int main(int argc, char **argv)
{
char *f_name_lp, *last_dot_pos, f_name[256], *f_name_pos;
int i, ncol;
if((argc < 2) || (argc > 2)) {
printf("### ERROR: main(): Usage: One of the following\ncgl_data_test input_file_name.mps\ncgl_data_test input_file_name.lp\n");
exit(1);
}
f_name_lp = strdup(argv[1]);
f_name_pos = strrchr(f_name_lp, '/');
if(f_name_pos != NULL) {
strcpy(f_name, &(f_name_pos[1]));
}
else {
strcpy(f_name, f_name_lp);
}
last_dot_pos = strrchr(f_name, '.');
if(last_dot_pos != NULL) {
last_dot_pos = '\0';
}
OsiClpSolverInterface *clp = new OsiClpSolverInterface;
clp->messageHandler()->setLogLevel(0);
if(strcmp(&(f_name_lp[strlen(f_name_lp)-3]), ".lp") == 0) {
clp->readLp(f_name_lp);
}
else {
if(strcmp(&(f_name_lp[strlen(f_name_lp)-4]), ".mps") == 0) {
clp->readMps(f_name_lp);
}
else {
printf("### ERROR: unrecognized file type\n");
exit(1);
}
}
ncol = clp->getNumCols();
clp->initialSolve();
printf("LP value: %12.2f\n", clp->getObjValue());
OsiCuts cuts;
// Define parameters for CglRedSplit generator
CglParam cpar;
cpar.setMAX_SUPPORT(ncol+1);
CglRedSplitParam rspar(cpar);
// Create a cut generator with the given parameters
CglRedSplit cutGen(rspar);
char *colType = new char[ncol];
for(i=0; i<ncol; i++) {
if(clp->isContinuous(i)) {
colType[i] = 'C';
}
else {
colType[i] = 'I';
}
}
int round, max_rounds = 10;
for(round=0; round<max_rounds; round++) {
cutGen.generateCuts(*clp, cuts);
int ncuts = cuts.sizeRowCuts();
const OsiRowCut **newRowCuts = new const OsiRowCut * [ncuts];
for(i=0; i<ncuts; i++) {
newRowCuts[i] = &cuts.rowCut(i);
}
clp->applyRowCuts(ncuts, newRowCuts);
delete[] newRowCuts;
printf("round %4d: %4d generated cuts new objective value: %12.2f\n",
round, ncuts, clp->getObjValue());
clp->resolve();
if(clp->isAbandoned()) {
printf("###ERROR: Numerical difficulties in Solver\n");
exit(1);
}
if(clp->isProvenPrimalInfeasible()) {
printf("### WARNING: Problem is infeasible\n");
exit(1);
}
}
delete clp;
free(f_name_lp);
delete[] colType;
return(0);
}
/*
Randomized Rounding Heuristic
Returns 1 if solution, 0 if not
*/
int
CbcHeuristicRandRound::solution(double & solutionValue,
double * betterSolution)
{
// rlh: Todo: Memory Cleanup
// std::cout << "Entering the Randomized Rounding Heuristic" << std::endl;
setWhen(1); // setWhen(1) didn't have the effect I expected (e.g., run once).
// Run only once.
//
// See if at root node
bool atRoot = model_->getNodeCount() == 0;
int passNumber = model_->getCurrentPassNumber();
// Just do once
if (!atRoot || passNumber > 1) {
// std::cout << "Leaving the Randomized Rounding Heuristic" << std::endl;
return 0;
}
std::cout << "Entering the Randomized Rounding Heuristic" << std::endl;
typedef struct {
int numberSolutions;
int maximumSolutions;
int numberColumns;
double ** solution;
int * numberUnsatisfied;
} clpSolution;
double start = CoinCpuTime();
numCouldRun_++; //
#ifdef HEURISTIC_INFORM
printf("Entering heuristic %s - nRuns %d numCould %d when %d\n",
heuristicName(),numRuns_,numCouldRun_,when_);
#endif
// Todo: Ask JJHF what "number of times
// the heuristic could run" means.
OsiSolverInterface * solver = model_->solver()->clone();
double primalTolerance ;
solver->getDblParam(OsiPrimalTolerance, primalTolerance) ;
OsiClpSolverInterface * clpSolver = dynamic_cast<OsiClpSolverInterface *> (solver);
assert (clpSolver);
ClpSimplex * simplex = clpSolver->getModelPtr();
// Initialize the structure holding the solutions for the Simplex iterations
clpSolution solutions;
// Set typeStruct field of ClpTrustedData struct to 1 to indicate
// desired behavior for RandRound heuristic (which is what?)
ClpTrustedData trustedSolutions;
trustedSolutions.typeStruct = 1;
trustedSolutions.data = &solutions;
solutions.numberSolutions = 0;
solutions.maximumSolutions = 0;
solutions.numberColumns = simplex->numberColumns();
solutions.solution = NULL;
solutions.numberUnsatisfied = NULL;
simplex->setTrustedUserPointer(&trustedSolutions);
// Solve from all slack to get some points
simplex->allSlackBasis();
// Calling primal() invalidates pointers to some rim vectors,
// like...row sense (!)
simplex->primal();
// 1. Okay - so a workaround would be to copy the data I want BEFORE
// calling primal.
// 2. Another approach is to ask the simplex solvers NOT to mess up my
// rims.
// 3. See freeCachedResults() for what is getting
// deleted. Everything else points into the structure.
// ...or use collower and colupper rather than rowsense.
// ..store address of where one of these
// Store the basic problem information
// -Get the number of columns, rows and rhs vector
int numCols = clpSolver->getNumCols();
int numRows = clpSolver->getNumRows();
// Find the integer variables (use columnType(?))
// One if not continuous, that is binary or general integer)
// columnType() = 0 continuous
// = 1 binary
// = 2 general integer
bool * varClassInt = new bool[numCols];
const char* columnType = clpSolver->columnType();
int numGenInt = 0;
for (int i = 0; i < numCols; i++) {
if (clpSolver->isContinuous(i))
varClassInt[i] = 0;
else
varClassInt[i] = 1;
if (columnType[i] == 2) numGenInt++;
}
// Heuristic is for problems with general integer variables.
// If there are none, quit.
if (numGenInt++ < 1) {
delete [] varClassInt ;
std::cout << "Leaving the Randomized Rounding Heuristic" << std::endl;
return 0;
}
// -Get the rows sense
const char * rowSense;
rowSense = clpSolver->getRowSense();
// -Get the objective coefficients
double *originalObjCoeff = CoinCopyOfArray(clpSolver->getObjCoefficients(), numCols);
// -Get the matrix of the problem
// rlh: look at using sparse representation
double ** matrix = new double * [numRows];
for (int i = 0; i < numRows; i++) {
matrix[i] = new double[numCols];
for (int j = 0; j < numCols; j++)
matrix[i][j] = 0;
}
const CoinPackedMatrix* matrixByRow = clpSolver->getMatrixByRow();
const double * matrixElements = matrixByRow->getElements();
const int * matrixIndices = matrixByRow->getIndices();
const int * matrixStarts = matrixByRow->getVectorStarts();
for (int j = 0; j < numRows; j++) {
for (int i = matrixStarts[j]; i < matrixStarts[j+1]; i++) {
matrix[j][matrixIndices[i]] = matrixElements[i];
}
}
double * newObj = new double [numCols];
srand ( static_cast<unsigned int>(time(NULL) + 1));
int randNum;
// Shuffle the rows:
// Put the rows in a random order
// so that the optimal solution is a different corner point than the
// starting point.
int * index = new int [numRows];
for (int i = 0; i < numRows; i++)
index[i] = i;
for (int i = 0; i < numRows; i++) {
int temp = index[i];
int randNumTemp = i + intRand(numRows - i);
index[i] = index[randNumTemp];
index[randNumTemp] = temp;
}
// Start finding corner points by iteratively doing the following:
// - contruct a randomly tilted objective
// - solve
for (int i = 0; i < numRows; i++) {
// TODO: that 10,000 could be a param in the member data
if (solutions.numberSolutions > 10000)
break;
randNum = intRand(2);
for (int j = 0; j < numCols; j++) {
// for row i and column j vary the coefficient "a bit"
if (randNum == 1)
// if the element is zero, then set the new obj
// coefficient to 0.1 (i.e., round up)
if (fabs(matrix[index[i]][j]) < primalTolerance)
newObj[j] = 0.1;
else
// if the element is nonzero, then increase the new obj
// coefficient "a bit"
newObj[j] = matrix[index[i]][j] * 1.1;
else
// if randnum is 2, then
// if the element is zero, then set the new obj coeffient
// to NEGATIVE 0.1 (i.e., round down)
if (fabs(matrix[index[i]][j]) < primalTolerance)
newObj[j] = -0.1;
else
// if the element is nonzero, then DEcrease the new obj coeffienct "a bit"
newObj[j] = matrix[index[i]][j] * 0.9;
}
// Use the new "tilted" objective
clpSolver->setObjective(newObj);
// Based on the row sense, we decide whether to max or min
if (rowSense[i] == 'L')
clpSolver->setObjSense(-1);
else
clpSolver->setObjSense(1);
// Solve with primal simplex
simplex->primal(1);
// rlh+ll: This was the original code. But we already have the
// model pointer (it's in simplex). And, calling getModelPtr()
// invalidates the cached data in the OsiClpSolverInterface
// object, which means our precious rowsens is lost. So let's
// not use the line below...
/******* clpSolver->getModelPtr()->primal(1); */
printf("---------------------------------------------------------------- %d\n", i);
}
// Iteratively do this process until...
// either you reach the max number of corner points (aka 10K)
// or all the rows have been used as an objective.
// Look at solutions
int numberSolutions = solutions.numberSolutions;
//const char * integerInfo = simplex->integerInformation();
//const double * columnLower = simplex->columnLower();
//const double * columnUpper = simplex->columnUpper();
printf("there are %d solutions\n", numberSolutions);
// Up to here we have all the corner points
// Now we need to do the random walks and roundings
double ** cornerPoints = new double * [numberSolutions];
for (int j = 0; j < numberSolutions; j++)
cornerPoints[j] = solutions.solution[j];
bool feasibility = 1;
// rlh: use some COIN max instead of 1e30 (?)
double bestObj = 1e30;
std::vector< std::vector <double> > feasibles;
int numFeasibles = 0;
// Check the feasibility of the corner points
int numCornerPoints = numberSolutions;
const double * rhs = clpSolver->getRightHandSide();
// rlh: row sense hasn't changed. why a fresh copy?
// Delete next line.
rowSense = clpSolver->getRowSense();
for (int i = 0; i < numCornerPoints; i++) {
//get the objective value for this this point
double objValue = 0;
for (int k = 0; k < numCols; k++)
objValue += cornerPoints[i][k] * originalObjCoeff[k];
if (objValue < bestObj) {
// check integer feasibility
feasibility = 1;
for (int j = 0; j < numCols; j++) {
if (varClassInt[j]) {
double closest = floor(cornerPoints[i][j] + 0.5);
if (fabs(cornerPoints[i][j] - closest) > primalTolerance) {
feasibility = 0;
break;
}
}
}
// check all constraints satisfied
if (feasibility) {
for (int irow = 0; irow < numRows; irow++) {
double lhs = 0;
for (int j = 0; j < numCols; j++) {
lhs += matrix[irow][j] * cornerPoints[i][j];
}
if (rowSense[irow] == 'L' && lhs > rhs[irow] + primalTolerance) {
feasibility = 0;
break;
}
if (rowSense[irow] == 'G' && lhs < rhs[irow] - primalTolerance) {
feasibility = 0;
break;
}
if (rowSense[irow] == 'E' && (lhs - rhs[irow] > primalTolerance || lhs - rhs[irow] < -primalTolerance)) {
feasibility = 0;
break;
}
}
}
if (feasibility) {
numFeasibles++;
feasibles.push_back(std::vector <double> (numCols));
for (int k = 0; k < numCols; k++)
feasibles[numFeasibles-1][k] = cornerPoints[i][k];
printf("obj: %f\n", objValue);
if (objValue < bestObj)
bestObj = objValue;
}
}
}
int numFeasibleCorners;
numFeasibleCorners = numFeasibles;
//find the center of gravity of the corner points as the first random point
double * rp = new double[numCols];
for (int i = 0; i < numCols; i++) {
rp[i] = 0;
for (int j = 0; j < numCornerPoints; j++) {
rp[i] += cornerPoints[j][i];
}
rp[i] = rp[i] / numCornerPoints;
}
//-------------------------------------------
//main loop:
// -generate the next random point
// -round the random point
// -check the feasibility of the random point
//-------------------------------------------
srand ( static_cast<unsigned int>(time(NULL) + 1));
int numRandomPoints = 0;
while (numRandomPoints < 50000) {
numRandomPoints++;
//generate the next random point
int randomIndex = intRand(numCornerPoints);
double random = CoinDrand48();
for (int i = 0; i < numCols; i++) {
rp[i] = (random * (cornerPoints[randomIndex][i] - rp[i])) + rp[i];
}
//CRISP ROUNDING
//round the random point just generated
double * roundRp = new double[numCols];
for (int i = 0; i < numCols; i++) {
roundRp[i] = rp[i];
if (varClassInt[i]) {
if (rp[i] >= 0) {
if (fmod(rp[i], 1) > 0.5)
roundRp[i] = floor(rp[i]) + 1;
else
roundRp[i] = floor(rp[i]);
} else {
if (fabs(fmod(rp[i], 1)) > 0.5)
roundRp[i] = floor(rp[i]);
else
roundRp[i] = floor(rp[i]) + 1;
}
}
}
//SOFT ROUNDING
// Look at original files for the "how to" on soft rounding;
// Soft rounding omitted here.
//Check the feasibility of the rounded random point
// -Check the feasibility
// -Get the rows sense
rowSense = clpSolver->getRowSense();
rhs = clpSolver->getRightHandSide();
//get the objective value for this feasible point
double objValue = 0;
for (int i = 0; i < numCols; i++)
objValue += roundRp[i] * originalObjCoeff[i];
if (objValue < bestObj) {
feasibility = 1;
for (int i = 0; i < numRows; i++) {
double lhs = 0;
for (int j = 0; j < numCols; j++) {
lhs += matrix[i][j] * roundRp[j];
}
if (rowSense[i] == 'L' && lhs > rhs[i] + primalTolerance) {
feasibility = 0;
break;
}
if (rowSense[i] == 'G' && lhs < rhs[i] - primalTolerance) {
feasibility = 0;
break;
}
if (rowSense[i] == 'E' && (lhs - rhs[i] > primalTolerance || lhs - rhs[i] < -primalTolerance)) {
feasibility = 0;
break;
}
}
if (feasibility) {
printf("Feasible Found.\n");
printf("%.2f\n", CoinCpuTime() - start);
numFeasibles++;
feasibles.push_back(std::vector <double> (numCols));
for (int i = 0; i < numCols; i++)
feasibles[numFeasibles-1][i] = roundRp[i];
printf("obj: %f\n", objValue);
if (objValue < bestObj)
bestObj = objValue;
}
}
delete [] roundRp;
}
printf("Number of Feasible Corners: %d\n", numFeasibleCorners);
printf("Number of Feasibles Found: %d\n", numFeasibles);
if (numFeasibles > 0)
printf("Best Objective: %f\n", bestObj);
printf("time: %.2f\n", CoinCpuTime() - start);
if (numFeasibles == 0) {
// cleanup
delete [] varClassInt;
for (int i = 0; i < numRows; i++)
delete matrix[i];
delete [] matrix;
delete [] newObj;
delete [] index;
for (int i = 0; i < numberSolutions; i++)
delete cornerPoints[i];
delete [] cornerPoints;
delete [] rp;
return 0;
}
// We found something better
solutionValue = bestObj;
for (int k = 0; k < numCols; k++) {
betterSolution[k] = feasibles[numFeasibles-1][k];
}
delete [] varClassInt;
for (int i = 0; i < numRows; i++)
delete matrix[i];
delete [] matrix;
delete [] newObj;
delete [] index;
for (int i = 0; i < numberSolutions; i++)
delete cornerPoints[i];
delete [] cornerPoints;
delete [] rp;
std::cout << "Leaving the Randomized Rounding Heuristic" << std::endl;
return 1;
}
