void glpk_wrapper::get_error_bounds(double * errors) {
int n = domain.size();
int * nbr_non_zero = new int[n];
for (int i = 0; i < n; i++) {
errors[i] = INFINITY;
nbr_non_zero[i] = 0;
}
// get the error on the KKT condition
int sol;
if (solver_type == SIMPLEX || solver_type == EXACT) {
sol = GLP_SOL;
} else {
sol = GLP_IPT;
}
double ae_max; // largest absolute error
int ae_ind; // number of row (PE), column (DE), or variable (PB, DB) with the largest absolute error
double re_max; // largest relative error
int re_ind; // number of row (PE), column (DE), or variable (PB, DB) with the largest relative error
// GLP_KKT_PE — check primal equality constraints
glp_check_kkt(lp, sol, GLP_KKT_PE, &ae_max, &ae_ind, &re_max, &re_ind);
// a sparse vector for the coeffs in the row
int row_size = 1;
int * row_idx = new int[n + 1];
double * row_coeff = new double[n + 1];
// PE
// gives the distance between the auxiliary var and A * strucutral variable
int m = glp_get_num_rows(lp);
for (int i = 1; i <= m ; ++i) {
// get the coeffs for that constraint
row_size = glp_get_mat_row(lp, i, row_idx, row_coeff);
for (int j = 1; j <= row_size; j++) {
int v = row_idx[j] - 1; // GLPK indexing
nbr_non_zero[v] += 1;
int c = row_coeff[j];
// relative error
errors[v] = std::min(errors[v], get_row_value(i) * re_max / c);
// absolute error
errors[v] = std::min(errors[v], ae_max / c);
}
}
// variables that don't matter
for (int i = 0; i < n; i++) {
if (nbr_non_zero[i] == 0) {
errors[i] = 0;
}
DREAL_LOG_INFO << "glpk_wrapper::get_error_bounds: error for " << domain.get_name(i) << " is " << errors[i];
}
delete[] nbr_non_zero;
delete[] row_idx;
delete[] row_coeff;
}
CFG *cfg_build_graph(void *P_)
{ glp_prob *P = P_;
int m = P->m;
int n = P->n;
CFG *G;
int i, k, type, len, *ind;
double *val;
struct term *t;
/* create the conflict graph (number of its vertices cannot be
* greater than double number of binary variables) */
G = cfg_create_graph(n, 2 * glp_get_num_bin(P));
/* allocate working arrays */
ind = talloc(1+n, int);
val = talloc(1+n, double);
t = talloc(1+n, struct term);
/* analyze constraints to discover edge inequalities */
for (i = 1; i <= m; i++)
{ type = P->row[i]->type;
if (type == GLP_LO || type == GLP_DB || type == GLP_FX)
{ /* i-th row has lower bound */
/* analyze inequality sum (-a[j]) * x[j] <= -lb */
len = glp_get_mat_row(P, i, ind, val);
for (k = 1; k <= len; k++)
val[k] = -val[k];
analyze_ineq(P, G, len, ind, val, -P->row[i]->lb, t);
}
if (type == GLP_UP || type == GLP_DB || type == GLP_FX)
{ /* i-th row has upper bound */
/* analyze inequality sum (+a[j]) * x[j] <= +ub */
len = glp_get_mat_row(P, i, ind, val);
analyze_ineq(P, G, len, ind, val, +P->row[i]->ub, t);
}
}
/* free working arrays */
tfree(ind);
tfree(val);
tfree(t);
return G;
}
void LinearProblem::RemoveRow(int row) {
static int indices[MAX_VARS];
static double values[MAX_VARS];
int nonZeros = glp_get_mat_row(lp_, row, indices, values);
glp_set_row_bnds(lp_, row, GLP_FR, 0.0, 0.0);
// glpk ignores the 0's index of the array
int ind[2];
double val[2];
for (int i = 1; i <= nonZeros; ++i) {
ind[1] = indices[i];
val[1] = (isMax(colToVar_[indices[i]]) ? -1 : 1);
int r = glp_add_rows(lp_, 1);
glp_set_row_bnds(lp_, r, GLP_UP, 0.0, MINUS_INFTY);
glp_set_mat_row(lp_, r, 1, ind, val);
}
}
PyObject *LPX_GetMatrix(LPXObject *self)
{
int row, numrows, listi, i, nnz, rownz;
PyObject *retval;
numrows = glp_get_num_rows(LP);
nnz = glp_get_num_nz(LP);
retval = PyList_New(nnz);
if (nnz == 0 || retval == NULL)
return retval;
// We don't really need this much memory, but, eh...
int *ind = (int*)calloc(nnz, sizeof(int));
double *val = (double*)calloc(nnz, sizeof(double));
listi = 0;
for (row=1; row<=numrows; ++row) {
rownz = glp_get_mat_row(LP, row, ind-1, val-1);
if (rownz == 0)
continue;
for (i = 0; i < rownz; ++i) {
PyList_SET_ITEM(retval, listi++, Py_BuildValue("iid", row - 1, ind[i] - 1, val[i]));
}
/*
* Continue to downscale these vectors, freeing memory in C even
* as we use more memory in Python.
*/
nnz -= rownz;
if (nnz) {
ind = (int*)realloc(ind, nnz*sizeof(int));
val = (double*)realloc(val, nnz*sizeof(double));
}
}
free(ind);
free(val);
if (PyList_Sort(retval)) {
Py_DECREF(retval);
return NULL;
}
return retval;
}
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;
}
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);
}
}
}
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 lpx_get_mat_row(LPX *lp, int i, int ind[], double val[])
{ /* retrieve row of the constraint matrix */
return glp_get_mat_row(lp, i, ind, val);
}
int c_glp_get_mat_row (glp_prob *lp, int i, int ind[], double val[]){
return glp_get_mat_row (lp, i, ind, val);
}
// 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");
}
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;
}
