示例#1
0
void glp_unscale_prob(glp_prob *lp)
{     int m = glp_get_num_rows(lp);
      int n = glp_get_num_cols(lp);
      int i, j;
      for (i = 1; i <= m; i++) glp_set_rii(lp, i, 1.0);
      for (j = 1; j <= n; j++) glp_set_sjj(lp, j, 1.0);
      return;
}
示例#2
0
int GLPKAddConstraint(LinEquation* InEquation) {
	if (InEquation->QuadCoeff.size() > 0) {
		FErrorFile() << "GLPK solver cannot accept quadratic constraints." << endl;
		FlushErrorFile();
		return FAIL;
	}

	if (GLPKModel == NULL) {
		FErrorFile() << "Could not add constraint because GLPK object does not exist." << endl;
		FlushErrorFile();
		return FAIL;
	}

	int NumRows = glp_get_num_rows(GLPKModel);

	if (InEquation->Index >= NumRows) {
		glp_add_rows(GLPKModel, 1);
	}

	if (InEquation->EqualityType == EQUAL) {
		glp_set_row_bnds(GLPKModel, InEquation->Index+1, GLP_FX, InEquation->RightHandSide, InEquation->RightHandSide);
	} else if (InEquation->EqualityType == GREATER) {
		glp_set_row_bnds(GLPKModel, InEquation->Index+1, GLP_LO, InEquation->RightHandSide, InEquation->RightHandSide);
	} else if (InEquation->EqualityType == LESS) {
		glp_set_row_bnds(GLPKModel, InEquation->Index+1, GLP_UP, InEquation->RightHandSide, InEquation->RightHandSide);
	} else {
		FErrorFile() << "Could not add constraint because the constraint type was not recognized." << endl;
		FlushErrorFile();
		return FAIL;
	}

	int NumColumns = glp_get_num_cols(GLPKModel);

	int* Indecies = new int[int(InEquation->Variables.size())+1];
	double* Coeff = new double[int(InEquation->Variables.size())+1];
	for (int i=0; i < int(InEquation->Variables.size()); i++) {
		if (InEquation->Variables[i]->Index < NumColumns) {
			if (InEquation->Variables[i]->Exclude) {
				Coeff[i+1] = 0;
			} else {
				Coeff[i+1] = InEquation->Coefficient[i];
			}
			Indecies[i+1] = InEquation->Variables[i]->Index+1;
		} else {
			FErrorFile() << "Variable index found in constraint is out of the range found in GLPK problem" << endl;
			FlushErrorFile();
			return FAIL;
		}
	}

	glp_set_mat_row(GLPKModel, InEquation->Index+1, int(InEquation->Variables.size()), Indecies, Coeff);

	delete [] Indecies;
	delete [] Coeff;

	return SUCCESS;
}
示例#3
0
文件: ULS.c 项目: Shakit/IP2015TP2
/* Different cases :
 *     - if the created node is root, then father is NULL, the problem version in the node is the one gave as parameter.
 *     - else we copy the problem, and had the constraint "x_{y} = valy"
 */
void create_node(node* n, glp_prob* prob, node* father, int y, double valy)
{
	n->father = father;
	n->leftSon = NULL;
	n->rightSon = NULL;
	n->check = 0;
	
	int i = 0;
	int ind[] = {0,y};
	double val[] = {0,1};
	
	if (n-> father == NULL)
	{
		n->prob = prob;
	}
	else
	{
		n->prob = glp_create_prob();
		glp_copy_prob(n->prob, n->father->prob, GLP_ON);
		i = glp_add_rows(n->prob, 1);
		glp_set_mat_row(n->prob, i, 1, ind, val);
		glp_set_row_bnds(n->prob, i, GLP_FX, valy, valy);
	}

	glp_smcp parm;
	glp_init_smcp(&parm);
	parm.msg_lev = GLP_MSG_OFF;

	glp_iocp parmip;
	glp_init_iocp(&parmip);
	parmip.msg_lev = GLP_MSG_OFF;

	glp_write_lp(prob, NULL, "ULS.lp");

	n->solveFlag = glp_simplex(n->prob, &parm); glp_intopt(n->prob, &parmip);

	n->z = glp_mip_obj_val(n->prob);
	n->x = (double *) malloc (glp_get_num_cols(n->prob) * sizeof(double));
	for (i = 0; i < glp_get_num_cols(n->prob); ++i) n->x[i] = glp_mip_col_val(n->prob, i+1);
}
示例#4
0
文件: ULS.c 项目: Shakit/IP2015TP2
/* Display node function */
void displayNode(node* n)
{
	int i;

	if (n->solveFlag == 0 && n->z == 0)
	{
		printf("NOT FEASIBLE\n");
	}
	else
	{
		printf(" z = %f\n",n->z);
		printf("Solution :\n");
		for (i = 0; i < glp_get_num_cols(n->prob); ++i) printf(" x%d = %f  \n", i+1, n->x[i]);
	}
}
示例#5
0
文件: ULS.c 项目: Shakit/IP2015TP2
/* While there is one unchecked node :
 *     We take the most promising.
 *     If its z value is lower than vUp (constructed solution) : 
 *         if this is an integer solution, we keep it
 *         else,
 *             we create its sons with a new constraint.
 */
node* branchAndBound (glp_prob * prob)
{
	node* root = (node *) malloc (sizeof(node));
	create_node(root, prob, NULL, 0, 0);

	node* res = construction(prob);
	double vUp = res->z; 
	
	node* node_ptr = checked(root);
	while (node_ptr != NULL)
	{
		if (node_ptr->z != 0)
		{
			if (!(node_ptr->z >= vUp))
			{
				int y = allYinteger(node_ptr->x, glp_get_num_cols(node_ptr->prob));
				if ( y == -1)
				{
					vUp = node_ptr->z;
					res = node_ptr;
				}
				else
				{
					node* left = (node *) malloc (sizeof(node));
					node* right = (node *) malloc (sizeof(node));

					create_node(left, prob, node_ptr, y, 0);
					create_node(right, prob, node_ptr, y, 1);

					
					node_ptr->leftSon = left;
					node_ptr->rightSon = right;
				}
			}
		}
	
		node_ptr->check = 1;
		node_ptr = checked(root);
	}

	return res;
}
示例#6
0
vector<int> LinearProblem::ElasticFilter() const {
  LinearProblem tmp(*this);

  int realCols = glp_get_num_cols(tmp.lp_);
  for (int i = realCols; i > 0; --i) { // set old coefs to zero
    glp_set_obj_coef(tmp.lp_, i,  0);
  }

  int elasticCols = glp_get_num_rows(tmp.lp_);
  glp_add_cols(tmp.lp_, elasticCols);
  for (int i = 1; i <= elasticCols; ++i) {
    int indices[MAX_VARS];
    double values[MAX_VARS];
    int nonZeros = glp_get_mat_row(tmp.lp_, i, indices, values);

    indices[nonZeros + 1] = realCols + i;
    values[nonZeros + 1] = 1.0;

    glp_set_mat_row(tmp.lp_, i, nonZeros + 1, indices, values);
    glp_set_obj_coef(tmp.lp_, realCols + i,  1);
    glp_set_col_bnds(tmp.lp_, realCols + i, GLP_UP, 0.0, 0.0);
  }

  vector<int> suspects;
  glp_std_basis(tmp.lp_);
  int status = tmp.Solve();
  while ((status != GLP_INFEAS) && (status != GLP_NOFEAS)) {
    for (int i = 1; i <= elasticCols; ++i) {
      if (glp_get_col_prim(tmp.lp_, realCols + i) < 0) {
        suspects.push_back(i);
        glp_set_col_bnds(tmp.lp_, realCols + i, GLP_FX, 0.0, 0.0);
      }
    }
    status = tmp.Solve();
  }

  return suspects;
}
示例#7
0
文件: glpapi17.c 项目: cran/farmR
int glp_mpl_postsolve(glp_tran *tran, glp_prob *prob, int sol)
{     /* postsolve the model */
      int j, m, n, ret;
      double x;
      if (!(tran->phase == 3 && !tran->flag_p))
         xerror("glp_mpl_postsolve: invalid call sequence\n");
      if (!(sol == GLP_SOL || sol == GLP_IPT || sol == GLP_MIP))
         xerror("glp_mpl_postsolve: sol = %d; invalid parameter\n",
            sol);
      m = mpl_get_num_rows(tran);
      n = mpl_get_num_cols(tran);
      if (!(m == glp_get_num_rows(prob) &&
            n == glp_get_num_cols(prob)))
         xerror("glp_mpl_postsolve: wrong problem object\n");
      if (!mpl_has_solve_stmt(tran))
      {  ret = 0;
         goto done;
      }
      for (j = 1; j <= n; j++)
      {  if (sol == GLP_SOL)
            x = glp_get_col_prim(prob, j);
         else if (sol == GLP_IPT)
            x = glp_ipt_col_prim(prob, j);
         else if (sol == GLP_MIP)
            x = glp_mip_col_val(prob, j);
         else
            xassert(sol != sol);
         if (fabs(x) < 1e-9) x = 0.0;
         mpl_put_col_value(tran, j, x);
      }
      ret = mpl_postsolve(tran);
      if (ret == 3)
         ret = 0;
      else if (ret == 4)
         ret = 1;
done: return ret;
}
示例#8
0
int GLPKLoadVariables(MFAVariable* InVariable, bool RelaxIntegerVariables,bool UseTightBounds) {
	if (GLPKModel == NULL) {
		FErrorFile() << "Could not add variable because GLPK object does not exist." << endl;
		FlushErrorFile();
		return FAIL;
	}

	int NumColumns = glp_get_num_cols(GLPKModel);

	if (InVariable->Index >= NumColumns) {
		glp_add_cols(GLPKModel, 1);
		string Name = GetMFAVariableName(InVariable);
		char* Temp = new char[Name.length()+1];
		strcpy(Temp,Name.data());
		glp_set_col_name(GLPKModel,InVariable->Index+1,Temp);
	}


	double LowerBound = InVariable->LowerBound;
	double UpperBound = InVariable->UpperBound;
	if (UseTightBounds) {
		LowerBound = InVariable->Min;
		UpperBound = InVariable->Max;
	}
	if (LowerBound != UpperBound) {
		glp_set_col_bnds(GLPKModel, InVariable->Index+1, GLP_DB, InVariable->LowerBound, InVariable->UpperBound);
	} else {
		glp_set_col_bnds(GLPKModel, InVariable->Index+1, GLP_FX, InVariable->LowerBound, InVariable->UpperBound);
	}

	if (InVariable->Binary && !RelaxIntegerVariables) {
		//glp_set_class(GLPKModel, GLP_MIP); There is no equivalent in the new API
		glp_set_col_kind(GLPKModel, InVariable->Index+1,GLP_IV);
	}

	return SUCCESS;
}
示例#9
0
int GLPKLoadObjective(LinEquation* InEquation, bool Max) {
	if (InEquation->QuadCoeff.size() > 0) {
		FErrorFile() << "GLPK solver cannot accept quadratic objectives." << endl;
		FlushErrorFile();
		return FAIL;
	}

	if (GLPKModel == NULL) {
		FErrorFile() << "Could not add objective because GLPK object does not exist." << endl;
		FlushErrorFile();
		return FAIL;
	}

	if (!Max) {
		glp_set_obj_dir(GLPKModel, GLP_MIN);
	} else {
		glp_set_obj_dir(GLPKModel, GLP_MAX);
	}
	
	int NumColumns = glp_get_num_cols(GLPKModel);

	for (int i=0; i < NumColumns; i++) {
		glp_set_obj_coef(GLPKModel, i+1, 0);
	}
	for (int i=0; i < int(InEquation->Variables.size()); i++) {
		if (NumColumns > InEquation->Variables[i]->Index) {
			glp_set_obj_coef(GLPKModel, InEquation->Variables[i]->Index+1, InEquation->Coefficient[i]);
		} else {
			FErrorFile() << "Variable index specified in objective was out of the range of variables added to the GLPK problem object." << endl;
			FlushErrorFile();
			return FAIL;
		}
	}

	return SUCCESS;
}
示例#10
0
文件: ULS.c 项目: Shakit/IP2015TP2
/* Create from the base problem, an other problem which forces stocks to be 0.
 * The cronstruted solution stay feasible for the first problem.
 */
node* construction (glp_prob * prob)
{
	node* res = (node *) malloc (sizeof(node));
	
	glp_prob * constProb = glp_create_prob(); glp_copy_prob(constProb, prob, GLP_ON);
	int i = glp_add_rows(constProb, 1);
	int k = glp_add_rows(constProb, 1);
    int nbj = glp_get_num_cols(prob)/3 -1;

	int ind[nbj+2];
	double val[nbj+2];
	int indk[nbj+2];
	double valk[nbj+2];
	int j;
	
	ind[0] = 0; val[0] = 1;
	ind[1] = 1; val[1] = 1;
	indk[1] = 2 ; valk[1] = 1;
	for (j = 1; j <= nbj; ++j)
	{
		ind[j] = j * 3 +2;
		indk[j] = j *3 + 3;
		val[j] = 1;
		valk[j] = 1;

		
	}
	
	glp_set_mat_row(constProb, i, nbj, ind, val);
	glp_set_row_bnds(constProb, i, GLP_FX, 0, 0);
	glp_set_mat_row(constProb, k, nbj, indk, valk);
	glp_set_row_bnds(constProb, k, GLP_FX, nbj, nbj);

	create_node(res, constProb, NULL, 0, 0);
	return res;
}
示例#11
0
文件: ppl_lpsol.c 项目: hnxiao/ppl
static void
solve(char* file_name) {
  ppl_Constraint_System_t ppl_cs;
#ifndef NDEBUG
  ppl_Constraint_System_t ppl_cs_copy;
#endif
  ppl_Generator_t optimum_location;
  ppl_Linear_Expression_t ppl_le;
  int dimension, row, num_rows, column, nz, i, j, type;
  int* coefficient_index;
  double lb, ub;
  double* coefficient_value;
  mpq_t rational_lb, rational_ub;
  mpq_t* rational_coefficient;
  mpq_t* objective;
  ppl_Linear_Expression_t ppl_objective_le;
  ppl_Coefficient_t optimum_n;
  ppl_Coefficient_t optimum_d;
  mpq_t optimum;
  mpz_t den_lcm;
  int optimum_found;
  glp_mpscp glpk_mpscp;

  glpk_lp = glp_create_prob();
  glp_init_mpscp(&glpk_mpscp);

  if (verbosity == 0) {
    /* FIXME: find a way to suppress output from glp_read_mps. */
  }

#ifdef PPL_LPSOL_SUPPORTS_TIMINGS

  if (print_timings)
    start_clock();

#endif /* defined(PPL_LPSOL_SUPPORTS_TIMINGS) */

  if (glp_read_mps(glpk_lp, GLP_MPS_FILE, &glpk_mpscp, file_name) != 0)
    fatal("cannot read MPS file `%s'", file_name);

#ifdef PPL_LPSOL_SUPPORTS_TIMINGS

  if (print_timings) {
    fprintf(stderr, "Time to read the input file: ");
    print_clock(stderr);
    fprintf(stderr, " s\n");
    start_clock();
  }

#endif /* defined(PPL_LPSOL_SUPPORTS_TIMINGS) */

  glpk_lp_num_int = glp_get_num_int(glpk_lp);

  if (glpk_lp_num_int > 0 && !no_mip && !use_simplex)
     fatal("the enumeration solving method can not handle MIP problems");

  dimension = glp_get_num_cols(glpk_lp);

  /* Read variables constrained to be integer. */
  if (glpk_lp_num_int > 0 && !no_mip && use_simplex) {
    if (verbosity >= 4)
      fprintf(output_file, "Integer variables:\n");
    integer_variables = (ppl_dimension_type*)
      malloc((glpk_lp_num_int + 1)*sizeof(ppl_dimension_type));
    for (i = 0, j = 0; i < dimension; ++i) {
      int col_kind = glp_get_col_kind(glpk_lp, i+1);
      if (col_kind == GLP_IV || col_kind == GLP_BV) {
        integer_variables[j] = i;
        if (verbosity >= 4) {
          ppl_io_fprint_variable(output_file, i);
          fprintf(output_file, " ");
        }
        ++j;
      }
    }
  }
  coefficient_index = (int*) malloc((dimension+1)*sizeof(int));
  coefficient_value = (double*) malloc((dimension+1)*sizeof(double));
  rational_coefficient = (mpq_t*) malloc((dimension+1)*sizeof(mpq_t));


  ppl_new_Constraint_System(&ppl_cs);

  mpq_init(rational_lb);
  mpq_init(rational_ub);
  for (i = 1; i <= dimension; ++i)
    mpq_init(rational_coefficient[i]);

  mpz_init(den_lcm);

  if (verbosity >= 4)
    fprintf(output_file, "\nConstraints:\n");

  /* Set up the row (ordinary) constraints. */
  num_rows = glp_get_num_rows(glpk_lp);
  for (row = 1; row <= num_rows; ++row) {
    /* Initialize the least common multiple computation. */
    mpz_set_si(den_lcm, 1);
    /* Set `nz' to the number of non-zero coefficients. */
    nz = glp_get_mat_row(glpk_lp, row, coefficient_index, coefficient_value);
    for (i = 1; i <= nz; ++i) {
      set_mpq_t_from_double(rational_coefficient[i], coefficient_value[i]);
      /* Update den_lcm. */
      mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_coefficient[i]));
    }

    lb = glp_get_row_lb(glpk_lp, row);
    ub = glp_get_row_ub(glpk_lp, row);

    set_mpq_t_from_double(rational_lb, lb);
    set_mpq_t_from_double(rational_ub, ub);

    mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_lb));
    mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_ub));

    ppl_new_Linear_Expression_with_dimension(&ppl_le, dimension);

    for (i = 1; i <= nz; ++i) {
      mpz_mul(tmp_z, den_lcm, mpq_numref(rational_coefficient[i]));
      mpz_divexact(tmp_z, tmp_z, mpq_denref(rational_coefficient[i]));
      ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
      ppl_Linear_Expression_add_to_coefficient(ppl_le, coefficient_index[i]-1,
                                               ppl_coeff);
    }

    type = glp_get_row_type(glpk_lp, row);
    add_constraints(ppl_le, type, rational_lb, rational_ub, den_lcm, ppl_cs);

    ppl_delete_Linear_Expression(ppl_le);
  }

  free(coefficient_value);
  for (i = 1; i <= dimension; ++i)
    mpq_clear(rational_coefficient[i]);
  free(rational_coefficient);
  free(coefficient_index);

#ifndef NDEBUG
  ppl_new_Constraint_System_from_Constraint_System(&ppl_cs_copy, ppl_cs);
#endif

  /*
    FIXME: here we could build the polyhedron and minimize it before
    adding the variable bounds.
  */

  /* Set up the columns constraints, i.e., variable bounds. */
  for (column = 1; column <= dimension; ++column) {

    lb = glp_get_col_lb(glpk_lp, column);
    ub = glp_get_col_ub(glpk_lp, column);

    set_mpq_t_from_double(rational_lb, lb);
    set_mpq_t_from_double(rational_ub, ub);

    /* Initialize the least common multiple computation. */
    mpz_set_si(den_lcm, 1);
    mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_lb));
    mpz_lcm(den_lcm, den_lcm, mpq_denref(rational_ub));

    ppl_new_Linear_Expression_with_dimension(&ppl_le, dimension);
    ppl_assign_Coefficient_from_mpz_t(ppl_coeff, den_lcm);
    ppl_Linear_Expression_add_to_coefficient(ppl_le, column-1, ppl_coeff);

    type = glp_get_col_type(glpk_lp, column);
    add_constraints(ppl_le, type, rational_lb, rational_ub, den_lcm, ppl_cs);

    ppl_delete_Linear_Expression(ppl_le);
  }

  mpq_clear(rational_ub);
  mpq_clear(rational_lb);

  /* Deal with the objective function. */
  objective = (mpq_t*) malloc((dimension+1)*sizeof(mpq_t));

  /* Initialize the least common multiple computation. */
  mpz_set_si(den_lcm, 1);

  mpq_init(objective[0]);
  set_mpq_t_from_double(objective[0], glp_get_obj_coef(glpk_lp, 0));
  for (i = 1; i <= dimension; ++i) {
    mpq_init(objective[i]);
    set_mpq_t_from_double(objective[i], glp_get_obj_coef(glpk_lp, i));
    /* Update den_lcm. */
    mpz_lcm(den_lcm, den_lcm, mpq_denref(objective[i]));
  }

  /* Set the ppl_objective_le to be the objective function. */
  ppl_new_Linear_Expression_with_dimension(&ppl_objective_le, dimension);
  /* Set value for objective function's inhomogeneous term. */
  mpz_mul(tmp_z, den_lcm, mpq_numref(objective[0]));
  mpz_divexact(tmp_z, tmp_z, mpq_denref(objective[0]));
  ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
  ppl_Linear_Expression_add_to_inhomogeneous(ppl_objective_le, ppl_coeff);
  /* Set values for objective function's variable coefficients. */
  for (i = 1; i <= dimension; ++i) {
    mpz_mul(tmp_z, den_lcm, mpq_numref(objective[i]));
    mpz_divexact(tmp_z, tmp_z, mpq_denref(objective[i]));
    ppl_assign_Coefficient_from_mpz_t(ppl_coeff, tmp_z);
    ppl_Linear_Expression_add_to_coefficient(ppl_objective_le, i-1, ppl_coeff);
  }

  if (verbosity >= 4) {
    fprintf(output_file, "Objective function:\n");
    if (mpz_cmp_si(den_lcm, 1) != 0)
      fprintf(output_file, "(");
    ppl_io_fprint_Linear_Expression(output_file, ppl_objective_le);
  }

  for (i = 0; i <= dimension; ++i)
    mpq_clear(objective[i]);
  free(objective);

  if (verbosity >= 4) {
    if (mpz_cmp_si(den_lcm, 1) != 0) {
      fprintf(output_file, ")/");
      mpz_out_str(output_file, 10, den_lcm);
    }
    fprintf(output_file, "\n%s\n",
            (maximize ? "Maximizing." : "Minimizing."));
  }

  ppl_new_Coefficient(&optimum_n);
  ppl_new_Coefficient(&optimum_d);
  ppl_new_Generator_zero_dim_point(&optimum_location);

  optimum_found = use_simplex
    ? solve_with_simplex(ppl_cs,
                         ppl_objective_le,
                         optimum_n,
                         optimum_d,
                         optimum_location)
    : solve_with_generators(ppl_cs,
                            ppl_objective_le,
                            optimum_n,
                            optimum_d,
                            optimum_location);

  ppl_delete_Linear_Expression(ppl_objective_le);

  if (glpk_lp_num_int > 0)
      free(integer_variables);

  if (optimum_found) {
    mpq_init(optimum);
    ppl_Coefficient_to_mpz_t(optimum_n, tmp_z);
    mpq_set_num(optimum, tmp_z);
    ppl_Coefficient_to_mpz_t(optimum_d, tmp_z);
    mpz_mul(tmp_z, tmp_z, den_lcm);
    mpq_set_den(optimum, tmp_z);
    if (verbosity == 1)
      fprintf(output_file, "Optimized problem.\n");
    if (verbosity >= 2)
      fprintf(output_file, "Optimum value: %.10g\n", mpq_get_d(optimum));
    if (verbosity >= 3) {
      fprintf(output_file, "Optimum location:\n");
      ppl_Generator_divisor(optimum_location, ppl_coeff);
      ppl_Coefficient_to_mpz_t(ppl_coeff, tmp_z);
      for (i = 0; i < dimension; ++i) {
        mpz_set(mpq_denref(tmp1_q), tmp_z);
        ppl_Generator_coefficient(optimum_location, i, ppl_coeff);
        ppl_Coefficient_to_mpz_t(ppl_coeff, mpq_numref(tmp1_q));
        ppl_io_fprint_variable(output_file, i);
        fprintf(output_file, " = %.10g\n", mpq_get_d(tmp1_q));
      }
    }
#ifndef NDEBUG
    {
      ppl_Polyhedron_t ph;
      unsigned int relation;
      ppl_new_C_Polyhedron_recycle_Constraint_System(&ph, ppl_cs_copy);
      ppl_delete_Constraint_System(ppl_cs_copy);
      relation = ppl_Polyhedron_relation_with_Generator(ph, optimum_location);
      ppl_delete_Polyhedron(ph);
      assert(relation == PPL_POLY_GEN_RELATION_SUBSUMES);
    }
#endif
    maybe_check_results(PPL_MIP_PROBLEM_STATUS_OPTIMIZED,
                        mpq_get_d(optimum));
    mpq_clear(optimum);
  }

  ppl_delete_Constraint_System(ppl_cs);
  ppl_delete_Coefficient(optimum_d);
  ppl_delete_Coefficient(optimum_n);
  ppl_delete_Generator(optimum_location);

  glp_delete_prob(glpk_lp);
}
示例#12
0
int glp_mpl_postsolve(glp_tran *tran, glp_prob *prob, int sol)
{     /* postsolve the model */
      int i, j, m, n, stat, ret;
      double prim, dual;
      if (!(tran->phase == 3 && !tran->flag_p))
         xerror("glp_mpl_postsolve: invalid call sequence\n");
      if (!(sol == GLP_SOL || sol == GLP_IPT || sol == GLP_MIP))
         xerror("glp_mpl_postsolve: sol = %d; invalid parameter\n",
            sol);
      m = mpl_get_num_rows(tran);
      n = mpl_get_num_cols(tran);
      if (!(m == glp_get_num_rows(prob) &&
            n == glp_get_num_cols(prob)))
         xerror("glp_mpl_postsolve: wrong problem object\n");
      if (!mpl_has_solve_stmt(tran))
      {  ret = 0;
         goto done;
      }
      for (i = 1; i <= m; i++)
      {  if (sol == GLP_SOL)
         {  stat = glp_get_row_stat(prob, i);
            prim = glp_get_row_prim(prob, i);
            dual = glp_get_row_dual(prob, i);
         }
         else if (sol == GLP_IPT)
         {  stat = 0;
            prim = glp_ipt_row_prim(prob, i);
            dual = glp_ipt_row_dual(prob, i);
         }
         else if (sol == GLP_MIP)
         {  stat = 0;
            prim = glp_mip_row_val(prob, i);
            dual = 0.0;
         }
         else
            xassert(sol != sol);
         if (fabs(prim) < 1e-9) prim = 0.0;
         if (fabs(dual) < 1e-9) dual = 0.0;
         mpl_put_row_soln(tran, i, stat, prim, dual);
      }
      for (j = 1; j <= n; j++)
      {  if (sol == GLP_SOL)
         {  stat = glp_get_col_stat(prob, j);
            prim = glp_get_col_prim(prob, j);
            dual = glp_get_col_dual(prob, j);
         }
         else if (sol == GLP_IPT)
         {  stat = 0;
            prim = glp_ipt_col_prim(prob, j);
            dual = glp_ipt_col_dual(prob, j);
         }
         else if (sol == GLP_MIP)
         {  stat = 0;
            prim = glp_mip_col_val(prob, j);
            dual = 0.0;
         }
         else
            xassert(sol != sol);
         if (fabs(prim) < 1e-9) prim = 0.0;
         if (fabs(dual) < 1e-9) dual = 0.0;
         mpl_put_col_soln(tran, j, stat, prim, dual);
      }
      ret = mpl_postsolve(tran);
      if (ret == 3)
         ret = 0;
      else if (ret == 4)
         ret = 1;
done: return ret;
}
示例#13
0
OptSolutionData* GLPKRunSolver(int ProbType) {
	OptSolutionData* NewSolution = NULL;

	int NumVariables = glp_get_num_cols(GLPKModel);

	int Status = 0;
	if (ProbType == MILP) {
		Status = glp_simplex(GLPKModel, NULL); // Use default settings
		if (Status != 0) {
			FErrorFile() << "Failed to optimize problem." << endl;
			FlushErrorFile();
			return NULL;
		}
		Status = glp_intopt(GLPKModel, NULL); // Use default settings
		if (Status != 0) {
			FErrorFile() << "Failed to optimize problem." << endl;
			FlushErrorFile();
			return NULL;
		}
		NewSolution = new OptSolutionData;

		Status = glp_mip_status(GLPKModel);
		if (Status == GLP_UNDEF || Status == GLP_NOFEAS) {
			NewSolution->Status = INFEASIBLE;
			return NewSolution;
		} else if (Status == GLP_FEAS) {
			NewSolution->Status = UNBOUNDED;
			return NewSolution;
		} else if (Status == GLP_OPT) {
			NewSolution->Status = SUCCESS;
		} else {
			delete NewSolution;
			FErrorFile() << "Problem status unrecognized." << endl;
			FlushErrorFile();
			return NULL;
		}

		NewSolution->Objective = glp_mip_obj_val(GLPKModel);
	
		NewSolution->SolutionData.resize(NumVariables);
		for (int i=0; i < NumVariables; i++) {
			NewSolution->SolutionData[i] = glp_mip_col_val(GLPKModel, i+1);
		}
	} else if (ProbType == LP) {
		//First we check the basis matrix to ensure it is not singular
		if (glp_warm_up(GLPKModel) != 0) {
			glp_adv_basis(GLPKModel, 0);
		}
		Status = glp_simplex(GLPKModel, NULL); // Use default settings
		if (Status == GLP_EBADB) {  /* the basis is invalid; build some valid basis */
			glp_adv_basis(GLPKModel, 0);
			Status = glp_simplex(GLPKModel, NULL); // Use default settings
		}
		if (Status != 0) {
			FErrorFile() << "Failed to optimize problem." << endl;
			FlushErrorFile();
			return NULL;
		}
		NewSolution = new OptSolutionData;

		Status = glp_get_status(GLPKModel);
		if (Status == GLP_INFEAS || Status == GLP_NOFEAS || Status == GLP_UNDEF) {
			cout << "Model is infeasible" << endl;
			FErrorFile() << "Model is infeasible" << endl;
			FlushErrorFile();
			NewSolution->Status = INFEASIBLE;
			return NewSolution;
		} else if (Status == GLP_FEAS || Status == GLP_UNBND) {
			cout << "Model is unbounded" << endl;
			FErrorFile() << "Model is unbounded" << endl;
			FlushErrorFile();
			NewSolution->Status = UNBOUNDED;
			return NewSolution;
		} else if (Status == GLP_OPT) {
			NewSolution->Status = SUCCESS;
		} else {
			delete NewSolution;
			FErrorFile() << "Problem status unrecognized." << endl;
			FlushErrorFile();
			return NULL;
		}

		NewSolution->Objective = glp_get_obj_val(GLPKModel);
	
		NewSolution->SolutionData.resize(NumVariables);
		for (int i=0; i < NumVariables; i++) {
			NewSolution->SolutionData[i] = glp_get_col_prim(GLPKModel, i+1);
		}
	} else {
		FErrorFile() << "Optimization problem type cannot be handled by GLPK solver." << endl;
		FlushErrorFile();
		return NULL;
	}

	return NewSolution;
}
示例#14
0
// retrieve all missing values of LP/MILP
void Rglpk_retrieve_MP_from_file (char **file, int *type,
				  int *lp_n_constraints,
				  int *lp_n_objective_vars,
				  double *lp_objective_coefficients,
				  int *lp_constraint_matrix_i,
				  int *lp_constraint_matrix_j,
				  double *lp_constraint_matrix_values,
				  int *lp_direction_of_constraints,
				  double *lp_right_hand_side,
				  double *lp_left_hand_side,
				  int *lp_objective_var_is_integer,
				  int *lp_objective_var_is_binary,
				  int *lp_bounds_type,
				  double *lp_bounds_lower,
				  double *lp_bounds_upper,
				  int *lp_ignore_first_row,
				  int *lp_verbosity,
				  char **lp_constraint_names,
				  char **lp_objective_vars_names
				  ) {
  extern glp_prob *lp;
  glp_tran *tran;
  const char *str; 
  
  int i, j, lp_column_kind, tmp;
  int ind_offset, status;

  // Turn on/off Terminal Output
  if (*lp_verbosity==1)
    glp_term_out(GLP_ON);
  else
    glp_term_out(GLP_OFF);

  // create problem object
  if (lp)
    glp_delete_prob(lp);
  lp = glp_create_prob();

  // read file -> gets stored as an GLPK problem object 'lp'
  // which file type do we have?
  switch (*type){
  case 1: 
    // Fixed (ancient) MPS Format, param argument currently NULL
    status = glp_read_mps(lp, GLP_MPS_DECK, NULL, *file);
    break;
  case 2:
    // Free (modern) MPS format, param argument currently NULL
    status = glp_read_mps(lp, GLP_MPS_FILE, NULL, *file);
    break;
  case 3:
    // CPLEX LP Format
    status = glp_read_lp(lp, NULL, *file);
    break;
  case 4:
    // MATHPROG Format (based on lpx_read_model function)
    tran = glp_mpl_alloc_wksp();

    status = glp_mpl_read_model(tran, *file, 0);

    if (!status) {
        status = glp_mpl_generate(tran, NULL);
        if (!status) {
            glp_mpl_build_prob(tran, lp);
        }
    }
    glp_mpl_free_wksp(tran);
    break;    
  } 

  // if file read successfully glp_read_* returns zero
  if ( status != 0 ) {
    glp_delete_prob(lp);
    lp = NULL;
    error("Reading file %c failed.", *file);
  }
  
  if(*lp_verbosity==1)
    Rprintf("Retrieve column specific data ...\n");

  if(glp_get_num_cols(lp) != *lp_n_objective_vars) {
    glp_delete_prob(lp);
    lp = NULL;
    error("The number of columns is not as specified");
  }

  // retrieve column specific data (values, bounds and type)
  for (i = 0; i < *lp_n_objective_vars; i++) {
    lp_objective_coefficients[i] = glp_get_obj_coef(lp, i+1);
    
    // Note that str must not be freed befor we have returned
    // from the .C call in R! 
    str = glp_get_col_name(lp, i+1);    
    if (str){
      lp_objective_vars_names[i] = (char *) str;
    }
    
    lp_bounds_type[i]            = glp_get_col_type(lp, i+1);
    lp_bounds_lower[i]           = glp_get_col_lb  (lp, i+1);
    lp_bounds_upper[i]           = glp_get_col_ub  (lp, i+1);
    lp_column_kind               = glp_get_col_kind(lp, i+1);
    // set to TRUE if objective variable is integer or binary  
    switch (lp_column_kind){
    case GLP_IV: 
      lp_objective_var_is_integer[i] = 1;
      break;
    case GLP_BV:
      lp_objective_var_is_binary[i] = 1;
      break;
    }
  }
  
  ind_offset = 0;

  if(*lp_verbosity==1)
    Rprintf("Retrieve row specific data ...\n");

  if(glp_get_num_rows(lp) != *lp_n_constraints) {
    glp_delete_prob(lp);
    lp = NULL;
    error("The number of rows is not as specified");
  }

  // retrieve row specific data (right hand side, direction of constraints)
  for (i = *lp_ignore_first_row; i < *lp_n_constraints; i++) {
    lp_direction_of_constraints[i] = glp_get_row_type(lp, i+1);
    
    str = glp_get_row_name(lp, i + 1);
    if (str) { 
      lp_constraint_names[i] = (char *) str;
    }
    
    // the right hand side. Note we don't allow for double bounded or
    // free auxiliary variables 
    if( lp_direction_of_constraints[i] == GLP_LO )
      lp_right_hand_side[i] = glp_get_row_lb(lp, i+1);
    if( lp_direction_of_constraints[i] == GLP_UP )
      lp_right_hand_side[i] = glp_get_row_ub(lp, i+1);
    if( lp_direction_of_constraints[i] == GLP_FX )
      lp_right_hand_side[i] = glp_get_row_lb(lp, i+1);
    if( lp_direction_of_constraints[i] == GLP_DB  ){
      lp_right_hand_side[i] = glp_get_row_ub(lp, i+1);
      lp_left_hand_side[i] =  glp_get_row_lb(lp, i+1);
    }

    tmp = glp_get_mat_row(lp, i+1, &lp_constraint_matrix_j[ind_offset-1],
			           &lp_constraint_matrix_values[ind_offset-1]);
    if (tmp > 0)
      for (j = 0; j < tmp; j++)
	lp_constraint_matrix_i[ind_offset+j] = i+1;
	ind_offset += tmp; 
  }
  
  if(*lp_verbosity==1)
    Rprintf("Done.\n");

}
示例#15
0
文件: lp.c 项目: kleptog/pyglpk
static PyObject* LPX_Str(LPXObject *self) {
  // Returns a string representation of this object.
  return PyString_FromFormat
    ("<%s %d-by-%d at %p>", self->ob_type->tp_name,
     glp_get_num_rows(LP), glp_get_num_cols(LP), self);
}
示例#16
0
int lpx_get_num_cols(LPX *lp)
{     /* retrieve number of columns */
      return glp_get_num_cols(lp);
}
示例#17
0
int lpx_write_pb(LPX *lp, const char *fname, int normalized,
      int binarize)
{
  FILE* fp;
  int m,n,i,j,k,o,nonfree=0, obj_dir, dbl, *ndx, row_type, emptylhs=0;
  double coeff, *val, bound, constant/*=0.0*/;
  char* objconstname = "dummy_one";
  char* emptylhsname = "dummy_zero";

  /* Variables needed for possible binarization */
  /*LPX* tlp;*/
  IPP *ipp = NULL;
  /*tlp=lp;*/

  if(binarize) /* Transform integer variables to binary ones */
    {
      ipp = ipp_create_wksp();
      ipp_load_orig(ipp, lp);
      ipp_binarize(ipp);
      lp = ipp_build_prob(ipp);
    }
  fp = fopen(fname, "w");

  if(fp!= NULL)
    {
      xprintf(
          "lpx_write_pb: writing problem in %sOPB format to `%s'...\n",
              (normalized?"normalized ":""), fname);

      m = glp_get_num_rows(lp);
      n = glp_get_num_cols(lp);
      for(i=1;i<=m;i++)
        {
          switch(glp_get_row_type(lp,i))
            {
            case GLP_LO:
            case GLP_UP:
            case GLP_FX:
              {
                nonfree += 1;
                break;
              }
            case GLP_DB:
              {
                nonfree += 2;
                break;
              }
            }
        }
      constant=glp_get_obj_coef(lp,0);
      fprintf(fp,"* #variables = %d #constraints = %d\n",
         n + (constant == 0?1:0), nonfree + (constant == 0?1:0));
      /* Objective function */
      obj_dir = glp_get_obj_dir(lp);
      fprintf(fp,"min: ");
      for(i=1;i<=n;i++)
        {
          coeff = glp_get_obj_coef(lp,i);
          if(coeff != 0.0)
            {
              if(obj_dir == GLP_MAX)
                coeff=-coeff;
              if(normalized)
                fprintf(fp, " %d x%d", (int)coeff, i);
              else
                fprintf(fp, " %d*%s", (int)coeff,
                  glp_get_col_name(lp,i));

            }
        }
      if(constant)
        {
          if(normalized)
            fprintf(fp, " %d x%d", (int)constant, n+1);
          else
            fprintf(fp, " %d*%s", (int)constant, objconstname);
        }
      fprintf(fp,";\n");

      if(normalized && !binarize)  /* Name substitution */
        {
          fprintf(fp,"* Variable name substitution:\n");
          for(j=1;j<=n;j++)
            {
              fprintf(fp, "* x%d = %s\n", j, glp_get_col_name(lp,j));
            }
          if(constant)
            fprintf(fp, "* x%d = %s\n", n+1, objconstname);
        }

      ndx = xcalloc(1+n, sizeof(int));
      val = xcalloc(1+n, sizeof(double));

      /* Constraints */
      for(j=1;j<=m;j++)
        {
          row_type=glp_get_row_type(lp,j);
          if(row_type!=GLP_FR)
            {
              if(row_type == GLP_DB)
                {
                  dbl=2;
                  row_type = GLP_UP;
                }
              else
                {
                  dbl=1;
                }
              k=glp_get_mat_row(lp, j, ndx, val);
              for(o=1;o<=dbl;o++)
                {
                  if(o==2)
                    {
                      row_type = GLP_LO;
                    }
                  if(k==0) /* Empty LHS */
                    {
                      emptylhs = 1;
                      if(normalized)
                        {
                          fprintf(fp, "0 x%d ", n+2);
                        }
                      else
                        {
                          fprintf(fp, "0*%s ", emptylhsname);
                        }
                    }

                  for(i=1;i<=k;i++)
                    {
                      if(val[i] != 0.0)
                        {

                          if(normalized)
                            {
                              fprintf(fp, "%d x%d ",
              (row_type==GLP_UP)?(-(int)val[i]):((int)val[i]), ndx[i]);
                            }
                          else
                            {
                              fprintf(fp, "%d*%s ", (int)val[i],
                                      glp_get_col_name(lp,ndx[i]));
                            }
                        }
                    }
                  switch(row_type)
                    {
                    case GLP_LO:
                      {
                        fprintf(fp, ">=");
                        bound = glp_get_row_lb(lp,j);
                        break;
                      }
                    case GLP_UP:
                      {
                        if(normalized)
                          {
                            fprintf(fp, ">=");
                            bound = -glp_get_row_ub(lp,j);
                          }
                        else
                          {
                            fprintf(fp, "<=");
                            bound = glp_get_row_ub(lp,j);
                          }

                        break;
                      }
                    case GLP_FX:
                      {
                        fprintf(fp, "=");
                        bound = glp_get_row_lb(lp,j);
                        break;
                      }
                    }
                  fprintf(fp," %d;\n",(int)bound);
                }
            }
        }
      xfree(ndx);
      xfree(val);

      if(constant)
        {
          xprintf(
        "lpx_write_pb: adding constant objective function variable\n");

          if(normalized)
            fprintf(fp, "1 x%d = 1;\n", n+1);
          else
            fprintf(fp, "1*%s = 1;\n", objconstname);
        }
      if(emptylhs)
        {
          xprintf(
            "lpx_write_pb: adding dummy variable for empty left-hand si"
            "de constraint\n");

          if(normalized)
            fprintf(fp, "1 x%d = 0;\n", n+2);
          else
            fprintf(fp, "1*%s = 0;\n", emptylhsname);
        }

    }
  else
    {
      xprintf("Problems opening file for writing: %s\n", fname);
      return(1);
    }
  fflush(fp);
  if (ferror(fp))
    {  xprintf("lpx_write_pb: can't write to `%s' - %s\n", fname,
               strerror(errno));
    goto fail;
    }
  fclose(fp);


  if(binarize)
    {
      /* delete the resultant problem object */
      if (lp != NULL) lpx_delete_prob(lp);
      /* delete MIP presolver workspace */
      if (ipp != NULL) ipp_delete_wksp(ipp);
      /*lp=tlp;*/
    }
  return 0;
 fail: if (fp != NULL) fclose(fp);
  return 1;
}
示例#18
0
// read in all necessary elements for retrieving the LP/MILP
void Rglpk_read_file (char **file, int *type, 
		      int *lp_direction_of_optimization,
		      int *lp_n_constraints, int *lp_n_objective_vars,
		      int *lp_n_values_in_constraint_matrix,
		      int *lp_n_integer_vars, int *lp_n_binary_vars, 
		      char **lp_prob_name,
		      char **lp_obj_name,
		      int *lp_verbosity) {

  int status;
  extern glp_prob *lp;
  glp_tran *tran;
  const char *str; 
  
  // Turn on/off Terminal Output
  if (*lp_verbosity==1)
    glp_term_out(GLP_ON);
  else
    glp_term_out(GLP_OFF);

  // create problem object 
  if (lp)
    glp_delete_prob(lp);
  lp = glp_create_prob();

  // read file -> gets stored as an GLPK problem object 'lp'
  // which file type do we have?
  switch (*type){
  case 1: 
    // Fixed (ancient) MPS Format, param argument currently NULL
    status = glp_read_mps(lp, GLP_MPS_DECK, NULL, *file);
    break;
  case 2:
    // Free (modern) MPS format, param argument currently NULL
    status = glp_read_mps(lp, GLP_MPS_FILE, NULL, *file);
    break;
  case 3:
    // CPLEX LP Format
    status = glp_read_lp(lp, NULL, *file);
    break;
  case 4:
    // MATHPROG Format (based on lpx_read_model function)
    tran = glp_mpl_alloc_wksp();

    status = glp_mpl_read_model(tran, *file, 0);

    if (!status) {
        status = glp_mpl_generate(tran, NULL);
        if (!status) {
            glp_mpl_build_prob(tran, lp);
        }
    }
    glp_mpl_free_wksp(tran);
    break;    
  } 

  // if file read successfully glp_read_* returns zero
  if ( status != 0 ) {
    glp_delete_prob(lp);
    lp = NULL;
    error("Reading file %s failed", *file);
  }

  // retrieve problem name
  str = glp_get_prob_name(lp);
  if (str){
    *lp_prob_name = (char *) str;
  }

  // retrieve name of objective function
  str = glp_get_obj_name(lp);
  if (str){
    *lp_obj_name = (char *) str;
  }
  
  // retrieve optimization direction flag
  *lp_direction_of_optimization = glp_get_obj_dir(lp);  

  // retrieve number of constraints
  *lp_n_constraints = glp_get_num_rows(lp);  

  // retrieve number of objective variables
  *lp_n_objective_vars = glp_get_num_cols(lp);

  // retrieve number of non-zero elements in constraint matrix
  *lp_n_values_in_constraint_matrix = glp_get_num_nz(lp);

  // retrieve number of integer variables
  *lp_n_integer_vars = glp_get_num_int(lp);
  
  // retrieve number of binary variables
  *lp_n_binary_vars = glp_get_num_bin(lp);
}
示例#19
0
int c_glp_get_num_cols (glp_prob *lp){
 	return glp_get_num_cols (lp);
}
示例#20
0
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
      	 double *a, double *b, char *ctype, int *freeLB, double *lb,
      	 int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
      	 int save_pb, char *save_filename, char *filetype,
         double *xmin, double *fmin, double *status,
      	 double *lambda, double *redcosts, double *time, double *mem)
{
  int typx = 0;
  int method;

  clock_t t_start = clock();

  //Redirect standard output
  if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
  else glp_term_hook (NULL, NULL);

  //-- Create an empty LP/MILP object
  LPX *lp = lpx_create_prob ();

  //-- Set the sense of optimization
  if (sense == 1)
    glp_set_obj_dir (lp, GLP_MIN);
  else
    glp_set_obj_dir (lp, GLP_MAX);

  //-- Define the number of unknowns and their domains.
  glp_add_cols (lp, n);
  for (int i = 0; i < n; i++)
  {
    //-- Define type of the structural variables
    if (! freeLB[i] && ! freeUB[i]) {
      if ( lb[i] == ub[i] )
        glp_set_col_bnds (lp, i+1, GLP_FX, lb[i], ub[i]);
      else
        glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
    }
    else
	  {
      if (! freeLB[i] && freeUB[i])
        glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
      else
      {
        if (freeLB[i] && ! freeUB[i])
		      glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
	      else
		      glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
	    }
	  }

  // -- Set the objective coefficient of the corresponding
  // -- structural variable. No constant term is assumed.
  glp_set_obj_coef(lp,i+1,c[i]);

  if (isMIP)
    glp_set_col_kind (lp, i+1, vartype[i]);
  }

  glp_add_rows (lp, m);

  for (int i = 0; i < m; i++)
  {
    /*  If the i-th row has no lower bound (types F,U), the
        corrispondent parameter will be ignored.
        If the i-th row has no upper bound (types F,L), the corrispondent
        parameter will be ignored.
        If the i-th row is of S type, the i-th LB is used, but
        the i-th UB is ignored.
    */

    switch (ctype[i])
    {
      case 'F': typx = GLP_FR; break;
      // upper bound
	  case 'U': typx = GLP_UP; break;
      // lower bound
	  case 'L': typx = GLP_LO; break;
      // fixed constraint
	  case 'S': typx = GLP_FX; break;
      // double-bounded variable
      case 'D': typx = GLP_DB; break;
	}

    if ( typx == GLP_DB && -b[i] < b[i]) {
        glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
    }
    else if(typx == GLP_DB && -b[i] == b[i]) {
        glp_set_row_bnds (lp, i+1, GLP_FX, b[i], b[i]);
    }
    else {
    // this should be glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
        glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
    }

  }
  // Load constraint matrix A
  glp_load_matrix (lp, nz, rn, cn, a);

  // Save problem
  if (save_pb) {
    if (!strcmp(filetype,"cplex")){
      if (glp_write_lp (lp, NULL, save_filename) != 0) {
	        mexErrMsgTxt("glpk: unable to write the problem");
	        longjmp (mark, -1);
      }
    }else{
      if (!strcmp(filetype,"fixedmps")){
        if (glp_write_mps (lp, GLP_MPS_DECK, NULL, save_filename) != 0) {
            mexErrMsgTxt("glpk: unable to write the problem");
	        longjmp (mark, -1);
        }
      }else{
        if (!strcmp(filetype,"freemps")){
          if (glp_write_mps (lp, GLP_MPS_FILE, NULL, save_filename) != 0) {
              mexErrMsgTxt("glpk: unable to write the problem");
	          longjmp (mark, -1);
          }
        }else{// plain text
          if (lpx_print_prob (lp, save_filename) != 0) {
              mexErrMsgTxt("glpk: unable to write the problem");
	          longjmp (mark, -1);
          }
        }
      }
    }
  }
  //-- scale the problem data (if required)
  if (! glpIntParam[16] || lpsolver != 1) {
    switch ( glpIntParam[1] ) {
        case ( 0 ): glp_scale_prob( lp, GLP_SF_SKIP ); break;
        case ( 1 ): glp_scale_prob( lp, GLP_SF_GM ); break;
        case ( 2 ): glp_scale_prob( lp, GLP_SF_EQ ); break;
        case ( 3 ): glp_scale_prob( lp, GLP_SF_AUTO  ); break;
        case ( 4 ): glp_scale_prob( lp, GLP_SF_2N ); break;
        default :
            mexErrMsgTxt("glpk: unrecognized scaling option");
            longjmp (mark, -1);
    }
  }
  else {
    /* do nothing? or unscale?
        glp_unscale_prob( lp );
    */
  }

  //-- build advanced initial basis (if required)
  if (lpsolver == 1 && ! glpIntParam[16])
    glp_adv_basis (lp, 0);

  glp_smcp sParam;
  glp_init_smcp(&sParam);

  //-- set control parameters for simplex/exact method
  if (lpsolver == 1 || lpsolver == 3){
    //remap of control parameters for simplex method
    sParam.msg_lev=glpIntParam[0];	// message level

    // simplex method: primal/dual
    switch ( glpIntParam[2] ) {
        case 0: sParam.meth=GLP_PRIMAL; break;
        case 1: sParam.meth=GLP_DUAL;   break;
        case 2: sParam.meth=GLP_DUALP;  break;
        default:
            mexErrMsgTxt("glpk: unrecognized primal/dual method");
            longjmp (mark, -1);
    }

    // pricing technique
    if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
    else sParam.pricing=GLP_PT_PSE;

    // ratio test
    if (glpIntParam[20]==0) sParam.r_test = GLP_RT_STD;
    else sParam.r_test=GLP_RT_HAR;

    //tollerances
    sParam.tol_bnd=glpRealParam[1];	// primal feasible tollerance
    sParam.tol_dj=glpRealParam[2];	// dual feasible tollerance
    sParam.tol_piv=glpRealParam[3];	// pivot tollerance
    sParam.obj_ll=glpRealParam[4];	// lower limit
    sParam.obj_ul=glpRealParam[5];	// upper limit

    // iteration limit
    if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
    else sParam.it_lim=glpIntParam[5];

    // time limit
    if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
    else sParam.tm_lim=(int) glpRealParam[6];
    sParam.out_frq=glpIntParam[7];	// output frequency
    sParam.out_dly=(int) glpRealParam[7];	// output delay
    // presolver
    if (glpIntParam[16]) sParam.presolve=GLP_ON;
    else sParam.presolve=GLP_OFF;
  }else{
	for(int i = 0; i < NIntP; i++) {
        // skip assinging ratio test or
        if ( i == 18 || i == 20) continue;
		lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
    }

	for (int i = 0; i < NRealP; i++) {
		lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
    }
  }

  //set MIP params if MIP....
  glp_iocp iParam;
  glp_init_iocp(&iParam);

  if ( isMIP ){
    method = 'I';

    switch (glpIntParam[0]) { //message level
         case 0:  iParam.msg_lev = GLP_MSG_OFF;   break;
         case 1:  iParam.msg_lev = GLP_MSG_ERR;   break;
         case 2:  iParam.msg_lev = GLP_MSG_ON;    break;
         case 3:  iParam.msg_lev = GLP_MSG_ALL;   break;
         default:  mexErrMsgTxt("glpk: msg_lev bad param");
    }
    switch (glpIntParam[14]) { //branching param
         case 0:  iParam.br_tech = GLP_BR_FFV;    break;
         case 1:  iParam.br_tech = GLP_BR_LFV;    break;
         case 2:  iParam.br_tech = GLP_BR_MFV;    break;
         case 3:  iParam.br_tech = GLP_BR_DTH;    break;
         default: mexErrMsgTxt("glpk: branch bad param");
    }
    switch (glpIntParam[15]) { //backtracking heuristic
        case 0:  iParam.bt_tech = GLP_BT_DFS;    break;
        case 1:  iParam.bt_tech = GLP_BT_BFS;    break;
        case 2:  iParam.bt_tech = GLP_BT_BLB;    break;
        case 3:  iParam.bt_tech = GLP_BT_BPH;    break;
        default: mexErrMsgTxt("glpk: backtrack bad param");
    }

    if (  glpRealParam[8] > 0.0 && glpRealParam[8] < 1.0 )
        iParam.tol_int = glpRealParam[8];  // absolute tolorence
    else
        mexErrMsgTxt("glpk: tolint must be between 0 and 1");

    iParam.tol_obj = glpRealParam[9];  // relative tolarence
    iParam.mip_gap = glpRealParam[10]; // realative gap tolerance

    // set time limit for mip
    if ( glpRealParam[6] < 0.0 || glpRealParam[6] > 1e6 )
       iParam.tm_lim = INT_MAX;
    else
       iParam.tm_lim = (int)(1000.0 * glpRealParam[6] );

    // Choose Cutsets for mip
    // shut all cuts off, then start over....
    iParam.gmi_cuts = GLP_OFF;
    iParam.mir_cuts = GLP_OFF;
    iParam.cov_cuts = GLP_OFF;
    iParam.clq_cuts = GLP_OFF;

    switch( glpIntParam[17] ) {
        case 0: break;
        case 1: iParam.gmi_cuts = GLP_ON; break;
        case 2: iParam.mir_cuts = GLP_ON; break;
        case 3: iParam.cov_cuts = GLP_ON; break;
        case 4: iParam.clq_cuts = GLP_ON; break;
        case 5: iParam.clq_cuts = GLP_ON;
                iParam.gmi_cuts = GLP_ON;
                iParam.mir_cuts = GLP_ON;
                iParam.cov_cuts = GLP_ON;
                iParam.clq_cuts = GLP_ON; break;
        default: mexErrMsgTxt("glpk: cutset bad param");
    }

    switch( glpIntParam[18] ) { // pre-processing for mip
        case 0: iParam.pp_tech = GLP_PP_NONE; break;
        case 1: iParam.pp_tech = GLP_PP_ROOT; break;
        case 2: iParam.pp_tech = GLP_PP_ALL;  break;
        default:  mexErrMsgTxt("glpk: pprocess bad param");
    }

    if (glpIntParam[16])  iParam.presolve=GLP_ON;
    else                  iParam.presolve=GLP_OFF;

    if (glpIntParam[19])  iParam.binarize = GLP_ON;
    else                  iParam.binarize = GLP_OFF;

  }
  else {
     /* Choose simplex method ('S')
     or interior point method ('T')
     or Exact method          ('E')
     to solve the problem  */
    switch (lpsolver) {
      case 1: method = 'S'; break;
      case 2: method = 'T'; break;
      case 3: method = 'E'; break;
      default:
            mexErrMsgTxt("glpk:  lpsolver != lpsolver");
            longjmp (mark, -1);
    }
  }

	// now run the problem...
	int errnum = 0;

	switch (method) {
	case 'I':
		errnum = glp_intopt( lp, &iParam );
		errnum += 200; //this is to avoid ambiguity in the return codes.
		break;

	case 'S':
		errnum = glp_simplex(lp, &sParam);
		errnum += 100; //this is to avoid ambiguity in the return codes.
		break;

	case 'T':
		errnum = glp_interior(lp, NULL );
		errnum += 300; //this is to avoid ambiguity in the return codes.
		break;

	case 'E':
		errnum = glp_exact(lp, &sParam);
		errnum += 100; //this is to avoid ambiguity in the return codes.
		break;

	default:  /*xassert (method != method); */
		mexErrMsgTxt("glpk: method != method");
		longjmp (mark, -1);
	}

    if (errnum==100 || errnum==200 || errnum==300 || errnum==106 || errnum==107 || errnum==108 || errnum==109 || errnum==209 || errnum==214 || errnum==308) {

    // Get status and object value
    if (isMIP) {
      *status = glp_mip_status (lp);
      *fmin = glp_mip_obj_val (lp);
    }
    else {

      if (lpsolver == 1 || lpsolver == 3) {
        *status = glp_get_status (lp);
        *fmin = glp_get_obj_val (lp);
	  }
      else {
        *status = glp_ipt_status (lp);
        *fmin = glp_ipt_obj_val (lp);
	  }
    }

    // Get optimal solution (if exists)
    if (isMIP) {

      for (int i = 0; i < n; i++)
        xmin[i] = glp_mip_col_val (lp, i+1);
    }
    else {

      /* Primal values */
      for (int i = 0; i < n; i++) {

        if (lpsolver == 1 || lpsolver == 3)
              xmin[i] = glp_get_col_prim (lp, i+1);
        else
		      xmin[i] = glp_ipt_col_prim (lp, i+1);
      }

      /* Dual values */
      for (int i = 0; i < m; i++) {

        if (lpsolver == 1 || lpsolver == 3)
            lambda[i] = glp_get_row_dual (lp, i+1);
	    else
            lambda[i] = glp_ipt_row_dual (lp, i+1);
      }

      /* Reduced costs */
      for (int i = 0; i < glp_get_num_cols (lp); i++) {

        if (lpsolver == 1 || lpsolver == 3)
            redcosts[i] = glp_get_col_dual (lp, i+1);
        else
            redcosts[i] = glp_ipt_col_dual (lp, i+1);
      }

    }

    *time = (clock () - t_start) / CLOCKS_PER_SEC;

    size_t tpeak;
    glp_mem_usage(NULL, NULL, NULL, &tpeak);
    *mem=((double) tpeak) / (1024);

	lpx_delete_prob(lp);

    return 0;
  }
  else {
   // printf("errnum is %d\n", errnum);
  }

  lpx_delete_prob(lp);

  /* this shouldn't be nessiary with glp_deleted_prob, but try it
  if we have weird behavior again... */
  glp_free_env();


  *status = errnum;

  return errnum;
}
static void
update_quality (struct GAS_MLP_Handle *mlp, struct ATS_Address * address)
{
  GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Updating quality metrics for peer `%s'\n",
      GNUNET_i2s (&address->peer));

  GNUNET_assert (NULL != address);
  GNUNET_assert (NULL != address->mlp_information);
  GNUNET_assert (NULL != address->ats);

  struct MLP_information *mlpi = address->mlp_information;
  struct GNUNET_ATS_Information *ats = address->ats;
  GNUNET_assert (mlpi != NULL);

  int c;
  for (c = 0; c < GNUNET_ATS_QualityPropertiesCount; c++)
  {
    int index = mlp_lookup_ats(address, mlp->q[c]);

    if (index == GNUNET_SYSERR)
      continue;

    GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Updating address for peer `%s' value `%s': %f\n",
        GNUNET_i2s (&address->peer),
        mlp_ats_to_string(mlp->q[c]),
        (double) ats[index].value);

    int i = mlpi->q_avg_i[c];
    double * qp = mlpi->q[c];
    qp[i] = (double) ats[index].value;

    int t;
    for (t = 0; t < MLP_AVERAGING_QUEUE_LENGTH; t++)
    {
      GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Peer `%s': `%s' queue[%u]: %f\n",
        GNUNET_i2s (&address->peer),
        mlp_ats_to_string(mlp->q[c]),
        t,
        qp[t]);
    }

    if (mlpi->q_avg_i[c] + 1 < (MLP_AVERAGING_QUEUE_LENGTH))
      mlpi->q_avg_i[c] ++;
    else
      mlpi->q_avg_i[c] = 0;


    int c2;
    int c3;
    double avg = 0.0;
    switch (mlp->q[c])
    {
      case GNUNET_ATS_QUALITY_NET_DELAY:
        c3 = 0;
        for (c2 = 0; c2 < MLP_AVERAGING_QUEUE_LENGTH; c2++)
        {
          if (mlpi->q[c][c2] != -1)
          {
            double * t2 = mlpi->q[c] ;
            avg += t2[c2];
            c3 ++;
          }
        }
        if ((c3 > 0) && (avg > 0))
          /* avg = 1 / ((q[0] + ... + q[l]) /c3) => c3 / avg*/
          mlpi->q_averaged[c] = (double) c3 / avg;
        else
          mlpi->q_averaged[c] = 0.0;

        GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Peer `%s': `%s' average sum: %f, average: %f, weight: %f\n",
          GNUNET_i2s (&address->peer),
          mlp_ats_to_string(mlp->q[c]),
          avg,
          avg / (double) c3,
          mlpi->q_averaged[c]);

        break;
      case GNUNET_ATS_QUALITY_NET_DISTANCE:
        c3 = 0;
        for (c2 = 0; c2 < MLP_AVERAGING_QUEUE_LENGTH; c2++)
        {
          if (mlpi->q[c][c2] != -1)
          {
            double * t2 = mlpi->q[c] ;
            avg += t2[c2];
            c3 ++;
          }
        }
        if ((c3 > 0) && (avg > 0))
          /* avg = 1 / ((q[0] + ... + q[l]) /c3) => c3 / avg*/
          mlpi->q_averaged[c] = (double) c3 / avg;
        else
          mlpi->q_averaged[c] = 0.0;

        GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Peer `%s': `%s' average sum: %f, average: %f, weight: %f\n",
          GNUNET_i2s (&address->peer),
          mlp_ats_to_string(mlp->q[c]),
          avg,
          avg / (double) c3,
          mlpi->q_averaged[c]);

        break;
      default:
        break;
    }

    if ((mlpi->c_b != 0) && (mlpi->r_q[c] != 0))
    {

      /* Get current number of columns */
      int found = GNUNET_NO;
      int cols = glp_get_num_cols(mlp->prob);
      int *ind = GNUNET_malloc (cols * sizeof (int) + 1);
      double *val = GNUNET_malloc (cols * sizeof (double) + 1);

      /* Get the matrix row of quality */
      int length = glp_get_mat_row(mlp->prob, mlp->r_q[c], ind, val);
      GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "cols %i, length %i c_b %i\n", cols, length, mlpi->c_b);
      int c4;
      /* Get the index if matrix row of quality */
      for (c4 = 1; c4 <= length; c4++ )
      {
        if (mlpi->c_b == ind[c4])
        {
          /* Update the value */
          GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Updating quality `%s' column `%s' row `%s' : %f -> %f\n",
              mlp_ats_to_string(mlp->q[c]),
              glp_get_col_name (mlp->prob, ind[c4]),
              glp_get_row_name (mlp->prob, mlp->r_q[c]),
              val[c4],
              mlpi->q_averaged[c]);
          val[c4] = mlpi->q_averaged[c];
          found = GNUNET_YES;
          break;
        }
      }

      if (found == GNUNET_NO)
        {

          ind[length+1] = mlpi->c_b;
          val[length+1] = mlpi->q_averaged[c];
          GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "%i ind[%i] val[%i]:  %i %f\n", length+1,  length+1, length+1, mlpi->c_b, mlpi->q_averaged[c]);
          glp_set_mat_row (mlp->prob, mlpi->r_q[c], length+1, ind, val);
        }
      else
        {
        /* Get the index if matrix row of quality */
        glp_set_mat_row (mlp->prob, mlpi->r_q[c], length, ind, val);
        }

      GNUNET_free (ind);
      GNUNET_free (val);
    }
  }
}
示例#22
0
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
      	 double *a, double *b, char *ctype, int *freeLB, double *lb,
      	 int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
      	 int save_pb, char *save_filename, char *filetype, 
         double *xmin, double *fmin, double *status,
      	 double *lambda, double *redcosts, double *time, double *mem)
{
  int typx = 0;
  int method;

  clock_t t_start = clock();

  // Obsolete
  //lib_set_fault_hook (NULL, glpk_fault_hook);

  //Redirect standard output
  if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
  else glp_term_hook (NULL, NULL);

  //-- Create an empty LP/MILP object
  glp_prob *lp = glp_create_prob ();

  //-- Set the sense of optimization
  if (sense == 1)
    glp_set_obj_dir (lp, GLP_MIN);
  else
    glp_set_obj_dir (lp, GLP_MAX);

  //-- Define the number of unknowns and their domains.
  glp_add_cols (lp, n);
  for (int i = 0; i < n; i++)
  {
    //-- Define type of the structural variables
    if (! freeLB[i] && ! freeUB[i])
      glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
    else
	  {
      if (! freeLB[i] && freeUB[i])
        glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
      else
      {
        if (freeLB[i] && ! freeUB[i])
		      glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
	      else
		      glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
	    }
	  }
  
  // -- Set the objective coefficient of the corresponding
  // -- structural variable. No constant term is assumed.
  glp_set_obj_coef(lp,i+1,c[i]);

  if (isMIP)
    glp_set_col_kind (lp, i+1, vartype[i]);
  }

  glp_add_rows (lp, m);

  for (int i = 0; i < m; i++)
  {
    /*  If the i-th row has no lower bound (types F,U), the
        corrispondent parameter will be ignored.
        If the i-th row has no upper bound (types F,L), the corrispondent
        parameter will be ignored.
        If the i-th row is of S type, the i-th LB is used, but
        the i-th UB is ignored.
    */

    switch (ctype[i])
    {
      case 'F': typx = GLP_FR; break;
      // upper bound
	    case 'U': typx = GLP_UP; break;
      // lower bound
	    case 'L': typx = GLP_LO; break;
      // fixed constraint
	    case 'S': typx = GLP_FX; break;
      // double-bounded variable
      case 'D': typx = GLP_DB; break;
	  }
      
    glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);

  }
  // Load constraint matrix A
  glp_load_matrix (lp, nz, rn, cn, a);

  // Save problem
  if (save_pb) {
    if (!strcmp(filetype,"cplex")){
      if (lpx_write_cpxlp (lp, save_filename) != 0) {
	        mexErrMsgTxt("glpkcc: unable to write the problem");
	        longjmp (mark, -1);
      }
    }else{
      if (!strcmp(filetype,"fixedmps")){
        if (lpx_write_mps (lp, save_filename) != 0) {
          mexErrMsgTxt("glpkcc: unable to write the problem");
	        longjmp (mark, -1);  
        }
      }else{
        if (!strcmp(filetype,"freemps")){
          if (lpx_write_freemps (lp, save_filename) != 0) {
            mexErrMsgTxt("glpkcc: unable to write the problem");
	          longjmp (mark, -1);
          }
        }else{// plain text
          if (lpx_print_prob (lp, save_filename) != 0) {
            mexErrMsgTxt("glpkcc: unable to write the problem");
	          longjmp (mark, -1);
          } 
        } 
      }    
    } 
  }
  //-- scale the problem data (if required)
  if (glpIntParam[1] && (! glpIntParam[16] || lpsolver != 1))
    lpx_scale_prob (lp);

  //-- build advanced initial basis (if required)
  if (lpsolver == 1 && ! glpIntParam[16])
    lpx_adv_basis (lp);

  glp_smcp sParam;
  glp_init_smcp(&sParam);
  
  //-- set control parameters
  if (lpsolver==1){
    //remap of control parameters for simplex method
    sParam.msg_lev=glpIntParam[0];	// message level
    // simplex method: primal/dual
    if (glpIntParam[2]==0) sParam.meth=GLP_PRIMAL;		
    else sParam.meth=GLP_DUALP;
    // pricing technique
    if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
    else sParam.pricing=GLP_PT_PSE;
    //sParam.r_test not available
    sParam.tol_bnd=glpRealParam[1];	// primal feasible tollerance
    sParam.tol_dj=glpRealParam[2];	// dual feasible tollerance
    sParam.tol_piv=glpRealParam[3];	// pivot tollerance
    sParam.obj_ll=glpRealParam[4];	// lower limit
    sParam.obj_ul=glpRealParam[5];	// upper limit
    // iteration limit
    if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
    else sParam.it_lim=glpIntParam[5];   
    // time limit
    if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
    else sParam.tm_lim=(int) glpRealParam[6];	
    sParam.out_frq=glpIntParam[7];	// output frequency
    sParam.out_dly=(int) glpRealParam[7];	// output delay
    // presolver
    if (glpIntParam[16]) sParam.presolve=GLP_ON;
    else sParam.presolve=GLP_OFF;
  }else{
	for(int i = 0; i < NIntP; i++)
		lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
		
	for (int i = 0; i < NRealP; i++)
		lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
  }
  

  // Choose simplex method ('S') or interior point method ('T') to solve the problem
  if (lpsolver == 1)
    method = 'S';
  else
    method = 'T';
	
  int errnum;

  switch (method){
    case 'S': {
      if (isMIP){
	    method = 'I';
	    errnum = lpx_intopt (lp);
      }
      else{
		errnum = glp_simplex(lp, &sParam);
		errnum += 100; //this is to avoid ambiguity in the return codes.
	  }
    }
    break;

    case 'T': errnum = lpx_interior(lp); break;

    default:  xassert (method != method);
  }

  /*  errnum assumes the following results:
      errnum = 0 <=> No errors
      errnum = 1 <=> Iteration limit exceeded.
      errnum = 2 <=> Numerical problems with basis matrix.
  */
  if (errnum == LPX_E_OK || errnum==100){
    // Get status and object value
    if (isMIP)
    {
      *status = glp_mip_status (lp);
      *fmin = glp_mip_obj_val (lp);
    }
    else
    {
      if (lpsolver == 1)
      {
        *status = glp_get_status (lp);
        *fmin = glp_get_obj_val (lp);
	    }
      else
      {
        *status = glp_ipt_status (lp);
        *fmin = glp_ipt_obj_val (lp);
	    }
    }
    // Get optimal solution (if exists)
    if (isMIP)
    {
      for (int i = 0; i < n; i++)
        xmin[i] = glp_mip_col_val (lp, i+1);
    }
    else
    {
      /* Primal values */
      for (int i = 0; i < n; i++)
      {
        if (lpsolver == 1)
          xmin[i] = glp_get_col_prim (lp, i+1);
        else
		      xmin[i] = glp_ipt_col_prim (lp, i+1);
      }
      /* Dual values */
      for (int i = 0; i < m; i++)
      {
        if (lpsolver == 1) lambda[i] = glp_get_row_dual (lp, i+1);
	     else lambda[i] = glp_ipt_row_dual (lp, i+1);
      }
      /* Reduced costs */
      for (int i = 0; i < glp_get_num_cols (lp); i++)
      {
        if (lpsolver == 1) redcosts[i] = glp_get_col_dual (lp, i+1);
        else redcosts[i] = glp_ipt_col_dual (lp, i+1);
      }
    }

    *time = (clock () - t_start) / CLOCKS_PER_SEC;
    
   	glp_ulong tpeak;
    lib_mem_usage(NULL, NULL, NULL, &tpeak);
    *mem=(double)(4294967296.0 * tpeak.hi + tpeak.lo) / (1024);
       
	  glp_delete_prob (lp);
    return 0;
  }

  glp_delete_prob (lp);

  *status = errnum;

  return errnum;
}