Example #1
0
void ClpRecourseSolver::go() {
	ClpSolve solvectl;
	// disable presolve so we can re-use optimal bases
	solvectl.setPresolveType(ClpSolve::presolveOff);
	solvectl.setSolveType(ClpSolve::useDual);
	solver.setMaximumSeconds(300);
	solver.initialSolve(solvectl);

}	
Example #2
0
clp_result_t clp_solve(int n_rows, int n_cols, int n_entries,
        const int *row_indices, const int *col_indices,
        const double *coeffs, const double *vec_c,
        const double *vec_b_lo, const double *vec_b_up,
        const double *vec_x_lo, const double *vec_x_up,
        int optimisation_mode, double *vec_x_soln) {

    // build column-packed coin sparse matrix from given COO data
    CoinPackedMatrix matrix(true, row_indices, col_indices,
        coeffs, (CoinBigIndex)n_entries);
    matrix.setDimensions(n_rows, n_cols);

    ClpSimplex model;

    model.loadProblem(matrix, vec_x_lo, vec_x_up, vec_c, vec_b_lo, vec_b_up);

    model.setOptimizationDirection(1.0); // minimise

    ClpSolve solve;
    if (optimisation_mode == USE_PRIMAL) {
        solve.setSolveType(ClpSolve::usePrimal);
    } else if (optimisation_mode == USE_DUAL) {
        solve.setSolveType(ClpSolve::useDual);
    } else if (optimisation_mode == USE_BARRIER) {
        solve.setSolveType(ClpSolve::useBarrier);
    }
    solve.setPresolveType(ClpSolve::presolveOn);

    model.initialSolve(solve);

    clp_result_t result;
    result.proven_optimal = model.isProvenOptimal();
    result.proven_dual_infeasible = model.isProvenDualInfeasible();
    result.proven_primal_infeasible = model.isProvenPrimalInfeasible();
    result.abandoned = model.isAbandoned();

    const double *x = model.getColSolution();
    assert(model.getNumCols() == n_cols);
    for (int i = 0; i < model.getNumCols(); ++i) {
        vec_x_soln[i] = x[i];
    }
    
    return result;
}
Example #3
0
//#############################################################################
// Solve methods
//#############################################################################
void CbcSolver2::initialSolve()
{
  modelPtr_->scaling(0);
  setBasis(basis_,modelPtr_);
  // Do long thin by sprint
  ClpSolve options;
  options.setSolveType(ClpSolve::usePrimalorSprint);
  options.setPresolveType(ClpSolve::presolveOff);
  options.setSpecialOption(1,3,30);
  modelPtr_->initialSolve(options);
  basis_ = getBasis(modelPtr_);
  modelPtr_->setLogLevel(0);
}
Example #4
0
int main (int argc, const char *argv[])
{
     ClpSimplex  model;
     int status;
     // Keep names
     if (argc < 2) {
          status = model.readMps("small.mps", true);
     } else {
          status = model.readMps(argv[1], false);
     }
     if (status)
          exit(10);
     /*
       This driver implements a method of treating a problem as all cuts.
       So it adds in all E rows, solves and then adds in violated rows.
     */

     double time1 = CoinCpuTime();
     ClpSimplex * model2;
     ClpPresolve pinfo;
     int numberPasses = 5; // can change this
     /* Use a tolerance of 1.0e-8 for feasibility, treat problem as
        not being integer, do "numberpasses" passes and throw away names
        in presolved model */
     model2 = pinfo.presolvedModel(model, 1.0e-8, false, numberPasses, false);
     if (!model2) {
          fprintf(stderr, "ClpPresolve says %s is infeasible with tolerance of %g\n",
                  argv[1], 1.0e-8);
          fprintf(stdout, "ClpPresolve says %s is infeasible with tolerance of %g\n",
                  argv[1], 1.0e-8);
          // model was infeasible - maybe try again with looser tolerances
          model2 = pinfo.presolvedModel(model, 1.0e-7, false, numberPasses, false);
          if (!model2) {
               fprintf(stderr, "ClpPresolve says %s is infeasible with tolerance of %g\n",
                       argv[1], 1.0e-7);
               fprintf(stdout, "ClpPresolve says %s is infeasible with tolerance of %g\n",
                       argv[1], 1.0e-7);
               exit(2);
          }
     }
     // change factorization frequency from 200
     model2->setFactorizationFrequency(100 + model2->numberRows() / 50);

     int numberColumns = model2->numberColumns();
     int originalNumberRows = model2->numberRows();

     // We will need arrays to choose rows to add
     double * weight = new double [originalNumberRows];
     int * sort = new int [originalNumberRows];
     int numberSort = 0;
     char * take = new char [originalNumberRows];

     const double * rowLower = model2->rowLower();
     const double * rowUpper = model2->rowUpper();
     int iRow, iColumn;
     // Set up initial list
     numberSort = 0;
     for (iRow = 0; iRow < originalNumberRows; iRow++) {
          weight[iRow] = 1.123e50;
          if (rowLower[iRow] == rowUpper[iRow]) {
               sort[numberSort++] = iRow;
               weight[iRow] = 0.0;
          }
     }
     numberSort /= 2;
     // Just add this number of rows each time in small problem
     int smallNumberRows = 2 * numberColumns;
     smallNumberRows = CoinMin(smallNumberRows, originalNumberRows / 20);
     // and pad out with random rows
     double ratio = ((double)(smallNumberRows - numberSort)) / ((double) originalNumberRows);
     for (iRow = 0; iRow < originalNumberRows; iRow++) {
          if (weight[iRow] == 1.123e50 && CoinDrand48() < ratio)
               sort[numberSort++] = iRow;
     }
     /* This is optional.
        The best thing to do is to miss out random rows and do a set which makes dual feasible.
        If that is not possible then make sure variables have bounds.

        One way that normally works is to automatically tighten bounds.
     */
#if 0
     // However for some we need to do anyway
     double * columnLower = model2->columnLower();
     double * columnUpper = model2->columnUpper();
     for (iColumn = 0; iColumn < numberColumns; iColumn++) {
          columnLower[iColumn] = CoinMax(-1.0e6, columnLower[iColumn]);
          columnUpper[iColumn] = CoinMin(1.0e6, columnUpper[iColumn]);
     }
#endif
     model2->tightenPrimalBounds(-1.0e4, true);
     printf("%d rows in initial problem\n", numberSort);
     double * fullSolution = model2->primalRowSolution();

     // Just do this number of passes
     int maxPass = 50;
     // And take out slack rows until this pass
     int takeOutPass = 30;
     int iPass;

     const int * start = model2->clpMatrix()->getVectorStarts();
     const int * length = model2->clpMatrix()->getVectorLengths();
     const int * row = model2->clpMatrix()->getIndices();
     int * whichColumns = new int [numberColumns];
     for (iColumn = 0; iColumn < numberColumns; iColumn++)
          whichColumns[iColumn] = iColumn;
     int numberSmallColumns = numberColumns;
     for (iPass = 0; iPass < maxPass; iPass++) {
          printf("Start of pass %d\n", iPass);
          // Cleaner this way
          std::sort(sort, sort + numberSort);
          // Create small problem
          ClpSimplex small(model2, numberSort, sort, numberSmallColumns, whichColumns);
          small.setFactorizationFrequency(100 + numberSort / 200);
          //small.setPerturbation(50);
          //small.setLogLevel(63);
          // A variation is to just do N iterations
          //if (iPass)
          //small.setMaximumIterations(100);
          // Solve
          small.factorization()->messageLevel(8);
          if (iPass) {
               small.dual();
          } else {
               small.writeMps("continf.mps");
               ClpSolve solveOptions;
               solveOptions.setSolveType(ClpSolve::useDual);
               //solveOptions.setSolveType(ClpSolve::usePrimalorSprint);
               //solveOptions.setSpecialOption(1,2,200); // idiot
               small.initialSolve(solveOptions);
          }
          bool dualInfeasible = (small.status() == 2);
          // move solution back
          double * solution = model2->primalColumnSolution();
          const double * smallSolution = small.primalColumnSolution();
          for (int j = 0; j < numberSmallColumns; j++) {
               iColumn = whichColumns[j];
               solution[iColumn] = smallSolution[j];
               model2->setColumnStatus(iColumn, small.getColumnStatus(j));
          }
          for (iRow = 0; iRow < numberSort; iRow++) {
               int kRow = sort[iRow];
               model2->setRowStatus(kRow, small.getRowStatus(iRow));
          }
          // compute full solution
          memset(fullSolution, 0, originalNumberRows * sizeof(double));
          model2->times(1.0, model2->primalColumnSolution(), fullSolution);
          if (iPass != maxPass - 1) {
               // Mark row as not looked at
               for (iRow = 0; iRow < originalNumberRows; iRow++)
                    weight[iRow] = 1.123e50;
               // Look at rows already in small problem
               int iSort;
               int numberDropped = 0;
               int numberKept = 0;
               int numberBinding = 0;
               int numberInfeasibilities = 0;
               double sumInfeasibilities = 0.0;
               for (iSort = 0; iSort < numberSort; iSort++) {
                    iRow = sort[iSort];
                    //printf("%d %g %g\n",iRow,fullSolution[iRow],small.primalRowSolution()[iSort]);
                    if (model2->getRowStatus(iRow) == ClpSimplex::basic) {
                         // Basic - we can get rid of if early on
                         if (iPass < takeOutPass && !dualInfeasible) {
                              // may have hit max iterations so check
                              double infeasibility = CoinMax(fullSolution[iRow] - rowUpper[iRow],
                                                             rowLower[iRow] - fullSolution[iRow]);
                              weight[iRow] = -infeasibility;
                              if (infeasibility > 1.0e-8) {
                                   numberInfeasibilities++;
                                   sumInfeasibilities += infeasibility;
                              } else {
                                   weight[iRow] = 1.0;
                                   numberDropped++;
                              }
                         } else {
                              // keep
                              weight[iRow] = -1.0e40;
                              numberKept++;
                         }
                    } else {
                         // keep
                         weight[iRow] = -1.0e50;
                         numberKept++;
                         numberBinding++;
                    }
               }
               // Now rest
               for (iRow = 0; iRow < originalNumberRows; iRow++) {
                    sort[iRow] = iRow;
                    if (weight[iRow] == 1.123e50) {
                         // not looked at yet
                         double infeasibility = CoinMax(fullSolution[iRow] - rowUpper[iRow],
                                                        rowLower[iRow] - fullSolution[iRow]);
                         weight[iRow] = -infeasibility;
                         if (infeasibility > 1.0e-8) {
                              numberInfeasibilities++;
                              sumInfeasibilities += infeasibility;
                         }
                    }
               }
               // sort
               CoinSort_2(weight, weight + originalNumberRows, sort);
               numberSort = CoinMin(originalNumberRows, smallNumberRows + numberKept);
               memset(take, 0, originalNumberRows);
               for (iRow = 0; iRow < numberSort; iRow++)
                    take[sort[iRow]] = 1;
               numberSmallColumns = 0;
               for (iColumn = 0; iColumn < numberColumns; iColumn++) {
                    int n = 0;
                    for (int j = start[iColumn]; j < start[iColumn] + length[iColumn]; j++) {
                         int iRow = row[j];
                         if (take[iRow])
                              n++;
                    }
                    if (n)
                         whichColumns[numberSmallColumns++] = iColumn;
               }
               printf("%d rows binding, %d rows kept, %d rows dropped - new size %d rows, %d columns\n",
                      numberBinding, numberKept, numberDropped, numberSort, numberSmallColumns);
               printf("%d rows are infeasible - sum is %g\n", numberInfeasibilities,
                      sumInfeasibilities);
               if (!numberInfeasibilities) {
                    printf("Exiting as looks optimal\n");
                    break;
               }
               numberInfeasibilities = 0;
               sumInfeasibilities = 0.0;
               for (iSort = 0; iSort < numberSort; iSort++) {
                    if (weight[iSort] > -1.0e30 && weight[iSort] < -1.0e-8) {
                         numberInfeasibilities++;
                         sumInfeasibilities += -weight[iSort];
                    }
               }
               printf("in small model %d rows are infeasible - sum is %g\n", numberInfeasibilities,
                      sumInfeasibilities);
          }
     }
     delete [] weight;
     delete [] sort;
     delete [] whichColumns;
     delete [] take;
     // If problem is big you may wish to skip this
     model2->dual();
     int numberBinding = 0;
     for (iRow = 0; iRow < originalNumberRows; iRow++) {
          if (model2->getRowStatus(iRow) != ClpSimplex::basic)
               numberBinding++;
     }
     printf("%d binding rows at end\n", numberBinding);
     pinfo.postsolve(true);

     int numberIterations = model2->numberIterations();;
     delete model2;
     /* After this postsolve model should be optimal.
        We can use checkSolution and test feasibility */
     model.checkSolution();
     if (model.numberDualInfeasibilities() ||
               model.numberPrimalInfeasibilities())
          printf("%g dual %g(%d) Primal %g(%d)\n",
                 model.objectiveValue(),
                 model.sumDualInfeasibilities(),
                 model.numberDualInfeasibilities(),
                 model.sumPrimalInfeasibilities(),
                 model.numberPrimalInfeasibilities());
     // But resolve for safety
     model.primal(1);

     numberIterations += model.numberIterations();;
     printf("Solve took %g seconds\n", CoinCpuTime() - time1);
     return 0;
}
Example #5
0
int main(int argc, const char *argv[])
{
     ClpSimplex  model;
     int status;
     // Keep names when reading an mps file
     if (argc < 2) {
#if defined(SAMPLEDIR)
          status = model.readMps(SAMPLEDIR "/p0033.mps", true);
#else
          fprintf(stderr, "Do not know where to find sample MPS files.\n");
          exit(1);
#endif
     } else
          status = model.readMps(argv[1], true);

     if (status) {
          fprintf(stderr, "Bad readMps %s\n", argv[1]);
          fprintf(stdout, "Bad readMps %s\n", argv[1]);
          exit(1);
     }
#ifdef STYLE1
     if (argc < 3 || !strstr(argv[2], "primal")) {
          // Use the dual algorithm unless user said "primal"
          model.initialDualSolve();
     } else {
          model.initialPrimalSolve();
     }
#else
     ClpSolve solvectl;


     if (argc < 3 || (!strstr(argv[2], "primal") && !strstr(argv[2], "barrier"))) {
          // Use the dual algorithm unless user said "primal" or "barrier"
          std::cout << std::endl << " Solve using Dual: " << std::endl;
          solvectl.setSolveType(ClpSolve::useDual);
          solvectl.setPresolveType(ClpSolve::presolveOn);
          model.initialSolve(solvectl);
     } else if (strstr(argv[2], "barrier")) {
          // Use the barrier algorithm if user said "barrier"
          std::cout << std::endl << " Solve using Barrier: " << std::endl;
          solvectl.setSolveType(ClpSolve::useBarrier);
          solvectl.setPresolveType(ClpSolve::presolveOn);
          model.initialSolve(solvectl);
     } else {
          std::cout << std::endl << " Solve using Primal: " << std::endl;
          solvectl.setSolveType(ClpSolve::usePrimal);
          solvectl.setPresolveType(ClpSolve::presolveOn);
          model.initialSolve(solvectl);
     }
#endif
     std::string modelName;
     model.getStrParam(ClpProbName, modelName);
     std::cout << "Model " << modelName << " has " << model.numberRows() << " rows and " <<
               model.numberColumns() << " columns" << std::endl;

     // remove this to print solution

     exit(0);

     /*
       Now to print out solution.  The methods used return modifiable
       arrays while the alternative names return const pointers -
       which is of course much more virtuous.

       This version just does non-zero columns

      */
#if 0
     int numberRows = model.numberRows();

     // Alternatively getRowActivity()
     double * rowPrimal = model.primalRowSolution();
     // Alternatively getRowPrice()
     double * rowDual = model.dualRowSolution();
     // Alternatively getRowLower()
     double * rowLower = model.rowLower();
     // Alternatively getRowUpper()
     double * rowUpper = model.rowUpper();
     // Alternatively getRowObjCoefficients()
     double * rowObjective = model.rowObjective();

     // If we have not kept names (parameter to readMps) this will be 0
     assert(model.lengthNames());

     // Row names
     const std::vector<std::string> * rowNames = model.rowNames();


     int iRow;

     std::cout << "                       Primal          Dual         Lower         Upper        (Cost)"
               << std::endl;

     for (iRow = 0; iRow < numberRows; iRow++) {
          double value;
          std::cout << std::setw(6) << iRow << " " << std::setw(8) << (*rowNames)[iRow];
          value = rowPrimal[iRow];
          if (fabs(value) < 1.0e5)
               std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
          else
               std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
          value = rowDual[iRow];
          if (fabs(value) < 1.0e5)
               std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
          else
               std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
          value = rowLower[iRow];
          if (fabs(value) < 1.0e5)
               std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
          else
               std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
          value = rowUpper[iRow];
          if (fabs(value) < 1.0e5)
               std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
          else
               std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
          if (rowObjective) {
               value = rowObjective[iRow];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
          }
          std::cout << std::endl;
     }
#endif
     std::cout << "--------------------------------------" << std::endl;

     // Columns

     int numberColumns = model.numberColumns();

     // Alternatively getColSolution()
     double * columnPrimal = model.primalColumnSolution();
     // Alternatively getReducedCost()
     double * columnDual = model.dualColumnSolution();
     // Alternatively getColLower()
     double * columnLower = model.columnLower();
     // Alternatively getColUpper()
     double * columnUpper = model.columnUpper();
     // Alternatively getObjCoefficients()
     double * columnObjective = model.objective();

     // If we have not kept names (parameter to readMps) this will be 0
     assert(model.lengthNames());

     // Column names
     const std::vector<std::string> * columnNames = model.columnNames();


     int iColumn;

     std::cout << "                       Primal          Dual         Lower         Upper          Cost"
               << std::endl;

     for (iColumn = 0; iColumn < numberColumns; iColumn++) {
          double value;
          value = columnPrimal[iColumn];
          if (fabs(value) > 1.0e-8) {
               std::cout << std::setw(6) << iColumn << " " << std::setw(8) << (*columnNames)[iColumn];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
               value = columnDual[iColumn];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
               value = columnLower[iColumn];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
               value = columnUpper[iColumn];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;
               value = columnObjective[iColumn];
               if (fabs(value) < 1.0e5)
                    std::cout << std::setiosflags(std::ios::fixed | std::ios::showpoint) << std::setw(14) << value;
               else
                    std::cout << std::setiosflags(std::ios::scientific) << std::setw(14) << value;

               std::cout << std::endl;
          }
     }
     std::cout << "--------------------------------------" << std::endl;

     return 0;
}