
int n_cols = 2;
double *objective = new double[n_cols];//the objective coefficients
double *col_lb = new double[n_cols];//the column lower bounds
double *col_ub = new double[n_cols];//the column upper bounds
//Define the objective coefficients.
//minimize -1 x0 - 1 x1
objective[0] = -1.0;
objective[1] = -1.0;
//Define the variable lower/upper bounds.
// x0 >= 0 => 0 <= x0 <= infinity
// x1 >= 0 => 0 <= x1 <= infinity
col_lb[0] = 0.0;
col_lb[1] = 0.0;
col_ub[0] = si->getInfinity();
col_ub[1] = si->getInfinity();
int n_rows = 2;
double *row_lb = new double[n_rows]; //the row lower bounds
double *row_ub = new double[n_rows]; //the row upper bounds
//Define the constraint matrix.
CoinPackedMatrix *matrix = new CoinPackedMatrix(false,0,0);
matrix->setDimensions(0, n_cols);
//1 x0 + 2 x1 <= 3 => -infinity <= 1 x0 + 2 x2 <= 3
CoinPackedVector row1;
row1.insert(0, 1.0);
row1.insert(1, 2.0);
row_lb[0] = -1.0 * si->getInfinity();
row_ub[0] = 3.0;
matrix->appendRow(row1);
//2 x0 + 1 x1 <= 3 => -infinity <= 2 x0 + 1 x1 <= 3
CoinPackedVector row2;
row2.insert(0, 2.0);
row2.insert(1, 1.0);
row_lb[1] = -1.0 * si->getInfinity();
row_ub[1] = 3.0;
matrix->appendRow(row2);
//load the problem to OSI
si->loadProblem(*matrix, col_lb, col_ub, objective, row_lb, row_ub);
//write the MPS file to a file called example.mps
si->writeMps("example");
// Solve the (relaxation of the) problem
si->initialSolve();
// Check the solution
if ( si->isProvenOptimal() ) {
std::cout << "Found optimal solution!" << std::endl;
std::cout << "Objective value is " << si->getObjValue() << std::endl;
int n = si->getNumCols();
const double *solution;
solution = si->getColSolution();
// We could then print the solution or examine it.
} else {
std::cout << "Didn't find optimal solution." << std::endl;
// Could then check other status functions.
}
return 0;
}
/** Create a set of candidate branching objects. */
int
BlisBranchStrategyRel::createCandBranchObjects(int numPassesLeft)
{
int bStatus = 0;
int i, pass, colInd;
int preferDir, saveLimit;
int numFirsts = 0;
int numInfs = 0;
int minCount = 0;
int numLowerTightens = 0;
int numUpperTightens = 0;
double lpX, score, infeasibility, downDeg, upDeg, sumDeg = 0.0;
bool roundAgain, downKeep, downGood, upKeep, upGood;
int *lbInd = NULL;
int *ubInd = NULL;
double *newLB = NULL;
double *newUB = NULL;
double * saveUpper = NULL;
double * saveLower = NULL;
double * saveSolution = NULL;
BlisModel *model = dynamic_cast<BlisModel *>(model_);
OsiSolverInterface * solver = model->solver();
int numCols = model->getNumCols();
int numObjects = model->numObjects();
//int lookAhead = dynamic_cast<BlisParams*>
// (model->blisPar())->entry(BlisParams::lookAhead);
//------------------------------------------------------
// Check if max time is reached or no pass is left.
//------------------------------------------------------
double timeLimit = model->AlpsPar()->entry(AlpsParams::timeLimit);
bool maxTimeReached = (CoinCpuTime() - model->startTime_ > timeLimit);
bool selectNow = false;
if (maxTimeReached || !numPassesLeft) {
selectNow = true;
#ifdef BLIS_DEBUG
printf("REL: CREATE: maxTimeReached %d, numPassesLeft %d\n",
maxTimeReached, numPassesLeft);
#endif
}
// Store first time objects.
std::vector<BlisObjectInt *> firstObjects;
// Store infeasible objects.
std::vector<BlisObjectInt *> infObjects;
// TODO: check if sorting is expensive.
std::multimap<double, BlisObjectInt*, BlisPseuoGreater> sortedObjects;
double objValue = solver->getObjSense() * solver->getObjValue();
const double * lower = solver->getColLower();
const double * upper = solver->getColUpper();
int lookAhead = dynamic_cast<BlisParams*>
(model->BlisPar())->entry(BlisParams::lookAhead);
BlisObjectInt * intObject = NULL;
//------------------------------------------------------
// Backup solver status and mark hot start.
//-----------------------------------------------------
saveSolution = new double[numCols];
memcpy(saveSolution, solver->getColSolution(), numCols*sizeof(double));
saveLower = new double[numCols];
saveUpper = new double[numCols];
memcpy(saveLower, lower, numCols * sizeof(double));
memcpy(saveUpper, upper, numCols * sizeof(double));
//------------------------------------------------------
// Find the infeasible objects.
// NOTE: we might go round this loop twice if we are feed in
// a "feasible" solution.
//------------------------------------------------------
for (pass = 0; pass < 2; ++pass) {
numInfs = 0;
BcpsObject * object = NULL;
infObjects.clear();
firstObjects.clear();
for (i = 0; i < numObjects; ++i) {
object = model->objects(i);
infeasibility = object->infeasibility(model, preferDir);
if (infeasibility) {
++numInfs;
intObject = dynamic_cast<BlisObjectInt *>(object);
if (intObject) {
//score = object->pseudocost().getScore();
//tempBO = object->createBranchObject(model, preferDir);
//candObjects.insert(std::make_pair(score, tempBO));
//tempBO = NULL;
infObjects.push_back(intObject);
if (!selectNow) {
minCount =
ALPS_MIN(intObject->pseudocost().getDownCount(),
intObject->pseudocost().getUpCount());
if (minCount < 1) {
firstObjects.push_back(intObject);
}
}
#ifdef BLIS_DEBUG_MORE
if (intObject->columnIndex() == 15) {
std::cout << "x[15] = " << saveSolution[15]
<< std::endl;
}
#endif
intObject = NULL;
}
else {
// TODO: currently all are integer objects.
#ifdef BLIS_DEBU
assert(0);
#endif
}
}
}
if (numInfs) {
#ifdef BLIS_DEBUG_MORE
std::cout << "REL: numInfs = " << numInfs
<< std::endl;
#endif
break;
}
else if (pass == 0) {
// The first pass and is IP feasible.
#ifdef BLIS_DEBUG
std::cout << "REL: given a feasible sol" << std::endl;
#endif
roundAgain = false;
CoinWarmStartBasis * ws =
dynamic_cast<CoinWarmStartBasis*>(solver->getWarmStart());
if (!ws) break;
// Force solution values within bounds
for (i = 0; i < numCols; ++i) {
lpX = saveSolution[i];
if (lpX < lower[i]) {
saveSolution[i] = lower[i];
roundAgain = true;
ws->setStructStatus(i, CoinWarmStartBasis::atLowerBound);
}
else if (lpX > upper[i]) {
saveSolution[i] = upper[i];
roundAgain = true;
ws->setStructStatus(i, CoinWarmStartBasis::atUpperBound);
}
}
if (roundAgain) {
// Need resolve and do the second round selection.
solver->setWarmStart(ws);
delete ws;
// Resolve.
solver->resolve();
if (!solver->isProvenOptimal()) {
// Become infeasible, can do nothing.
bStatus = -2;
goto TERM_CREATE;
}
else {
// Save new lp solution.
memcpy(saveSolution, solver->getColSolution(),
numCols * sizeof(double));
objValue = solver->getObjSense() * solver->getObjValue();
}
}
else {
delete ws;
break;
}
}
} // EOF 2 pass
//--------------------------------------------------
// If we have a set of first time object,
// branch up and down to initialize pseudo-cost.
//--------------------------------------------------
numFirsts = static_cast<int> (firstObjects.size());
if (numFirsts > 0) {
CoinWarmStart * ws = solver->getWarmStart();
solver->getIntParam(OsiMaxNumIterationHotStart, saveLimit);
int maxIter = ALPS_MAX(model->getAveIterations(), 50);
solver->setIntParam(OsiMaxNumIterationHotStart, maxIter);
solver->markHotStart();
lbInd = new int [numFirsts];
ubInd = new int [numFirsts];
newLB = new double [numFirsts];
newUB = new double [numFirsts];
for (i = 0; i < numFirsts && bStatus != -2; ++i) {
colInd = firstObjects[i]->columnIndex();
lpX = saveSolution[colInd];
BlisStrongBranch(model, objValue, colInd, lpX,
saveLower, saveUpper,
downKeep, downGood, downDeg,
upKeep, upGood, upDeg);
if(!downKeep && !upKeep) {
// Both branch can be fathomed
bStatus = -2;
}
else if (!downKeep) {
// Down branch can be fathomed.
lbInd[numLowerTightens] = colInd;
newLB[numLowerTightens++] = ceil(lpX);
//break;
}
else if (!upKeep) {
// Up branch can be fathomed.
ubInd[numUpperTightens] = colInd;
newUB[numUpperTightens++] = floor(lpX);
// break;
}
// Update pseudocost.
if(downGood) {
firstObjects[i]->pseudocost().update(-1, downDeg, lpX);
}
if(downGood) {
firstObjects[i]->pseudocost().update(1, upDeg, lpX);
}
}
//--------------------------------------------------
// Set new bounds in lp solver for resolving
//--------------------------------------------------
if (bStatus != -2) {
if (numUpperTightens > 0) {
bStatus = -1;
for (i = 0; i < numUpperTightens; ++i) {
solver->setColUpper(ubInd[i], newUB[i]);
}
}
if (numLowerTightens > 0) {
bStatus = -1;
for (i = 0; i < numLowerTightens; ++i) {
solver->setColLower(lbInd[i], newLB[i]);
}
}
}
//--------------------------------------------------
// Unmark hotstart and recover LP solver.
//--------------------------------------------------
solver->unmarkHotStart();
solver->setColSolution(saveSolution);
solver->setIntParam(OsiMaxNumIterationHotStart, saveLimit);
solver->setWarmStart(ws);
delete ws;
}
//std::cout << "REL: bStatus = " << bStatus << std::endl;
if (bStatus < 0) {
// Infeasible or monotone.
goto TERM_CREATE;
}
else {
// All object's pseudocost have been initialized.
// Sort them, and do strong branch for the unreliable one
// NOTE: it set model->savedLpSolution.
// model->feasibleSolution(numIntegerInfs, numObjectInfs);
sumDeg = 0.0;
for (i = 0; i < numInfs; ++i) {
score = infObjects[i]->pseudocost().getScore();
sumDeg += score;
std::pair<const double, BlisObjectInt*> sa(score, infObjects[i]);
sortedObjects.insert(sa);
#ifdef BLIS_DEBUG_MORE
std::cout << "col[" << infObjects[i]->columnIndex() << "]="
<< score << ", "<< std::endl;
#endif
}
int numNotChange = 0;
std::multimap< double, BlisObjectInt*, BlisPseuoGreater >::iterator pos;
CoinWarmStart * ws = solver->getWarmStart();
solver->getIntParam(OsiMaxNumIterationHotStart, saveLimit);
int maxIter = ALPS_MAX(model->getAveIterations(), 50);
solver->setIntParam(OsiMaxNumIterationHotStart, maxIter);
solver->markHotStart();
BlisObjectInt *bestObject = NULL;
double bestScore = -10.0;
for (pos = sortedObjects.begin(); pos != sortedObjects.end(); ++pos) {
intObject = pos->second;
colInd = intObject->columnIndex();
#ifdef BLIS_DEBUG_MORE
std::cout << "col[" << colInd << "]: "
<< "score=" << pos->first
<< ", upCount=" << intObject->pseudocost().getUpCount()
<<", downCount="<< intObject->pseudocost().getDownCount()
<< std::endl;
#endif
// Check if reliable.
int objRelibility=ALPS_MIN(intObject->pseudocost().getUpCount(),
intObject->pseudocost().getDownCount());
if (objRelibility < relibility_) {
// Unrelible object. Do strong branching.
lpX = saveSolution[colInd];
BlisStrongBranch(model, objValue, colInd, lpX,
saveLower, saveUpper,
downKeep, downGood, downDeg,
upKeep, upGood, upDeg);
// Update pseudocost.
if(downGood) {
intObject->pseudocost().update(-1, downDeg, lpX);
}
if(downGood) {
intObject->pseudocost().update(1, upDeg, lpX);
}
}
// Compare with the best.
if (intObject->pseudocost().getScore() > bestScore) {
bestScore = intObject->pseudocost().getScore();
bestObject = intObject;
// Reset
numNotChange = 0;
}
else {
// If best doesn't change for "lookAhead" comparisons, then
// the best is reliable.
if (++numNotChange > lookAhead) {
if (bestObject->pseudocost().getUpCost() >
bestObject->pseudocost().getDownCost()) {
preferDir = 1;
}
else {
preferDir = -1;
}
break;
}
}
}
solver->unmarkHotStart();
solver->setColSolution(saveSolution);
solver->setIntParam(OsiMaxNumIterationHotStart, saveLimit);
solver->setWarmStart(ws);
delete ws;
model->setSolEstimate(objValue + sumDeg);
assert(bestObject != NULL);
bestBranchObject_ = bestObject->createBranchObject(model, preferDir);
}
TERM_CREATE:
//------------------------------------------------------
// Cleanup.
//------------------------------------------------------
delete [] lbInd;
delete [] ubInd;
delete [] newLB;
delete [] newUB;
delete [] saveSolution;
delete [] saveLower;
delete [] saveUpper;
return bStatus;
}
void
CglMixedIntegerRoundingUnitTest(const OsiSolverInterface *baseSiP,
const std::string mpsDir)
{
// Test default constructor
{
CglMixedIntegerRounding aGenerator;
}
// Test copy & assignment
{
CglMixedIntegerRounding rhs;
{
CglMixedIntegerRounding bGenerator;
CglMixedIntegerRounding cGenerator(bGenerator);
rhs=bGenerator;
}
}
// Test get/set methods
{
CglMixedIntegerRounding getset;
int gagg = 10 * getset.getMAXAGGR_();
getset.setMAXAGGR_(gagg);
int gagg2 = getset.getMAXAGGR_();
assert(gagg == gagg2);
bool gmult = !getset.getMULTIPLY_();
getset.setMULTIPLY_(gmult);
bool gmult2 = getset.getMULTIPLY_();
assert(gmult == gmult2);
int gcrit = getset.getCRITERION_();
gcrit = (gcrit) % 3 + 1;
getset.setCRITERION_(gcrit);
int gcrit2 = getset.getCRITERION_();
assert(gcrit == gcrit2);
int gpre = getset.getDoPreproc();
gpre = (gpre + 1) % 3 - 1;
getset.setDoPreproc(gpre);
int gpre2 = getset.getDoPreproc();
assert(gpre == gpre2);
}
// Test generateCuts
{
CglMixedIntegerRounding gct;
OsiSolverInterface *siP = baseSiP->clone();
std::string fn = mpsDir+"capPlan1";
std::string fn2 = mpsDir+"capPlan1.mps";
FILE *in_f = fopen(fn2.c_str(), "r");
if(in_f == NULL) {
std::cout<<"Can not open file "<<fn2<<std::endl<<"Skip test of CglMixedIntegerRounding::generateCuts()"<<std::endl;
}
else {
fclose(in_f);
siP->readMps(fn.c_str(),"mps");
siP->initialSolve();
double lpRelax = siP->getObjValue();
OsiCuts cs;
gct.generateCuts(*siP, cs);
int nRowCuts = cs.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" MIR cuts"<<std::endl;
assert(cs.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cs);
siP->resolve();
double lpRelaxAfter= siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelax<<std::endl;
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
assert( lpRelax < lpRelaxAfter );
assert(lpRelaxAfter < 964);
}
delete siP;
}
}
TNLPSolver::ReturnStatus LpBranchingSolver::
solveFromHotStart(OsiTMINLPInterface* tminlp_interface)
{
TNLPSolver::ReturnStatus retstatus = TNLPSolver::solvedOptimal;
// updated the bounds of the linear solver
std::vector<int> diff_low_bnd_index;
std::vector<double> diff_low_bnd_value;
std::vector<int> diff_up_bnd_index;
std::vector<double> diff_up_bnd_value;
// Get the bounds. We assume that the bounds in the linear solver
// are always the original ones
const int numCols = tminlp_interface->getNumCols();
const double* colLow_orig = lin_->getColLower();
const double* colUp_orig = lin_->getColUpper();
const double* colLow = tminlp_interface->getColLower();
const double* colUp = tminlp_interface->getColUpper();
OsiSolverInterface * lin = lin_;
// eventualy clone lin_
if(warm_start_mode_ == Clone){
lin = lin_->clone();
// std::cout<<"Cloning it"<<std::endl;
}
// Set the bounds on the LP solver according to the changes in
// tminlp_interface
for (int i=0; i<numCols; i++) {
const double& lo = colLow[i];
if (colLow_orig[i] < lo) {
if(warm_start_mode_ == Basis){
diff_low_bnd_value.push_back(colLow_orig[i]);
diff_low_bnd_index.push_back(i);
}
lin->setColLower(i,lo);
}
const double& up = colUp[i];
if (colUp_orig[i] > up) {
if(warm_start_mode_ == Basis){
diff_up_bnd_index.push_back(i);
diff_up_bnd_value.push_back(colUp_orig[i]);
}
lin->setColUpper(i,lo);
}
}
if(warm_start_mode_ == Basis){
lin->setWarmStart(warm_);
}
lin->resolve();
double obj = lin->getObjValue();
bool go_on = true;
if (lin->isProvenPrimalInfeasible() ||
lin->isDualObjectiveLimitReached()) {
retstatus = TNLPSolver::provenInfeasible;
go_on = false;
}
else if (lin->isIterationLimitReached()) {
retstatus = TNLPSolver::iterationLimit;
go_on = false;
}
else {
if (maxCuttingPlaneIterations_ > 0 && go_on) {
double violation;
obj = ecp_->doEcpRounds(*lin, true, &violation);
if (obj == COIN_DBL_MAX) {
retstatus = TNLPSolver::provenInfeasible;
}
else if (violation <= 1e-8) {
retstatus = TNLPSolver::solvedOptimal;
}
}
}
tminlp_interface->problem()->set_obj_value(obj);
tminlp_interface->problem()->Set_x_sol(numCols, lin_->getColSolution());
//restore the original bounds
if(warm_start_mode_ == Basis){
for (unsigned int i = 0; i < diff_low_bnd_index.size(); i++) {
lin_->setColLower(diff_low_bnd_index[i],diff_low_bnd_value[i]);
}
for (unsigned int i = 0; i < diff_up_bnd_index.size(); i++) {
lin_->setColUpper(diff_up_bnd_index[i],diff_up_bnd_value[i]);
}
}
else {
delete lin;
}
return retstatus;
}
// See if rounding will give solution
// Sets value of solution
// Assumes rhs for original matrix still okay
// At present only works with integers
// Fix values if asked for
// Returns 1 if solution, 0 if not
int
AbcRounding::solution(double & solutionValue,
double * betterSolution)
{
// Get a copy of original matrix (and by row for rounding);
matrix_ = *(model_->solver()->getMatrixByCol());
matrixByRow_ = *(model_->solver()->getMatrixByRow());
seed_=1;
OsiSolverInterface * solver = model_->solver();
const double * lower = solver->getColLower();
const double * upper = solver->getColUpper();
const double * rowLower = solver->getRowLower();
const double * rowUpper = solver->getRowUpper();
const double * solution = solver->getColSolution();
const double * objective = solver->getObjCoefficients();
double integerTolerance = 1.0e-5;
//model_->getDblParam(AbcModel::AbcIntegerTolerance);
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 = direction * solver->getObjValue();
int returnCode = 0;
// Column copy
const double * element = matrix_.getElements();
const int * row = matrix_.getIndices();
const int * columnStart = matrix_.getVectorStarts();
const int * columnLength = matrix_.getVectorLengths();
// Row copy
const double * elementByRow = matrixByRow_.getElements();
const int * column = matrixByRow_.getIndices();
const int * rowStart = matrixByRow_.getVectorStarts();
const int * rowLength = matrixByRow_.getVectorLengths();
// Get solution array for heuristic solution
int numberColumns = solver->getNumCols();
double * newSolution = new double [numberColumns];
memcpy(newSolution, solution, numberColumns * sizeof(double));
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 was feasible - if not adjust (cleaning may move)
for (i = 0; i < numberRows; i++) {
if(rowActivity[i] < rowLower[i]) {
//assert (rowActivity[i]>rowLower[i]-1000.0*primalTolerance);
rowActivity[i] = rowLower[i];
} else if(rowActivity[i] > rowUpper[i]) {
//assert (rowActivity[i]<rowUpper[i]+1000.0*primalTolerance);
rowActivity[i] = rowUpper[i];
}
}
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
double below = floor(value);
double newValue = newSolution[iColumn];
double cost = direction * objective[iColumn];
double move;
if (cost > 0.0) {
// try up
move = 1.0 - (value - below);
} else if (cost < 0.0) {
// try down
move = below - value;
} else {
// won't be able to move unless we can grab another variable
// just for now go down
move = below-value;
}
newValue += move;
newSolution[iColumn] = newValue;
newSolutionValue += move * cost;
int j;
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] += move * element[j];
}
}
}
double penalty = 0.0;
// see if feasible
for (i = 0; i < numberRows; i++) {
double value = rowActivity[i];
double thisInfeasibility = 0.0;
if (value < rowLower[i] - primalTolerance)
thisInfeasibility = value - rowLower[i];
else if (value > rowUpper[i] + primalTolerance)
thisInfeasibility = value - rowUpper[i];
if (thisInfeasibility) {
// See if there are any slacks I can use to fix up
// maybe put in coding for multiple slacks?
double bestCost = 1.0e50;
int k;
int iBest = -1;
double addCost = 0.0;
double newValue = 0.0;
double changeRowActivity = 0.0;
double absInfeasibility = fabs(thisInfeasibility);
for (k = rowStart[i]; k < rowStart[i] + rowLength[i]; k++) {
int iColumn = column[k];
if (columnLength[iColumn] == 1) {
double currentValue = newSolution[iColumn];
double elementValue = elementByRow[k];
double lowerValue = lower[iColumn];
double upperValue = upper[iColumn];
double gap = rowUpper[i] - rowLower[i];
double absElement = fabs(elementValue);
if (thisInfeasibility * elementValue > 0.0) {
// we want to reduce
if ((currentValue - lowerValue) * absElement >=
absInfeasibility) {
// possible - check if integer
double distance = absInfeasibility / absElement;
double thisCost =
-direction * objective[iColumn] * distance;
if (solver->isInteger(iColumn)) {
distance = ceil(distance - primalTolerance);
assert (currentValue - distance >=
lowerValue - primalTolerance);
if (absInfeasibility - distance * absElement
< -gap - primalTolerance)
thisCost = 1.0e100; // no good
else
thisCost =
-direction*objective[iColumn]*distance;
}
if (thisCost < bestCost) {
bestCost = thisCost;
iBest = iColumn;
addCost = thisCost;
newValue = currentValue - distance;
changeRowActivity = -distance * elementValue;
}
}
} else {
// we want to increase
if ((upperValue - currentValue) * absElement >=
absInfeasibility) {
// possible - check if integer
double distance = absInfeasibility / absElement;
double thisCost =
direction * objective[iColumn] * distance;
if (solver->isInteger(iColumn)) {
distance = ceil(distance - 1.0e-7);
assert (currentValue - distance <=
upperValue + primalTolerance);
if (absInfeasibility - distance * absElement
< -gap - primalTolerance)
thisCost = 1.0e100; // no good
else
thisCost =
direction*objective[iColumn]*distance;
}
if (thisCost < bestCost) {
bestCost = thisCost;
iBest = iColumn;
addCost = thisCost;
newValue = currentValue + distance;
changeRowActivity = distance * elementValue;
}
}
}
}
}
if (iBest >= 0) {
/*printf("Infeasibility of %g on row %d cost %g\n",
thisInfeasibility,i,addCost);*/
newSolution[iBest] = newValue;
thisInfeasibility = 0.0;
newSolutionValue += addCost;
rowActivity[i] += changeRowActivity;
}
penalty += fabs(thisInfeasibility);
}
}
// Could also set SOS (using random) and repeat
if (!penalty) {
// See if we can do better
//seed_++;
//CoinSeedRandom(seed_);
// Random number between 0 and 1.
double randomNumber = CoinDrand48();
int iPass;
int start[2];
int end[2];
int iRandom = (int) (randomNumber * ((double) numberIntegers));
start[0] = iRandom;
end[0] = numberIntegers;
start[1] = 0;
end[1] = iRandom;
for (iPass = 0; iPass < 2; iPass++) {
int i;
for (i = start[iPass]; i < end[iPass]; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
assert(fabs(floor(value + 0.5) - value) < integerTolerance);
double cost = direction * objective[iColumn];
double move = 0.0;
if (cost > 0.0)
move = -1.0;
else if (cost < 0.0)
move = 1.0;
while (move) {
bool good = true;
double newValue = newSolution[iColumn] + move;
if (newValue < lower[iColumn] - primalTolerance||
newValue > upper[iColumn] + primalTolerance) {
move = 0.0;
} else {
// see if we can move
int j;
for (j = columnStart[iColumn];
j < columnStart[iColumn] + columnLength[iColumn];
j++) {
int iRow = row[j];
double newActivity =
rowActivity[iRow] + move*element[j];
if (newActivity < rowLower[iRow] - primalTolerance
||
newActivity > rowUpper[iRow]+primalTolerance) {
good = false;
break;
}
}
if (good) {
newSolution[iColumn] = newValue;
newSolutionValue += move * cost;
int j;
for (j = columnStart[iColumn];
j < columnStart[iColumn] +
columnLength[iColumn]; j++) {
int iRow = row[j];
rowActivity[iRow] += move*element[j];
}
} else {
move=0.0;
}
}
}
}
}
if (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];
}
}
}
// check was approximately feasible
bool feasible = true;
for (i = 0; i < numberRows; i++) {
if(rowActivity[i] < rowLower[i]) {
if (rowActivity[i] < rowLower[i] - 1000.0*primalTolerance)
feasible = false;
} else if(rowActivity[i] > rowUpper[i]) {
if (rowActivity[i] > rowUpper[i] + 1000.0*primalTolerance)
feasible = false;
}
}
if (feasible) {
// new solution
memcpy(betterSolution, newSolution,
numberColumns * sizeof(double));
solutionValue = newSolutionValue;
//printf("** Solution of %g found by rounding\n",newSolutionValue);
returnCode=1;
} else {
// Can easily happen
//printf("Debug AbcRounding giving bad solution\n");
}
}
}
delete [] newSolution;
delete [] rowActivity;
return returnCode;
}
/** Perform a branch-and-bound on given setup.*/
void CouenneBab::branchAndBound (Bonmin::BabSetupBase & s) {
double remaining_time = s.getDoubleParameter(Bonmin::BabSetupBase::MaxTime) + CoinCpuTime();
/* Put a link to this into solver.*/
OsiBabSolver * babInfo = dynamic_cast<OsiBabSolver *>(s.continuousSolver()->getAuxiliaryInfo());
assert(babInfo);
Bonmin::BabInfo * bonBabInfoPtr = dynamic_cast<Bonmin::BabInfo*>(babInfo);
if (bonBabInfoPtr == NULL) { //Replace with a Bonmin::babInfo
bonBabInfoPtr = new Bonmin::BabInfo(*babInfo);
s.continuousSolver()->setAuxiliaryInfo(bonBabInfoPtr);
delete bonBabInfoPtr;
bonBabInfoPtr = dynamic_cast<Bonmin::BabInfo*>(s.continuousSolver()->getAuxiliaryInfo());
}
bonBabInfoPtr->setBabPtr(this);
s.nonlinearSolver()->solver()->setup_global_time_limit(s.getDoubleParameter(Bonmin::BabSetupBase::MaxTime));
OsiSolverInterface * solver = s.continuousSolver()->clone();
delete modelHandler_;
modelHandler_ = s.continuousSolver()->messageHandler()->clone();
model_.passInMessageHandler(modelHandler_);
model_.assignSolver(solver, true);
// s.continuousSolver() = model_.solver();
// if(s.continuousSolver()->objects()!=NULL){
// model_.addObjects(s.continuousSolver()->numberObjects(),s.continuousSolver()->objects());
// }
int specOpt = s.getIntParameter(Bonmin::BabSetupBase::SpecialOption);
if (specOpt) {
model_.setSpecialOptions(specOpt);
if (specOpt==16) {
Bonmin::CbcNlpStrategy strat(s.getIntParameter(Bonmin::BabSetupBase::MaxFailures),
s.getIntParameter(Bonmin::BabSetupBase::MaxInfeasible),
s.getIntParameter(Bonmin::BabSetupBase::FailureBehavior));
model_.setStrategy(strat);
}
}
model_.setMaximumCutPasses(s.getIntParameter(Bonmin::BabSetupBase::NumCutPasses));
model_.setMaximumCutPassesAtRoot(s.getIntParameter(Bonmin::BabSetupBase::NumCutPassesAtRoot));
//Setup cutting plane methods
for (Bonmin::BabSetupBase::CuttingMethods::iterator i = s.cutGenerators().begin() ;
i != s.cutGenerators().end() ; i++) {
Bonmin::OaDecompositionBase * oa = dynamic_cast<Bonmin::OaDecompositionBase *>(i->cgl);
if (oa && oa->reassignLpsolver())
oa->assignLpInterface(model_.solver());
model_.addCutGenerator(i->cgl,i->frequency,i->id.c_str(), i->normal,
i->atSolution);
if(i->always){
model_.cutGenerators()[model_.numberCutGenerators()-1]
->setMustCallAgain(true);
}
}
for (Bonmin::BabSetupBase::HeuristicMethods::iterator i = s.heuristics().begin() ;
i != s.heuristics().end() ; i++) {
CbcHeuristic * heu = i->heuristic;
heu->setModel(&model_);
model_.addHeuristic(heu, i->id.c_str());
}
//need to record solver logLevel here
int logLevel = s.continuousSolver()->messageHandler()->logLevel();
//Set true branch-and-bound parameters
model_.setLogLevel(s.getIntParameter(Bonmin::BabSetupBase::BabLogLevel));
// Put back solver logLevel
model_.solver()->messageHandler()->setLogLevel(logLevel);
model_.setPrintFrequency(s.getIntParameter(Bonmin::BabSetupBase::BabLogInterval));
bool ChangedObject = false;
//Pass over user set branching priorities to Cbc
if (s.continuousSolver()->objects()==NULL) {
//assert (s.branchingMethod() == NULL);
const OsiTMINLPInterface * nlpSolver = s.nonlinearSolver();
//set priorities, prefered directions...
const int * priorities = nlpSolver->getPriorities();
const double * upPsCosts = nlpSolver->getUpPsCosts();
const double * downPsCosts = nlpSolver->getDownPsCosts();
const int * directions = nlpSolver->getBranchingDirections();
bool hasPseudo = (upPsCosts!=NULL);
model_.findIntegers(true,hasPseudo);
OsiObject ** simpleIntegerObjects = model_.objects();
int numberObjects = model_.numberObjects();
if (priorities != NULL || directions != NULL || hasPseudo) {
ChangedObject = true;
for (int i = 0 ; i < numberObjects ; i++) {
CbcObject * object = dynamic_cast<CbcObject *>
(simpleIntegerObjects[i]);
int iCol = object->columnNumber();
if (priorities)
object->setPriority(priorities[iCol]);
if (directions)
object->setPreferredWay(directions[iCol]);
if (upPsCosts) {
CbcSimpleIntegerPseudoCost * pscObject =
dynamic_cast<CbcSimpleIntegerPseudoCost*> (object);
pscObject->setUpPseudoCost(upPsCosts[iCol]);
pscObject->setDownPseudoCost(downPsCosts[iCol]);
}
}
}
#if 1
// Now pass user set Sos constraints (code inspired from CoinSolve.cpp)
const TMINLP::SosInfo * sos = s.nonlinearSolver()->model()->sosConstraints();
if (!s.getIntParameter(Bonmin::BabSetupBase::DisableSos) && sos && sos->num > 0) {
// we have some sos constraints
const OsiTMINLPInterface * nlpSolver = s.nonlinearSolver();
const int & numSos = sos->num;
(*nlpSolver->messageHandler())<<"Adding "<<sos->num<<" sos constraints."
<<CoinMessageEol;
CbcObject ** objects = new CbcObject*[numSos];
const int * starts = sos->starts;
const int * indices = sos->indices;
const char * types = sos->types;
const double * weights = sos->weights;
//verify if model has user set priorities
bool hasPriorities = false;
const int * varPriorities = nlpSolver->getPriorities();
int numberObjects = model_.numberObjects();
if (varPriorities)
{
for (int i = 0 ; i < numberObjects ; i++) {
if (varPriorities[i]) {
hasPriorities = true;
break;
}
}
}
const int * sosPriorities = sos->priorities;
if (sosPriorities)
{
for (int i = 0 ; i < numSos ; i++) {
if (sosPriorities[i]) {
hasPriorities = true;
break;
}
}
}
for (int i = 0 ; i < numSos ; i++)
{
int start = starts[i];
int length = starts[i + 1] - start;
#ifdef DO_IT_NWAY
printf("setting nway object\n"),
objects[i] = new CbcNWay(&model_, length, &indices[start],
i);
objects[i]->setPriority(1);
#else
objects[i] = new CbcSOS(&model_, length, &indices[start],
&weights[start], i, types[i]);
objects[i]->setPriority(10);
#endif
if (hasPriorities && sosPriorities && sosPriorities[i]) {
objects[i]->setPriority(sosPriorities[i]);
}
}
model_.addObjects (numSos, objects);
for (int i = 0 ; i < numSos ; i++)
delete objects[i];
delete [] objects;
}
#endif
//If Setup contains more objects add them to Cbc
if (s.objects().size()) {
CbcObject ** objects = new CbcObject *[s.objects().size()];
for (unsigned int i = 0 ; i < s.objects().size() ; i++) {
objects[i] = dynamic_cast<CbcObject *> (s.objects()[i]);
assert(objects[i]);
objects[i]->setModel(&model_);
}
model_.addObjects ((int) s.objects().size(), objects);
delete [] objects;
}
replaceIntegers(model_.objects(), model_.numberObjects());
} else { // Pass in objects to Cbc
// Redundant definition of default branching (as Default == User)
assert (s.branchingMethod() != NULL);
// Add nonlinear and integer objects (need to add OsiSOS)
model_.addObjects (s.continuousSolver () -> numberObjects (), s.continuousSolver () -> objects ());
// Now model_ has only CouenneObjects and SOS objects
// for (int i=0; i<nco; i++)
// if (!(dynamic_cast <CbcSimpleInteger *> (s.continuousSolver () -> objects () [i])))
// model_ . objects () [nRealObj++] = s.continuousSolver () -> objects () [i] -> clone ();
CbcBranchDefaultDecision branch;
s.branchingMethod()->setSolver(model_.solver());
BonChooseVariable * strong2 = dynamic_cast<BonChooseVariable *>(s.branchingMethod());
if (strong2)
strong2->setCbcModel(&model_);
branch.setChooseMethod(*s.branchingMethod());
model_.setBranchingMethod(&branch);
// prevent duplicating object when copying in CbcModel.cpp
model_.solver()->deleteObjects();
}
model_.setDblParam(CbcModel::CbcCutoffIncrement, s.getDoubleParameter(Bonmin::BabSetupBase::CutoffDecr));
model_.setCutoff(s.getDoubleParameter(Bonmin::BabSetupBase::Cutoff) + CUTOFF_TOL);
model_.setDblParam(CbcModel::CbcAllowableGap, s.getDoubleParameter(Bonmin::BabSetupBase::AllowableGap));
model_.setDblParam(CbcModel::CbcAllowableFractionGap, s.getDoubleParameter(Bonmin::BabSetupBase::AllowableFractionGap));
// Definition of node selection strategy
if (s.nodeComparisonMethod()==Bonmin::BabSetupBase::bestBound) {
CbcCompareObjective compare;
model_.setNodeComparison(compare);
}
else if (s.nodeComparisonMethod()==Bonmin::BabSetupBase::DFS) {
CbcCompareDepth compare;
model_.setNodeComparison(compare);
}
else if (s.nodeComparisonMethod()==Bonmin::BabSetupBase::BFS) {
CbcCompareDefault compare;
compare.setWeight(0.0);
model_.setNodeComparison(compare);
}
else if (s.nodeComparisonMethod()==Bonmin::BabSetupBase::dynamic) {
CbcCompareDefault compare;
model_.setNodeComparison(compare);
}
else if (s.nodeComparisonMethod()==Bonmin::BabSetupBase::bestGuess) {
// Right now, this is a mess. We need a separation of the
// pseudo costs from the ChooseVariable method
CbcCompareEstimate compare;
model_.setNodeComparison(compare);
GuessHeuristic * guessHeu = new GuessHeuristic(model_);
model_.addHeuristic(guessHeu);
delete guessHeu;
}
if (s.treeTraversalMethod() == Bonmin::BabSetupBase::HeapOnly) {
//Do nothing this is the default of Cbc.
}
else if (s.treeTraversalMethod() == Bonmin::BabSetupBase::DiveFromBest) {
CbcDiver treeTraversal;
treeTraversal.initialize(s);
model_.passInTreeHandler(treeTraversal);
}
else if (s.treeTraversalMethod() == Bonmin::BabSetupBase::ProbedDive) {
CbcProbedDiver treeTraversal;
treeTraversal.initialize(s);
model_.passInTreeHandler(treeTraversal);
}
else if (s.treeTraversalMethod() == Bonmin::BabSetupBase::DfsDiveFromBest) {
CbcDfsDiver treeTraversal;
treeTraversal.initialize(s);
model_.passInTreeHandler(treeTraversal);
}
else if (s.treeTraversalMethod() == Bonmin::BabSetupBase::DfsDiveDynamic) {
CbcDfsDiver treeTraversal;
treeTraversal.initialize(s);
model_.passInTreeHandler(treeTraversal);
DiverCompare compare;
compare.setComparisonDive(*model_.nodeComparison());
compare.setComparisonBound(CbcCompareObjective());
CbcDfsDiver * dfs = dynamic_cast<CbcDfsDiver *> (model_.tree());
assert(dfs);
compare.setDiver(dfs);
model_.setNodeComparison(compare);
}
model_.setNumberStrong(s.getIntParameter(Bonmin::BabSetupBase::NumberStrong));
model_.setNumberBeforeTrust(s.getIntParameter(Bonmin::BabSetupBase::MinReliability));
model_.setNumberPenalties(8);
model_.setDblParam(CbcModel::CbcMaximumSeconds, s.getDoubleParameter(Bonmin::BabSetupBase::MaxTime));
model_.setMaximumNodes(s.getIntParameter(Bonmin::BabSetupBase::MaxNodes));
model_.setMaximumNumberIterations(s.getIntParameter(Bonmin::BabSetupBase::MaxIterations));
model_.setMaximumSolutions(s.getIntParameter(Bonmin::BabSetupBase::MaxSolutions));
model_.setIntegerTolerance(s.getDoubleParameter(Bonmin::BabSetupBase::IntTol));
//Get objects from model_ if it is not null means there are some sos constraints or non-integer branching object
// pass them to cut generators.
OsiObject ** objects = model_.objects();
if (specOpt!=16 && objects) {
int numberObjects = model_.numberObjects();
if (objects_ != NULL) {
for (int i = 0 ; i < nObjects_; i++)
delete objects_[i];
}
delete [] objects_;
objects_ = new OsiObject*[numberObjects];
nObjects_ = numberObjects;
for (int i = 0 ; i < numberObjects; i++) {
OsiObject * obj = objects[i];
CbcSimpleInteger * intObj = dynamic_cast<CbcSimpleInteger *> (obj);
if (intObj) {
objects_[i] = intObj->osiObject();
}
else {
CbcSOS * sosObj = dynamic_cast<CbcSOS *>(obj);
if (sosObj) objects_[i] = sosObj->osiObject(model_.solver());
else {//Maybe an unsupported CbcObject
CbcObject * cbcObj = dynamic_cast<CbcObject *>(obj);
if (cbcObj) {
std::cerr<<"Unsupported CbcObject appears in the code"<<std::endl;
throw UNSUPPORTED_CBC_OBJECT;
}
else {//It has to be an OsiObject.
objects_[i]=obj->clone();
}
}
}
}
CbcCutGenerator ** gen = model_.cutGenerators();
int numGen = model_.numberCutGenerators();
for (int i = 0 ; i < numGen ; i++) {
Bonmin::OaDecompositionBase * oa = dynamic_cast<Bonmin::OaDecompositionBase * >(gen[i]->generator());
// if (oa)
// printf ("\n\n\nat least one OADecompBase\n\n\n");
if (oa) // pass objects
oa->setObjects(objects_,nObjects_);
}
}
// if (objects_) {
// for (int i = 0 ; i < nObjects_; i++)
// delete objects_ [i];
// delete [] objects_;
// }
// OsiObject ** objects = model_.objects();
// int numObjects = model_.numberObjects();
// nObjects_ = 0;
// objects_ = new OsiObject* [numObjects];
// for (int i=0; i < numObjects; ++i)
// if (objects [i])
// objects_ [nObjects_++] = objects [i] -> clone ();
try {
//Get the time and start.
{
OsiTMINLPInterface * tmpOsi = NULL;
if(s.nonlinearSolver() == s.continuousSolver()){
tmpOsi = dynamic_cast<OsiTMINLPInterface *> (model_.solver());
tmpOsi->forceSolverOutput(s.getIntParameter(Bonmin::BabSetupBase::RootLogLevel));
}
model_.initialSolve();
if(tmpOsi != NULL){
tmpOsi->setSolverOutputToDefault();
}
}
int ival;
s.options()->GetEnumValue("enable_dynamic_nlp", ival, "bonmin.");
if(s.nonlinearSolver() == s.continuousSolver() && ival) {
if(!model_.solver()->isProvenOptimal() ){//Something went wrong check if objective is linear and alternate model
// can be solved
OsiTMINLPInterface * tmpOsi = dynamic_cast<OsiTMINLPInterface *> (model_.solver());
TMINLPLinObj * tmp_tminlp = dynamic_cast<TMINLPLinObj *> (tmpOsi->model());
tmpOsi->setModel(tmp_tminlp->tminlp());
model_.initialSolve();
}
else {
LinearCutsGenerator cgl;
cgl.initialize(s);
OsiCuts cuts;
cgl.generateCuts(*model_.solver(), cuts);
std::vector<const OsiRowCut *> mycuts(cuts.sizeRowCuts());
for(int i = 0 ; i < cuts.sizeRowCuts() ; i++){
mycuts[i] = cuts.rowCutPtr(i);
}
model_. solver () -> applyRowCuts ((int) mycuts.size(), (const OsiRowCut **) &mycuts[0]);
}
//Added by Claudia
OsiTMINLPInterface * nlpSolver = dynamic_cast<OsiTMINLPInterface *>(model_.solver());
if(nlpSolver && nlpSolver->getNewCutoffDecr()!=COIN_DBL_MAX)
model_.setDblParam(CbcModel::CbcCutoffIncrement, nlpSolver->getNewCutoffDecr());
model_.solver()->resolve();
}
// for Couenne
model_.passInSolverCharacteristics (bonBabInfoPtr);
continuousRelaxation_ =model_.solver()->getObjValue();
if (specOpt==16)//Set warm start point for Ipopt
{
#if 1
const double * colsol = model_.solver()->getColSolution();
const double * duals = model_.solver()->getRowPrice();
OsiTMINLPInterface * tnlpSolver = dynamic_cast<OsiTMINLPInterface *>(model_.solver());
// Primal dual point is not copied if one (supposedly a better one) has already been put into the solver.
if(tnlpSolver->problem()->has_x_init() != 2){
model_.solver()->setColSolution(colsol);
model_.solver()->setRowPrice(duals);
}
#else
OsiTMINLPInterface * tnlpSolver = dynamic_cast<OsiTMINLPInterface *>(model_.solver());
CoinWarmStart * warm = tnlpSolver->solver()->getWarmStart(tnlpSolver->problem());
tnlpSolver->solver()->setWarmStart(warm, tnlpSolver->problem());
delete warm;
#endif
#if 0 // Sometimes primal dual point is problematic in the context of Cut-and-branch
model_.solver()->resolve();
if(!model_.solver()->isProvenOptimal())
model_.solver()->setColSolution(NULL);
#endif
}
#ifdef SIGNAL
CoinSighandler_t saveSignal = SIG_DFL;
// register signal handler
saveSignal = signal (SIGINT,couenne_signal_handler);
currentBranchModel = &model_;
#endif
// to get node parent info in Cbc, pass parameter 3.
//model_.branchAndBound(3);
remaining_time -= CoinCpuTime();
model_.setDblParam(CbcModel::CbcMaximumSeconds, remaining_time);
if(remaining_time > 0.)
model_.branchAndBound();
}
catch(TNLPSolver::UnsolvedError *E){
s.nonlinearSolver()->model()->finalize_solution
(TMINLP::MINLP_ERROR, 0, NULL, DBL_MAX);
throw E;
}
numNodes_ = model_.getNodeCount();
bestObj_ = model_.getObjValue();
bestBound_ = model_.getBestPossibleObjValue();
mipIterationCount_ = model_.getIterationCount();
bool hasFailed = false;
if (specOpt==16)//Did we continue branching on a failure
{
CbcNlpStrategy * nlpStrategy = dynamic_cast<CbcNlpStrategy *>(model_.strategy());
if (nlpStrategy)
hasFailed = nlpStrategy->hasFailed();
else
throw -1;
}
else
hasFailed = s.nonlinearSolver()->hasContinuedOnAFailure();
// Output summarizing cut generators (taken from CbcSolver.cpp)
// ToDo put into proper print level
int numberGenerators = model_.numberCutGenerators();
for (int iGenerator=0;iGenerator<numberGenerators;iGenerator++) {
CbcCutGenerator * generator = model_.cutGenerator(iGenerator);
//CglStored * stored = dynamic_cast<CglStored*>(generator->generator());
if (true&&!(generator->numberCutsInTotal() || generator->numberColumnCuts()))
continue;
if(modelHandler_->logLevel() >= 1) {
*modelHandler_ << generator->cutGeneratorName()
<< "was tried" << generator->numberTimesEntered()
<< "times and created" << generator->numberCutsInTotal()+generator->numberColumnCuts()
<< "cuts of which" << generator->numberCutsActive()
<< "were active after adding rounds of cuts";
// if (generator->timing()) {
// char timebuf[20];
// sprintf(timebuf, "(%.3fs)", generator->timeInCutGenerator());
// *modelHandler_ << timebuf << CoinMessageEol;
// }
// else {
// *modelHandler_ << CoinMessageEol;
// }
}
}
TMINLP::SolverReturn status = TMINLP::MINLP_ERROR;
if (model_.numberObjects()==0) {
if (bestSolution_)
delete [] bestSolution_;
OsiSolverInterface * solver =
(s.nonlinearSolver() == s.continuousSolver())?
model_.solver() : s.nonlinearSolver();
bestSolution_ = new double[solver->getNumCols()];
CoinCopyN(solver->getColSolution(), solver->getNumCols(),
bestSolution_);
bestObj_ = bestBound_ = solver->getObjValue();
}
if (bonBabInfoPtr->bestSolution2().size() > 0) {
assert((int) bonBabInfoPtr->bestSolution2().size() == s.nonlinearSolver()->getNumCols());
if (bestSolution_)
delete [] bestSolution_;
bestSolution_ = new double[s.nonlinearSolver()->getNumCols()];
std::copy(bonBabInfoPtr->bestSolution2().begin(), bonBabInfoPtr->bestSolution2().end(),
bestSolution_);
bestObj_ = (bonBabInfoPtr->bestObj2());
(*s.nonlinearSolver()->messageHandler())<<"\nReal objective function: "
<<bestObj_<<CoinMessageEol;
}
else if (model_.bestSolution()) {
if (bestSolution_)
delete [] bestSolution_;
bestSolution_ = new double[s.nonlinearSolver()->getNumCols()];
CoinCopyN(model_.bestSolution(), s.nonlinearSolver()->getNumCols(), bestSolution_);
}
if(remaining_time <= 0.){
status = TMINLP::LIMIT_EXCEEDED;
if (bestSolution_) {
mipStatus_ = Feasible;
}
else {
mipStatus_ = NoSolutionKnown;
}
}
else if (model_.status() == 0) {
if(model_.isContinuousUnbounded()){
status = TMINLP::CONTINUOUS_UNBOUNDED;
mipStatus_ = UnboundedOrInfeasible;
}
else
if (bestSolution_) {
status = TMINLP::SUCCESS;
mipStatus_ = FeasibleOptimal;
}
else {
status = TMINLP::INFEASIBLE;
mipStatus_ = ProvenInfeasible;
}
}
else if (model_.status() == 1 || model_.status() == 5) {
#if (BONMIN_VERSION_MAJOR > 1) || (BONMIN_VERSION_MINOR > 6)
status = model_.status() == 1 ? TMINLP::LIMIT_EXCEEDED : TMINLP::USER_INTERRUPT;
#else
status = TMINLP::LIMIT_EXCEEDED;
#endif
if (bestSolution_) {
mipStatus_ = Feasible;
}
else {
mipStatus_ = NoSolutionKnown;
}
}
else if (model_.status()==2) {
status = TMINLP::MINLP_ERROR;
}
// Which solution should we use? false if RBS's, true if Cbc's
bool use_RBS_Cbc =
!problem_ ||
!(problem_ -> getRecordBestSol ()) ||
!(problem_ -> getRecordBestSol () -> getHasSol()) ||
(((fabs (bestObj_) < COUENNE_INFINITY / 1e4) &&
(problem_ -> getRecordBestSol () -> getVal () > bestObj_)));
/* if we do not pass the cbc solution and problem_ -> getRecordBestSol () -> getHasSol() is true, then there should be a solution vector in problem_ -> getRecordBestSol () */
assert(use_RBS_Cbc || problem_ -> getRecordBestSol () -> getSol() != NULL);
s.nonlinearSolver () -> model () -> finalize_solution
(status,
s.nonlinearSolver () -> getNumCols (),
use_RBS_Cbc ? bestSolution_ : problem_ -> getRecordBestSol () -> getSol (),
use_RBS_Cbc ? bestObj_ : problem_ -> getRecordBestSol () -> getVal ());
}
void
CglResidualCapacityUnitTest(const OsiSolverInterface *baseSiP,
const std::string mpsDir)
{
// Test default constructor
{
CglResidualCapacity aGenerator;
}
// Test copy & assignment
{
CglResidualCapacity rhs;
{
CglResidualCapacity bGenerator;
CglResidualCapacity cGenerator(bGenerator);
rhs=bGenerator;
}
}
// Test get/set methods
{
CglResidualCapacity getset;
double geps = 10 * getset.getEpsilon();
getset.setEpsilon(geps);
double geps2 = getset.getEpsilon();
assert(geps == geps2);
double gtol = 10 * getset.getTolerance();
getset.setTolerance(gtol);
double gtol2 = getset.getTolerance();
assert(gtol == gtol2);
int gpre = getset.getDoPreproc();
gpre = (gpre + 1) % 3 - 1;
getset.setDoPreproc(gpre);
int gpre2 = getset.getDoPreproc();
assert(gpre == gpre2);
}
// Test generateCuts
{
CglResidualCapacity gct;
OsiSolverInterface *siP = baseSiP->clone();
std::string fn = mpsDir+"capPlan1";
std::string fn2 = mpsDir+"capPlan1.mps";
FILE *in_f = fopen(fn2.c_str(), "r");
if(in_f == NULL) {
std::cout<<"Can not open file "<<fn2<<std::endl<<"Skip test of CglResidualCapacity::generateCuts()"<<std::endl;
}
else {
fclose(in_f);
siP->readMps(fn.c_str(),"mps");
siP->initialSolve();
double lpRelax = siP->getObjValue();
OsiCuts cs;
gct.setDoPreproc(1); // Needed for DyLP
gct.generateCuts(*siP, cs);
int nRowCuts = cs.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" Residual Capacity cuts"<<std::endl;
assert(cs.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cs);
siP->resolve();
double lpRelaxAfter= siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelax<<std::endl;
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
assert( lpRelax < lpRelaxAfter );
assert(lpRelaxAfter < 964);
}
delete siP;
}
}
//#############################################################################
void
MibSHeuristic::objCutHeuristic()
{
/* Solve the LP relaxation with the new constraint d^2 y <= d^y* */
MibSModel * model = MibSModel_;
//OsiSolverInterface * oSolver = model->origLpSolver_;
OsiSolverInterface * oSolver = model->getSolver();
//OsiSolverInterface * hSolver = new OsiCbcSolverInterface();
OsiSolverInterface * hSolver = new OsiSymSolverInterface();
double objSense(model->getLowerObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int tCols(lCols + uCols);
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double * lObjCoeffs = model->getLowerObjCoeffs();
hSolver->loadProblem(*oSolver->getMatrixByCol(),
oSolver->getColLower(), oSolver->getColUpper(),
oSolver->getObjCoefficients(),
oSolver->getRowLower(), oSolver->getRowUpper());
int j(0);
for(j = 0; j < tCols; j++){
if(oSolver->isInteger(j))
hSolver->setInteger(j);
}
double * optLowerSolutionOrd = model->bS_->optLowerSolutionOrd_;
CoinPackedVector objCon;
int i(0), index(0);
double rhs(0.0);
for(i = 0; i < lCols; i++){
index = lColIndices[i];
objCon.insert(index, lObjCoeffs[i] * objSense);
//should this be ordered? and should lObjCoeffs by at index?
//rhs += optLowerSolutionOrd_[i] * lObjCoeffs[i] * objSense;
rhs += optLowerSolutionOrd[i] * lObjCoeffs[i] * objSense;
}
//Hmm, I think this was wrong before...?
// hSolver->addRow(objCon, - hSolver->getInfinity(), rhs);
hSolver->addRow(objCon, rhs, hSolver->getInfinity());
/* optimize w.r.t. to the UL objective with the new row */
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(hSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(hSolver)->setSymParam("max_active_nodes", 1);
}
hSolver->branchAndBound();
if(0)
hSolver->writeLp("objcutheuristic");
if(hSolver->isProvenOptimal()){
MibSSolution *mibSol = NULL;
OsiSolverInterface * lSolver = model->bS_->setUpModel(hSolver, true);
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("max_active_nodes", 1);
}
lSolver->branchAndBound();
const double * sol = hSolver->getColSolution();
double objVal(lSolver->getObjValue() * objSense);
double etol(etol_);
double lowerObj = getLowerObj(sol, objSense);
double * optUpperSolutionOrd = new double[uCols];
double * optLowerSolutionOrd = new double[lCols];
CoinZeroN(optUpperSolutionOrd, uCols);
CoinZeroN(optLowerSolutionOrd, lCols);
if(fabs(objVal - lowerObj) < etol){
/** Current solution is bilevel feasible **/
mibSol = new MibSSolution(hSolver->getNumCols(),
hSolver->getColSolution(),
hSolver->getObjValue(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
else{
/* solution is not bilevel feasible, create one that is */
const double * uSol = hSolver->getColSolution();
const double * lSol = lSolver->getColSolution();
//int numElements(lSolver->getNumCols());
int numElements(hSolver->getNumCols());
int i(0), pos(0), index(0);
double * lpSolution = new double[numElements];
double upperObj(0.0);
//FIXME: problem is still here. indices may be wrong.
//also is all this necessary, or can we just paste together uSol and lSol?
for(i = 0; i < numElements; i++){
//index = indices[i];
pos = model->bS_->binarySearch(0, lCols - 1, i, lColIndices);
if(pos < 0){
pos = model->bS_->binarySearch(0, uCols - 1, i, uColIndices);
//optUpperSolutionOrd[pos] = values[i];
//optUpperSolutionOrd[pos] = uSol[pos];
if (pos >= 0){
optUpperSolutionOrd[pos] = uSol[i];
}
}
else{
//optLowerSolutionOrd[pos] = lSol[i];
optLowerSolutionOrd[pos] = lSol[pos];
}
}
for(i = 0; i < uCols; i++){
index = uColIndices[i];
lpSolution[index] = optUpperSolutionOrd[i];
upperObj +=
optUpperSolutionOrd[i] * hSolver->getObjCoefficients()[index];
}
for(i = 0; i < lCols; i++){
index = lColIndices[i];
lpSolution[index] = optLowerSolutionOrd[i];
upperObj +=
optLowerSolutionOrd[i] * hSolver->getObjCoefficients()[index];
}
if(model->checkUpperFeasibility(lpSolution)){
mibSol = new MibSSolution(hSolver->getNumCols(),
lpSolution,
upperObj * hSolver->getObjSense(),
model);
model->storeSolution(BlisSolutionTypeHeuristic, mibSol);
mibSol = NULL;
}
delete [] lpSolution;
}
delete lSolver;
}
delete hSolver;
}
void
CglCliqueUnitTest(const OsiSolverInterface *baseSiP,
const std::string mpsDir)
{
// Test default constructor
{
CglClique aGenerator;
}
// Test copy & assignment
{
CglClique rhs;
{
CglClique bGenerator;
CglClique cGenerator(bGenerator);
//rhs=bGenerator;
}
}
// Test get/set methods
{
CglClique getset;
// None to test
}
// Test generateCuts
{
CglClique gct;
OsiSolverInterface *siP = baseSiP->clone();
std::string fn = mpsDir+"l152lav";
std::string fn2 = mpsDir+"l152lav.mps";
FILE *in_f = fopen(fn2.c_str(), "r");
if(in_f == NULL) {
std::cout<<"Can not open file "<<fn2<<std::endl<<"Skip test of CglClique::generateCuts()"<<std::endl;
}
else {
fclose(in_f);
siP->readMps(fn.c_str(),"mps");
siP->initialSolve();
double lpRelax = siP->getObjValue();
OsiCuts cs;
gct.generateCuts(*siP, cs);
int nRowCuts = cs.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" Clique cuts"<<std::endl;
assert(cs.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cs);
siP->resolve();
double lpRelaxAfter= siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelax<<std::endl;
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
assert( lpRelax < lpRelaxAfter );
assert(lpRelaxAfter < 4722.1);
}
delete siP;
}
}
//#############################################################################
bfSol*
MibSHeuristic::getBilevelSolution(const double * sol, double origLower)
{
/*
Find a bilevel feasible solution by solving the LL problem
for a fixed UL solution, given by the UL portion of sol
*/
MibSModel * model = MibSModel_;
OsiSolverInterface * oSolver = model->getSolver();
OsiSolverInterface * lSolver = model->bS_->setUpModel(oSolver, true, sol);
//double uObjSense(model->getSolver()->getObjSense());
int lCols(model->getLowerDim());
int uCols(model->getUpperDim());
int * lColIndices = model->getLowerColInd();
int * uColIndices = model->getUpperColInd();
double etol(etol_);
int tCols(uCols + lCols);
int i(0);
if(0){
lSolver->writeLp("bilevelsolver");
std::cout
<< "Original Lower-level Solution Value: " << origLower << std::endl;
for(i = 0; i < lCols; i++){
std::cout << "lowsol[" << i << "]: " << sol[lColIndices[i]] << std::endl;
}
}
if(0){
dynamic_cast<OsiCbcSolverInterface *>
(lSolver)->getModelPtr()->messageHandler()->setLogLevel(0);
}
else{
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("prep_level", -1);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("verbosity", -2);
dynamic_cast<OsiSymSolverInterface *>
(lSolver)->setSymParam("max_active_nodes", 1);
}
lSolver->branchAndBound();
if(lSolver->isProvenOptimal()){
double objVal(0.0);
double lowerObj(lSolver->getObjValue());
double * colsol = new double[tCols];
for(i = 0; i < uCols; i++){
colsol[uColIndices[i]] = sol[uColIndices[i]];
}
if(0){
std::cout << "candidate lower solution value: " << origLower << std::endl;
std::cout << "actual lower solution value: " << lowerObj << std::endl;
}
if(fabs(origLower - lowerObj) < etol){
//original solution was bilevel feasible
if(0)
std::cout << "Original solution was bilevel feasible:" << std::endl;
for(i = 0; i < lCols; i++){
if(0){
std::cout << "lowerportion["
<< i << "]: " << sol[lColIndices[i]] << std::endl;
}
colsol[lColIndices[i]] = sol[lColIndices[i]];
}
}
else{
if(0){
std::cout << "Not bilevel feasible." << std::endl;
}
for(i = 0; i < lCols; i++){
if(0){
std::cout << "newportion["
<< i << "]: " << lSolver->getColSolution()[i] << std::endl;
}
colsol[lColIndices[i]] = lSolver->getColSolution()[i];
}
}
for(i = 0; i < tCols; i++)
objVal += colsol[i] * oSolver->getObjCoefficients()[i];
bfSol * bfsol =
new bfSol(objVal, colsol);
delete lSolver;
return bfsol;
}
else{
delete lSolver;
return NULL;
}
}
void
CglTwomirUnitTest(const OsiSolverInterface *baseSiP,
const std::string mpsDir)
{
// Test default constructor
{
CglTwomir aGenerator;
}
// Test copy & assignment
{
CglTwomir rhs;
{
CglTwomir bGenerator;
CglTwomir cGenerator(bGenerator);
rhs=bGenerator;
}
}
// Test get/set methods
{
CglTwomir getset;
int gtmin = getset.getTmin() + 1;
int gtmax = getset.getTmax() + 1;
getset.setMirScale(gtmin, gtmax);
double gtmin2 = getset.getTmin();
double gtmax2 = getset.getTmax();
assert(gtmin == gtmin2);
assert(gtmax == gtmax2);
int gamax = 2 * getset.getAmax() + 1;
getset.setAMax(gamax);
int gamax2 = getset.getAmax();
assert(gamax == gamax2);
}
// Test generateCuts
{
CglTwomir gct;
OsiSolverInterface *siP = baseSiP->clone();
std::string fn = mpsDir+"capPlan1";
std::string fn2 = mpsDir+"capPlan1.mps";
FILE *in_f = fopen(fn2.c_str(), "r");
if(in_f == NULL) {
std::cout<<"Can not open file "<<fn2<<std::endl<<"Skip test of CglTwomir::generateCuts()"<<std::endl;
}
else {
fclose(in_f);
siP->readMps(fn.c_str(),"mps");
siP->initialSolve();
double lpRelax = siP->getObjValue();
OsiCuts cs;
gct.generateCuts(*siP, cs);
int nRowCuts = cs.sizeRowCuts();
std::cout<<"There are "<<nRowCuts<<" Twomir cuts"<<std::endl;
assert(cs.sizeRowCuts() > 0);
OsiSolverInterface::ApplyCutsReturnCode rc = siP->applyCuts(cs);
siP->resolve();
double lpRelaxAfter= siP->getObjValue();
std::cout<<"Initial LP value: "<<lpRelax<<std::endl;
std::cout<<"LP value with cuts: "<<lpRelaxAfter<<std::endl;
assert( lpRelax < lpRelaxAfter );
assert(lpRelaxAfter < 964);
}
delete siP;
}
}
// inner part of dive
int
CbcHeuristicDive::solution(double & solutionValue, int & numberNodes,
int & numberCuts, OsiRowCut ** cuts,
CbcSubProblem ** & nodes,
double * newSolution)
{
#ifdef DIVE_DEBUG
int nRoundInfeasible = 0;
int nRoundFeasible = 0;
#endif
int reasonToStop = 0;
double time1 = CoinCpuTime();
int numberSimplexIterations = 0;
int maxSimplexIterations = (model_->getNodeCount()) ? maxSimplexIterations_
: maxSimplexIterationsAtRoot_;
// but can't be exactly coin_int_max
maxSimplexIterations = CoinMin(maxSimplexIterations,COIN_INT_MAX>>3);
OsiSolverInterface * solver = cloneBut(6); // was model_->solver()->clone();
# ifdef COIN_HAS_CLP
OsiClpSolverInterface * clpSolver
= dynamic_cast<OsiClpSolverInterface *> (solver);
if (clpSolver) {
ClpSimplex * clpSimplex = clpSolver->getModelPtr();
int oneSolveIts = clpSimplex->maximumIterations();
oneSolveIts = CoinMin(1000+2*(clpSimplex->numberRows()+clpSimplex->numberColumns()),oneSolveIts);
clpSimplex->setMaximumIterations(oneSolveIts);
if (!nodes) {
// say give up easily
clpSimplex->setMoreSpecialOptions(clpSimplex->moreSpecialOptions() | 64);
} else {
// get ray
int specialOptions = clpSimplex->specialOptions();
specialOptions &= ~0x3100000;
specialOptions |= 32;
clpSimplex->setSpecialOptions(specialOptions);
clpSolver->setSpecialOptions(clpSolver->specialOptions() | 1048576);
if ((model_->moreSpecialOptions()&16777216)!=0) {
// cutoff is constraint
clpSolver->setDblParam(OsiDualObjectiveLimit, COIN_DBL_MAX);
}
}
}
# endif
const double * lower = solver->getColLower();
const double * upper = solver->getColUpper();
const double * rowLower = solver->getRowLower();
const double * rowUpper = solver->getRowUpper();
const double * solution = solver->getColSolution();
const double * objective = solver->getObjCoefficients();
double integerTolerance = model_->getDblParam(CbcModel::CbcIntegerTolerance);
double primalTolerance;
solver->getDblParam(OsiPrimalTolerance, primalTolerance);
int numberRows = matrix_.getNumRows();
assert (numberRows <= solver->getNumRows());
int numberIntegers = model_->numberIntegers();
const int * integerVariable = model_->integerVariable();
double direction = solver->getObjSense(); // 1 for min, -1 for max
double newSolutionValue = direction * solver->getObjValue();
int returnCode = 0;
// Column copy
const double * element = matrix_.getElements();
const int * row = matrix_.getIndices();
const CoinBigIndex * columnStart = matrix_.getVectorStarts();
const int * columnLength = matrix_.getVectorLengths();
#ifdef DIVE_FIX_BINARY_VARIABLES
// Row copy
const double * elementByRow = matrixByRow_.getElements();
const int * column = matrixByRow_.getIndices();
const CoinBigIndex * rowStart = matrixByRow_.getVectorStarts();
const int * rowLength = matrixByRow_.getVectorLengths();
#endif
// Get solution array for heuristic solution
int numberColumns = solver->getNumCols();
memcpy(newSolution, solution, numberColumns*sizeof(double));
// vectors to store the latest variables fixed at their bounds
int* columnFixed = new int [numberIntegers];
double* originalBound = new double [numberIntegers+2*numberColumns];
double * lowerBefore = originalBound+numberIntegers;
double * upperBefore = lowerBefore+numberColumns;
memcpy(lowerBefore,lower,numberColumns*sizeof(double));
memcpy(upperBefore,upper,numberColumns*sizeof(double));
double * lastDjs=newSolution+numberColumns;
bool * fixedAtLowerBound = new bool [numberIntegers];
PseudoReducedCost * candidate = new PseudoReducedCost [numberIntegers];
double * random = new double [numberIntegers];
int maxNumberAtBoundToFix = static_cast<int> (floor(percentageToFix_ * numberIntegers));
assert (!maxNumberAtBoundToFix||!nodes);
// count how many fractional variables
int numberFractionalVariables = 0;
for (int i = 0; i < numberIntegers; i++) {
random[i] = randomNumberGenerator_.randomDouble() + 0.3;
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
numberFractionalVariables++;
}
}
const double* reducedCost = NULL;
// See if not NLP
if (model_->solverCharacteristics()->reducedCostsAccurate())
reducedCost = solver->getReducedCost();
int iteration = 0;
while (numberFractionalVariables) {
iteration++;
// initialize any data
initializeData();
// select a fractional variable to bound
int bestColumn = -1;
int bestRound; // -1 rounds down, +1 rounds up
bool canRound = selectVariableToBranch(solver, newSolution,
bestColumn, bestRound);
// if the solution is not trivially roundable, we don't try to round;
// if the solution is trivially roundable, we try to round. However,
// if the rounded solution is worse than the current incumbent,
// then we don't round and proceed normally. In this case, the
// bestColumn will be a trivially roundable variable
if (canRound) {
// check if by rounding all fractional variables
// we get a solution with an objective value
// better than the current best integer solution
double delta = 0.0;
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
assert(downLocks_[i] == 0 || upLocks_[i] == 0);
double obj = objective[iColumn];
if (downLocks_[i] == 0 && upLocks_[i] == 0) {
if (direction * obj >= 0.0)
delta += (floor(value) - value) * obj;
else
delta += (ceil(value) - value) * obj;
} else if (downLocks_[i] == 0)
delta += (floor(value) - value) * obj;
else
delta += (ceil(value) - value) * obj;
}
}
if (direction*(solver->getObjValue() + delta) < solutionValue) {
#ifdef DIVE_DEBUG
nRoundFeasible++;
#endif
if (!nodes||bestColumn<0) {
// Round all the fractional variables
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
assert(downLocks_[i] == 0 || upLocks_[i] == 0);
if (downLocks_[i] == 0 && upLocks_[i] == 0) {
if (direction * objective[iColumn] >= 0.0)
newSolution[iColumn] = floor(value);
else
newSolution[iColumn] = ceil(value);
} else if (downLocks_[i] == 0)
newSolution[iColumn] = floor(value);
else
newSolution[iColumn] = ceil(value);
}
}
break;
} else {
// can't round if going to use in branching
int i;
for (i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[bestColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
if (iColumn==bestColumn) {
assert(downLocks_[i] == 0 || upLocks_[i] == 0);
double obj = objective[bestColumn];
if (downLocks_[i] == 0 && upLocks_[i] == 0) {
if (direction * obj >= 0.0)
bestRound=-1;
else
bestRound=1;
} else if (downLocks_[i] == 0)
bestRound=-1;
else
bestRound=1;
break;
}
}
}
}
}
#ifdef DIVE_DEBUG
else
nRoundInfeasible++;
#endif
}
// do reduced cost fixing
#ifdef DIVE_DEBUG
int numberFixed = reducedCostFix(solver);
std::cout << "numberReducedCostFixed = " << numberFixed << std::endl;
#else
reducedCostFix(solver);
#endif
int numberAtBoundFixed = 0;
#ifdef DIVE_FIX_BINARY_VARIABLES
// fix binary variables based on pseudo reduced cost
if (binVarIndex_.size()) {
int cnt = 0;
int n = static_cast<int>(binVarIndex_.size());
for (int j = 0; j < n; j++) {
int iColumn1 = binVarIndex_[j];
double value = newSolution[iColumn1];
if (fabs(value) <= integerTolerance &&
lower[iColumn1] != upper[iColumn1]) {
double maxPseudoReducedCost = 0.0;
#ifdef DIVE_DEBUG
std::cout << "iColumn1 = " << iColumn1 << ", value = " << value << std::endl;
#endif
int iRow = vbRowIndex_[j];
double chosenValue = 0.0;
for (int k = rowStart[iRow]; k < rowStart[iRow] + rowLength[iRow]; k++) {
int iColumn2 = column[k];
#ifdef DIVE_DEBUG
std::cout << "iColumn2 = " << iColumn2 << std::endl;
#endif
if (iColumn1 != iColumn2) {
double pseudoReducedCost = fabs(reducedCost[iColumn2] *
elementByRow[k]);
#ifdef DIVE_DEBUG
int k2;
for (k2 = rowStart[iRow]; k2 < rowStart[iRow] + rowLength[iRow]; k2++) {
if (column[k2] == iColumn1)
break;
}
std::cout << "reducedCost[" << iColumn2 << "] = "
<< reducedCost[iColumn2]
<< ", elementByRow[" << iColumn2 << "] = " << elementByRow[k]
<< ", elementByRow[" << iColumn1 << "] = " << elementByRow[k2]
<< ", pseudoRedCost = " << pseudoReducedCost
<< std::endl;
#endif
if (pseudoReducedCost > maxPseudoReducedCost)
maxPseudoReducedCost = pseudoReducedCost;
} else {
// save value
chosenValue = fabs(elementByRow[k]);
}
}
assert (chosenValue);
maxPseudoReducedCost /= chosenValue;
#ifdef DIVE_DEBUG
std::cout << ", maxPseudoRedCost = " << maxPseudoReducedCost << std::endl;
#endif
candidate[cnt].var = iColumn1;
candidate[cnt++].pseudoRedCost = maxPseudoReducedCost;
}
}
#ifdef DIVE_DEBUG
std::cout << "candidates for rounding = " << cnt << std::endl;
#endif
std::sort(candidate, candidate + cnt, compareBinaryVars);
for (int i = 0; i < cnt; i++) {
int iColumn = candidate[i].var;
if (numberAtBoundFixed < maxNumberAtBoundToFix) {
columnFixed[numberAtBoundFixed] = iColumn;
originalBound[numberAtBoundFixed] = upper[iColumn];
fixedAtLowerBound[numberAtBoundFixed] = true;
solver->setColUpper(iColumn, lower[iColumn]);
numberAtBoundFixed++;
if (numberAtBoundFixed == maxNumberAtBoundToFix)
break;
}
}
}
#endif
// fix other integer variables that are at their bounds
int cnt = 0;
#ifdef GAP
double gap = 1.0e30;
#endif
if (reducedCost && true) {
#ifndef JJF_ONE
cnt = fixOtherVariables(solver, solution, candidate, random);
#else
#ifdef GAP
double cutoff = model_->getCutoff() ;
if (cutoff < 1.0e20 && false) {
double direction = solver->getObjSense() ;
gap = cutoff - solver->getObjValue() * direction ;
gap *= 0.1; // Fix more if plausible
double tolerance;
solver->getDblParam(OsiDualTolerance, tolerance) ;
if (gap <= 0.0)
gap = tolerance;
gap += 100.0 * tolerance;
}
int nOverGap = 0;
#endif
int numberFree = 0;
int numberFixed = 0;
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
if (upper[iColumn] > lower[iColumn]) {
numberFree++;
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) <= integerTolerance) {
candidate[cnt].var = iColumn;
candidate[cnt++].pseudoRedCost =
fabs(reducedCost[iColumn] * random[i]);
#ifdef GAP
if (fabs(reducedCost[iColumn]) > gap)
nOverGap++;
#endif
}
} else {
numberFixed++;
}
}
#ifdef GAP
int nLeft = maxNumberAtBoundToFix - numberAtBoundFixed;
#ifdef CLP_INVESTIGATE4
printf("cutoff %g obj %g nover %d - %d free, %d fixed\n",
cutoff, solver->getObjValue(), nOverGap, numberFree, numberFixed);
#endif
if (nOverGap > nLeft && true) {
nOverGap = CoinMin(nOverGap, nLeft + maxNumberAtBoundToFix / 2);
maxNumberAtBoundToFix += nOverGap - nLeft;
}
#else
#ifdef CLP_INVESTIGATE4
printf("cutoff %g obj %g - %d free, %d fixed\n",
model_->getCutoff(), solver->getObjValue(), numberFree, numberFixed);
#endif
#endif
#endif
} else {
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
if (upper[iColumn] > lower[iColumn]) {
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) <= integerTolerance) {
candidate[cnt].var = iColumn;
candidate[cnt++].pseudoRedCost = numberIntegers - i;
}
}
}
}
std::sort(candidate, candidate + cnt, compareBinaryVars);
for (int i = 0; i < cnt; i++) {
int iColumn = candidate[i].var;
if (upper[iColumn] > lower[iColumn]) {
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) <= integerTolerance &&
numberAtBoundFixed < maxNumberAtBoundToFix) {
// fix the variable at one of its bounds
if (fabs(lower[iColumn] - value) <= integerTolerance) {
columnFixed[numberAtBoundFixed] = iColumn;
originalBound[numberAtBoundFixed] = upper[iColumn];
fixedAtLowerBound[numberAtBoundFixed] = true;
solver->setColUpper(iColumn, lower[iColumn]);
numberAtBoundFixed++;
} else if (fabs(upper[iColumn] - value) <= integerTolerance) {
columnFixed[numberAtBoundFixed] = iColumn;
originalBound[numberAtBoundFixed] = lower[iColumn];
fixedAtLowerBound[numberAtBoundFixed] = false;
solver->setColLower(iColumn, upper[iColumn]);
numberAtBoundFixed++;
}
if (numberAtBoundFixed == maxNumberAtBoundToFix)
break;
}
}
}
#ifdef DIVE_DEBUG
std::cout << "numberAtBoundFixed = " << numberAtBoundFixed << std::endl;
#endif
double originalBoundBestColumn;
double bestColumnValue;
int whichWay;
if (bestColumn >= 0) {
bestColumnValue = newSolution[bestColumn];
if (bestRound < 0) {
originalBoundBestColumn = upper[bestColumn];
solver->setColUpper(bestColumn, floor(bestColumnValue));
whichWay=0;
} else {
originalBoundBestColumn = lower[bestColumn];
solver->setColLower(bestColumn, ceil(bestColumnValue));
whichWay=1;
}
} else {
break;
}
int originalBestRound = bestRound;
int saveModelOptions = model_->specialOptions();
while (1) {
model_->setSpecialOptions(saveModelOptions | 2048);
solver->resolve();
model_->setSpecialOptions(saveModelOptions);
if (!solver->isAbandoned()&&!solver->isIterationLimitReached()) {
numberSimplexIterations += solver->getIterationCount();
} else {
numberSimplexIterations = maxSimplexIterations + 1;
reasonToStop += 100;
break;
}
if (!solver->isProvenOptimal()) {
if (nodes) {
if (solver->isProvenPrimalInfeasible()) {
if (maxSimplexIterationsAtRoot_!=COIN_INT_MAX) {
// stop now
printf("stopping on first infeasibility\n");
break;
} else if (cuts) {
// can do conflict cut
printf("could do intermediate conflict cut\n");
bool localCut;
OsiRowCut * cut = model_->conflictCut(solver,localCut);
if (cut) {
if (!localCut) {
model_->makePartialCut(cut,solver);
cuts[numberCuts++]=cut;
} else {
delete cut;
}
}
}
} else {
reasonToStop += 10;
break;
}
}
if (numberAtBoundFixed > 0) {
// Remove the bound fix for variables that were at bounds
for (int i = 0; i < numberAtBoundFixed; i++) {
int iColFixed = columnFixed[i];
if (fixedAtLowerBound[i])
solver->setColUpper(iColFixed, originalBound[i]);
else
solver->setColLower(iColFixed, originalBound[i]);
}
numberAtBoundFixed = 0;
} else if (bestRound == originalBestRound) {
bestRound *= (-1);
whichWay |=2;
if (bestRound < 0) {
solver->setColLower(bestColumn, originalBoundBestColumn);
solver->setColUpper(bestColumn, floor(bestColumnValue));
} else {
solver->setColLower(bestColumn, ceil(bestColumnValue));
solver->setColUpper(bestColumn, originalBoundBestColumn);
}
} else
break;
} else
break;
}
if (!solver->isProvenOptimal() ||
direction*solver->getObjValue() >= solutionValue) {
reasonToStop += 1;
} else if (iteration > maxIterations_) {
reasonToStop += 2;
} else if (CoinCpuTime() - time1 > maxTime_) {
reasonToStop += 3;
} else if (numberSimplexIterations > maxSimplexIterations) {
reasonToStop += 4;
// also switch off
#ifdef CLP_INVESTIGATE
printf("switching off diving as too many iterations %d, %d allowed\n",
numberSimplexIterations, maxSimplexIterations);
#endif
when_ = 0;
} else if (solver->getIterationCount() > 1000 && iteration > 3 && !nodes) {
reasonToStop += 5;
// also switch off
#ifdef CLP_INVESTIGATE
printf("switching off diving one iteration took %d iterations (total %d)\n",
solver->getIterationCount(), numberSimplexIterations);
#endif
when_ = 0;
}
memcpy(newSolution, solution, numberColumns*sizeof(double));
numberFractionalVariables = 0;
double sumFractionalVariables=0.0;
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
double away = fabs(floor(value + 0.5) - value);
if (away > integerTolerance) {
numberFractionalVariables++;
sumFractionalVariables += away;
}
}
if (nodes) {
// save information
//branchValues[numberNodes]=bestColumnValue;
//statuses[numberNodes]=whichWay+(bestColumn<<2);
//bases[numberNodes]=solver->getWarmStart();
ClpSimplex * simplex = clpSolver->getModelPtr();
CbcSubProblem * sub =
new CbcSubProblem(clpSolver,lowerBefore,upperBefore,
simplex->statusArray(),numberNodes);
nodes[numberNodes]=sub;
// other stuff
sub->branchValue_=bestColumnValue;
sub->problemStatus_=whichWay;
sub->branchVariable_=bestColumn;
sub->objectiveValue_ = simplex->objectiveValue();
sub->sumInfeasibilities_ = sumFractionalVariables;
sub->numberInfeasibilities_ = numberFractionalVariables;
printf("DiveNode %d column %d way %d bvalue %g obj %g\n",
numberNodes,sub->branchVariable_,sub->problemStatus_,
sub->branchValue_,sub->objectiveValue_);
numberNodes++;
if (solver->isProvenOptimal()) {
memcpy(lastDjs,solver->getReducedCost(),numberColumns*sizeof(double));
memcpy(lowerBefore,lower,numberColumns*sizeof(double));
memcpy(upperBefore,upper,numberColumns*sizeof(double));
}
}
if (!numberFractionalVariables||reasonToStop)
break;
}
if (nodes) {
printf("Exiting dive for reason %d\n",reasonToStop);
if (reasonToStop>1) {
printf("problems in diving\n");
int whichWay=nodes[numberNodes-1]->problemStatus_;
CbcSubProblem * sub;
if ((whichWay&2)==0) {
// leave both ways
sub = new CbcSubProblem(*nodes[numberNodes-1]);
nodes[numberNodes++]=sub;
} else {
sub = nodes[numberNodes-1];
}
if ((whichWay&1)==0)
sub->problemStatus_=whichWay|1;
else
sub->problemStatus_=whichWay&~1;
}
if (!numberNodes) {
// was good at start! - create fake
clpSolver->resolve();
ClpSimplex * simplex = clpSolver->getModelPtr();
CbcSubProblem * sub =
new CbcSubProblem(clpSolver,lowerBefore,upperBefore,
simplex->statusArray(),numberNodes);
nodes[numberNodes]=sub;
// other stuff
sub->branchValue_=0.0;
sub->problemStatus_=0;
sub->branchVariable_=-1;
sub->objectiveValue_ = simplex->objectiveValue();
sub->sumInfeasibilities_ = 0.0;
sub->numberInfeasibilities_ = 0;
printf("DiveNode %d column %d way %d bvalue %g obj %g\n",
numberNodes,sub->branchVariable_,sub->problemStatus_,
sub->branchValue_,sub->objectiveValue_);
numberNodes++;
assert (solver->isProvenOptimal());
}
nodes[numberNodes-1]->problemStatus_ |= 256*reasonToStop;
// use djs as well
if (solver->isProvenPrimalInfeasible()&&cuts) {
// can do conflict cut and re-order
printf("could do final conflict cut\n");
bool localCut;
OsiRowCut * cut = model_->conflictCut(solver,localCut);
if (cut) {
printf("cut - need to use conflict and previous djs\n");
if (!localCut) {
model_->makePartialCut(cut,solver);
cuts[numberCuts++]=cut;
} else {
delete cut;
}
} else {
printf("bad conflict - just use previous djs\n");
}
}
}
// re-compute new solution value
double objOffset = 0.0;
solver->getDblParam(OsiObjOffset, objOffset);
newSolutionValue = -objOffset;
for (int i = 0 ; i < numberColumns ; i++ )
newSolutionValue += objective[i] * newSolution[i];
newSolutionValue *= direction;
//printf("new solution value %g %g\n",newSolutionValue,solutionValue);
if (newSolutionValue < solutionValue && !reasonToStop) {
double * rowActivity = new double[numberRows];
memset(rowActivity, 0, numberRows*sizeof(double));
// paranoid check
memset(rowActivity, 0, numberRows*sizeof(double));
for (int 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 was approximately feasible
bool feasible = true;
for (int i = 0; i < numberRows; i++) {
if (rowActivity[i] < rowLower[i]) {
if (rowActivity[i] < rowLower[i] - 1000.0*primalTolerance)
feasible = false;
} else if (rowActivity[i] > rowUpper[i]) {
if (rowActivity[i] > rowUpper[i] + 1000.0*primalTolerance)
feasible = false;
}
}
for (int i = 0; i < numberIntegers; i++) {
int iColumn = integerVariable[i];
double value = newSolution[iColumn];
if (fabs(floor(value + 0.5) - value) > integerTolerance) {
feasible = false;
break;
}
}
if (feasible) {
// new solution
solutionValue = newSolutionValue;
//printf("** Solution of %g found by CbcHeuristicDive\n",newSolutionValue);
//if (cuts)
//clpSolver->getModelPtr()->writeMps("good8.mps", 2);
returnCode = 1;
} else {
// Can easily happen
//printf("Debug CbcHeuristicDive giving bad solution\n");
}
delete [] rowActivity;
}
#ifdef DIVE_DEBUG
std::cout << "nRoundInfeasible = " << nRoundInfeasible
<< ", nRoundFeasible = " << nRoundFeasible
<< ", returnCode = " << returnCode
<< ", reasonToStop = " << reasonToStop
<< ", simplexIts = " << numberSimplexIterations
<< ", iterations = " << iteration << std::endl;
#endif
delete [] columnFixed;
delete [] originalBound;
delete [] fixedAtLowerBound;
delete [] candidate;
delete [] random;
delete [] downArray_;
downArray_ = NULL;
delete [] upArray_;
upArray_ = NULL;
delete solver;
return returnCode;
}
/// OaDecomposition method
double
OaFeasibilityChecker::performOa(OsiCuts & cs, solverManip &lpManip,
BabInfo * babInfo, double &cutoff,const CglTreeInfo & info) const
{
bool isInteger = true;
bool feasible = 1;
OsiSolverInterface * lp = lpManip.si();
OsiBranchingInformation branch_info(lp,false);
//int numcols = lp->getNumCols();
double milpBound = -COIN_DBL_MAX;
int numberPasses = 0;
double * nlpSol = NULL;
int numberCutsBefore = cs.sizeRowCuts();
while (isInteger && feasible ) {
numberPasses++;
//setup the nlp
//Fix the variable which have to be fixed, after having saved the bounds
double * colsol = const_cast<double *>(lp->getColSolution());
branch_info.solution_ = colsol;
fixIntegers(*nlp_,branch_info, parameters_.cbcIntegerTolerance_,objects_, nObjects_);
//Now solve the NLP get the cuts, and intall them in the local LP
nlp_->resolve(txt_id);
if (post_nlp_solve(babInfo, cutoff)) {
//nlp solved and feasible
// Update the cutoff
double ub = nlp_->getObjValue();
cutoff = ub > 0 ? ub *(1 - parameters_.cbcCutoffIncrement_) : ub*(1 + parameters_.cbcCutoffIncrement_);
// Update the lp solver cutoff
lp->setDblParam(OsiDualObjectiveLimit, cutoff);
}
// Get the cuts outer approximation at the current point
nlpSol = const_cast<double *>(nlp_->getColSolution());
const double * toCut = (parameter().addOnlyViolated_)?
colsol:NULL;
if(cut_count_ <= maximum_oa_cuts_ && type_ == OA)
nlp_->getOuterApproximation(cs, nlpSol, 1, toCut,
true);
else {//if (type_ == Benders)
nlp_->getBendersCut(cs, parameter().global_);
}
if(pol_ == DetectCycles)
nlp_->getBendersCut(savedCuts_, parameter().global_);
int numberCuts = cs.sizeRowCuts() - numberCutsBefore;
cut_count_ += numberCuts;
if (numberCuts > 0)
installCuts(*lp, cs, numberCuts);
lp->resolve();
double objvalue = lp->getObjValue();
//milpBound = max(milpBound, lp->getObjValue());
feasible = (lp->isProvenOptimal() &&
!lp->isDualObjectiveLimitReached() && (objvalue<cutoff)) ;
//if value of integers are unchanged then we have to get out
bool changed = true;//if lp is infeasible we don't have to check anything
isInteger = 0;
// if(!fixed)//fathom on bounds
// milpBound = 1e200;
if (feasible) {
changed = isDifferentOnIntegers(*nlp_, objects_, nObjects_,
0.1,
nlp_->getColSolution(), lp->getColSolution());
}
if (changed) {
branch_info.solution_ = lp->getColSolution();
isInteger = integerFeasible(*lp,branch_info, parameters_.cbcIntegerTolerance_,
objects_, nObjects_);
}
else {
isInteger = 0;
// if(!fixed)//fathom on bounds
milpBound = 1e200;
}
#ifdef OA_DEBUG
printf("Obj value after cuts %g, %d rows\n",lp->getObjValue(),
numberCuts) ;
#endif
}
int num_cuts_now = cs.sizeRowCuts();
if(pol_ == KeepAll){
for(int i = numberCutsBefore ; i < num_cuts_now ; i++){
cs.rowCut(i).setEffectiveness(99.9e99);
}
}
#ifdef OA_DEBUG
debug_.printEndOfProcedureDebugMessage(cs, true, cutoff, milpBound, isInteger, feasible, std::cout);
std::cout<<"milpBound found: "<<milpBound<<std::endl;
#endif
return milpBound;
}
