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;
}
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;
}
/* 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);
}
/* 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]);
}
}
/* 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;
}
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;
}
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;
}
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;
}
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;
}
/* 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;
}
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);
}
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;
}
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;
}
// 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");
}
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);
}
int lpx_get_num_cols(LPX *lp)
{ /* retrieve number of columns */
return glp_get_num_cols(lp);
}
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;
}
// 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);
}
int c_glp_get_num_cols (glp_prob *lp){
return glp_get_num_cols (lp);
}
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);
}
}
}
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;
}
