int main(void)
{ glp_prob *mip;
glp_tran *tran;
int ret;
mip = glp_create_prob();
tran = glp_mpl_alloc_wksp();
ret = glp_mpl_read_model(tran, "sudoku.mod", 1);
if (ret != 0)
{ fprintf(stderr, "Error on translating model\n");
goto skip;
}
ret = glp_mpl_read_data(tran, "sudoku.dat");
if (ret != 0)
{ fprintf(stderr, "Error on translating data\n");
goto skip;
}
ret = glp_mpl_generate(tran, NULL);
if (ret != 0)
{ fprintf(stderr, "Error on generating model\n");
goto skip;
}
glp_mpl_build_prob(tran, mip);
glp_simplex(mip, NULL);
glp_intopt(mip, NULL);
ret = glp_mpl_postsolve(tran, mip, GLP_MPL_MIP);
if (ret != 0)
fprintf(stderr, "Error on postsolving model\n");
skip: glp_mpl_free_wksp(tran);
glp_delete_prob(mip);
return 0;
}
void B2GlpkHasher::build_hash()
{
glp_simplex(_lp, NULL);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
parm.binarize = GLP_ON;
glp_intopt(_lp, &parm);
double motif_count = glp_mip_obj_val(_lp);
if((motif_count > 0) && (motif_count <= (_str_set.size() * 2)))
{
_motif_set = _trace_set;
B2HashMap<B2Trace, unsigned int> &trace_vals = _motif_set.trace_vals();
for(B2HashMap<B2Trace, unsigned int>::iterator trace_it = trace_vals.begin(); trace_it != trace_vals.end(); )
{
double is_motif = glp_mip_col_val(_lp, trace_it->second);
if(is_motif == 0)
{
_motif_set.erase(trace_it->first);
B2_HASH_MAP_ERASE(trace_vals, trace_it);
}
else
{
++trace_it;
};
};
}
else
{
b2_preproc_error_inc(B2_PREPROC_ERROR_BAD_MOTIF_SET, 1);
};
};
int CMyProblem::SolveMIP()
{
//инициализация параметров MIP-решателя
glp_iocp params;
glp_init_iocp(¶ms);
params.presolve=GLP_ON;
params.tm_lim=360000;
return glp_intopt(lp, ¶ms);
}
int main(void)
{
glp_prob *mip = glp_create_prob();
glp_set_prob_name(mip, "sample");
glp_set_obj_dir(mip, GLP_MAX);
// 拘束条件
// 具体的な関数は後で
glp_add_rows(mip, 3); // 拘束条件の数
glp_set_row_name(mip, 1, "c1"); glp_set_row_bnds(mip, 1, GLP_DB, 0.0, 20.0);
glp_set_row_name(mip, 2, "c2"); glp_set_row_bnds(mip, 2, GLP_DB, 0.0, 30.0);
glp_set_row_name(mip, 3, "c3"); glp_set_row_bnds(mip, 3, GLP_FX, 0.0, 0);
// 変数
// 変数そのものにかかる拘束は、拘束条件ではなくてこちらで管理
glp_add_cols(mip, 4); // 変数の数
glp_set_col_name(mip, 1, "x1");
glp_set_col_bnds(mip, 1, GLP_DB, 0.0, 40.0); glp_set_obj_coef(mip, 1, 1.0);
glp_set_col_name(mip, 2, "x2");
glp_set_col_bnds(mip, 2, GLP_LO, 0.0, 0.0); glp_set_obj_coef(mip, 2, 2.0);
glp_set_col_name(mip, 3, "x3");
glp_set_col_bnds(mip, 3, GLP_LO, 0.0, 0.0); glp_set_obj_coef(mip, 3, 3.0);
glp_set_col_kind(mip, 3, GLP_IV); // 整数値としての宣言
glp_set_col_name(mip, 4, "x4");
glp_set_col_bnds(mip, 4, GLP_DB, 2.0, 3.0); glp_set_obj_coef(mip, 4, 1.0);
glp_set_col_kind(mip, 4, GLP_IV); // 整数値としての宣言
int ia[1+9], ja[1+9];
double ar[1+9];
ia[1]=1,ja[1]=1,ar[1]=-1; // a[1,1] = -1
ia[2]=1,ja[2]=2,ar[2]=1; // a[1,2] = 1
ia[3]=1,ja[3]=3,ar[3]=1; // a[1,3] = 1
ia[4]=1,ja[4]=4,ar[4]=10; // a[1,4] = 10
ia[5]=2,ja[5]=1,ar[5]=1; // a[2,1] = 1
ia[6]=2,ja[6]=2,ar[6]=-3; // a[2,2] = -3
ia[7]=2,ja[7]=3,ar[7]=1; // a[2,3] = 1
ia[8]=3,ja[8]=2,ar[8]=1; // a[3,2] = 1
ia[9]=3,ja[9]=4,ar[9]=-3.5; // a[3,4] = -3.5
glp_load_matrix(mip, 9, ia, ja, ar);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
int err = glp_intopt(mip, &parm);
double z = glp_mip_obj_val(mip);
double x1 = glp_mip_col_val(mip, 1);
double x2 = glp_mip_col_val(mip, 2);
double x3 = glp_mip_col_val(mip, 3);
double x4 = glp_mip_col_val(mip, 4);
printf("\nz = %g; x1 = %g; x2 = %g; x3 = %g, x4 = %g\n", z, x1, x2, x3, x4);
// z = 122.5; x1 = 40; x2 = 10.5; x3 = 19.5, x4 = 3
glp_delete_prob(mip);
return 0;
}
void NADA() {
glp_prob *lp;
lp = glp_create_prob();
glp_add_cols(lp, 3);
glp_set_col_bnds(lp, 1, GLP_LO, 0.0, 3.0); // 0 <= x1
glp_set_col_bnds(lp, 2, GLP_LO, 0.0, 2.0); // 0 <= x2
glp_set_col_bnds(lp, 3, GLP_LO, 0.0, 0.0); // 0 <= x3
glp_set_obj_dir(lp, GLP_MAX); //max
glp_set_obj_coef(lp, 1, -3.0); // -3x1
glp_set_obj_coef(lp, 2, 4.0); // +4x2
glp_set_obj_coef(lp, 3, 11.0); // +11x3
// max -3x1 + 4x2 + 11 x3.
int indCol[123];
double val[123];
glp_add_rows(lp, 1);
indCol[1] = 1; val[1] = 10; // 10*x1
indCol[2] = 2; val[2] = 3; // 3*x2
glp_set_row_bnds(lp, 1, GLP_UP, 0.0, 15.0);// <=15
glp_set_mat_row(lp, 1, 2, indCol, val);// 10 x1 + 3 x2 <= 15
glp_add_rows(lp, 1);
indCol[1] = 3; val[1] = 9; // 9*x3
indCol[2] = 1; val[2] = 7; // 7*x1
glp_set_row_bnds(lp, 2, GLP_UP, 0.0, 38.0);// <=38
glp_set_mat_row(lp, 2, 2, indCol, val);// 7x1+9x2<=38
glp_add_rows(lp, 1);
indCol[1] = 3; val[1] = 15; // 15*x3
glp_set_row_bnds(lp, 3, GLP_LO, 0.0, 25.0);// >=25
glp_set_mat_row(lp, 3, 1, indCol, val);// 15x3 >=25
glp_set_col_kind(lp, 1, GLP_IV);// X1 EH INTEIRO
glp_set_col_kind(lp, 2, GLP_IV);// X2 EH INTEIRO
glp_set_col_kind(lp, 3, GLP_IV);// X3 EH INTEIRO
glp_intopt(lp, NULL); // acha solucao com restricao de integralidade
// glp_simplex(lp, NULL);
// printf("Solucao Otima: %.3f\n", glp_get_obj_val(lp));
// printf("X1: %.3f\n", glp_get_col_prim(lp, 1));
// printf("X2: %.3f\n", glp_get_col_prim(lp, 2));
// printf("X3: %.3f\n", glp_get_col_prim(lp, 3));
printf("Solucao Otima: %.3f\n", glp_mip_obj_val(lp));
printf("X1: %.3f\n", glp_mip_col_val(lp, 1));
printf("X2: %.3f\n", glp_mip_col_val(lp, 2));
printf("X3: %.3f\n", glp_mip_col_val(lp, 3));
// for (int est = 1; est <= nEstradas; ++est) {
// glp_set_col_bnds(lp, est, GLP_LO, 0.0, 0.0);
// }
}
double solve()
{
glp_smcp smcp;
glp_iocp iocp;
glp_init_smcp(&smcp); smcp.msg_lev = GLP_MSG_ERR;
glp_init_iocp(&iocp); iocp.msg_lev = GLP_MSG_ERR;
glp_load_matrix(ip_, ia_.size()-1, &ia_[0], &ja_[0], &ar_[0]);
glp_simplex(ip_, &smcp);
glp_intopt(ip_, &iocp);
return glp_mip_obj_val(ip_);
}
/* 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);
}
int main(int argc, char *argv[])
{
leEstradas();
glp_prob *lp = montarModeloInicial();
while(1){
glp_intopt(lp, NULL); // acha solucao com restricao de integralidade
printf("Solucao Otima: %.3f\n", glp_mip_obj_val(lp));
printf("X1: %.3f\n", glp_mip_col_val(lp, 1));
printf("X2: %.3f\n", glp_mip_col_val(lp, 2));
printf("X3: %.3f\n", glp_mip_col_val(lp, 3));
int arestaEscolhida[1234];
for(int est = 1; est) {
arestaEscolhida[est] = glp_mip_col_val(lp, est);
}
int verticesAlcancados[123];
for( ) ; // para contar se todos os vertices foram alcancados
// se tiverem sido, de um break e mostre a resposta;
encontraVerticesAlcancaveis(arestaEscolhida, verticesAlcancados);
glp_add_row(lp, 1);
int indCol[123];
double val[123];
int nCoef = 0;
for (int e = 1; e <= nArestas; ++e) {
Estrada estrada = estradas[e];
int nextremosAlcancados = verticesAlcancados[estrada.ori] +
verticesAlcancados[estrada.dest];
if (nextremosAlcancados == 1) {
indCol[nCoef + 1] = e;
val[nCoef + 1] = 1.0;
}
}
glp_set_mat_row(lp, glp_get_num_rows(lp), nCoef, indCol, val);
glp_set_row_bnds(lp, glp_get_num_rows(lp), GLP_LO, 2.0, 2.0);
system("PAUSE");
return EXIT_SUCCESS;
}
int c_glp_mip_solve(glp_prob *lp, int msg_lev, int br_tech, int bt_tech, int pp_tech,
int fp_heur, int tm_lim, int cuts, double mip_gap, int presolve){
glp_iocp iocp;
glp_mem_limit(10000);
// printf ("%d %d %d time\n", msg_lev, br_tech, tm_lim);
glp_init_iocp(&iocp);
iocp.msg_lev = msg_lev;
iocp.br_tech = br_tech;
iocp.bt_tech = bt_tech;
iocp.pp_tech = pp_tech;
// iocp.fp_heur = fp_heur ? GLP_ON : GLP_OFF;
// printf ("fp %d\n", iocp.fp_heur);
iocp.gmi_cuts = cuts & 1 ? GLP_ON : GLP_OFF;
iocp.mir_cuts = cuts & 2 ? GLP_ON : GLP_OFF;
iocp.cov_cuts = cuts & 4 ? GLP_ON : GLP_OFF;
iocp.clq_cuts = cuts & 8 ? GLP_ON : GLP_OFF;
iocp.mip_gap = mip_gap;
iocp.tm_lim = tm_lim;
// printf ("%d %d %d time\n", msg_lev, br_tech, tm_lim);
iocp.presolve = presolve ? GLP_ON : GLP_OFF;
return glp_intopt(lp, &iocp);
}
int main(int argc, char *argv[]) {
/* Structures de données propres à GLPK */
glp_prob *prob; // Déclaration d'un pointeur sur le problème
int ia[1 + NBCREUX];
int ja[1 + NBCREUX];
double ar[1 + NBCREUX]; // Déclaration des 3 tableaux servant à définir la partie creuse de la matrice des contraintes
/* Variables récupérant les résultats de la résolution du problème (fonction objectif et valeur des variables) */
int i, j;
double z;
double x[NBVAR];
// Autres variables
int * p = (int*)malloc(n * sizeof(int));
p[1] = 34;
p[2] = 6;
p[3] = 8;
p[4] = 17;
p[5] = 16;
p[6] = 5;
p[7] = 13;
p[8] = 21;
p[9] = 25;
p[10] = 31;
p[11] = 14;
p[12] = 13;
p[13] = 33;
p[14] = 9;
p[15] = 25;
p[16] = 25;
/* Transfert de ces données dans les structures utilisées par la bibliothèque GLPK */
prob = glp_create_prob(); /* allocation mémoire pour le problème */
glp_set_prob_name(prob, "wagons"); /* affectation d'un nom */
glp_set_obj_dir(prob, GLP_MIN); /* Il s'agit d'un problème de minimisation */
/* Déclaration du nombre de contraintes (nombre de lignes de la matrice des contraintes) */
glp_add_rows(prob, NBCONTR);
/* On commence par préciser les bornes sur les contraintes, les indices commencent à 1 (!) dans GLPK */
/* Premier ensemble de contraintes ( c = 1 ) */
for(i = 1; i <= n; i++) {
glp_set_row_bnds(prob, i, GLP_FX, 1.0, 1.0);
}
/* Second ensembles de contraintes (c <= 0 ) */
for(i = n + 1; i <= NBCONTR; i++) {
glp_set_row_bnds(prob, i, GLP_UP, 0.0, 0.0);
}
/* Déclaration du nombre de variables */
glp_add_cols(prob, NBVAR);
/* On précise le type des variables, les indices commencent à 1 également pour les variables! */
for(i = 1; i <= NBVAR - 1; i++) {
glp_set_col_bnds(prob, i, GLP_DB, 0.0, 1.0);
glp_set_col_kind(prob, i, GLP_BV); /* les variables sont binaires */
}
glp_set_col_bnds(prob, NBVAR, GLP_LO, 0.0, 0.0); /* La dernière variables est continue (par défaut) non négative */
/* Définition des coefficients des variables dans la fonction objectif */
for(i = 1;i <= n*m;i++) {
glp_set_obj_coef(prob,i,0.0); // Tous les coûts sont à 0 (sauf le dernier)
}
/* Dernier coût (qui vaut 1) */
glp_set_obj_coef(prob,n*m + 1,1.0);
/* Définition des coefficients non-nuls dans la matrice des contraintes, autrement dit les coefficients de la matrice creuse */
int pos = 1;
for(i = 1; i <= n; i++) {
for(j = 1; j <= m; j++) {
// Première moitié de la matrice
ja[pos] = (i - 1)*m + j;
ia[pos] = i;
ar[pos] = 1;
pos++;
// Deuxième moitié de la matrice
ja[pos] = (i - 1)*m + j;
ia[pos] = n + j;
ar[pos] = p[i];
pos++;
}
}
// ajout des -1 dans la dernière colonne
for(i = n + 1; i <= n + m; i++) {
ja[pos] = n*m + 1;
ia[pos] = i;
ar[pos] = -1;
pos++;
}
/* Chargement de la matrice dans le problème */
glp_load_matrix(prob,NBCREUX,ia,ja,ar);
/* Ecriture de la modélisation dans un fichier */
glp_write_lp(prob,NULL,"wagons.lp");
/* Résolution, puis lecture des résultats */
glp_simplex(prob,NULL); glp_intopt(prob,NULL); /* Résolution */
z = glp_mip_obj_val(prob); /* Récupération de la valeur optimale. Dans le cas d'un problème en variables continues, l'appel est différent : z = glp_get_obj_val(prob); */
for(i = 0;i < NBVAR; i++) x[i] = glp_mip_col_val(prob,i+1); /* Récupération de la valeur des variables, Appel différent dans le cas d'un problème en variables continues : for(i = 0;i < p.nbvar;i++) x[i] = glp_get_col_prim(prob,i+1); */
printf("z = %lf\n",z);
for(i = 0;i < NBVAR;i++) printf("x%c = %d, ",'B'+i,(int)(x[i] + 0.5)); /* un cast est ajouté, x[i] pourrait être égal à 0.99999... */
puts("");
/* Libération de la mémoire */
glp_delete_prob(prob);
free(p);
return 0;
}
/**
* Solves the MLP problem
*
* @param mlp the MLP Handle
* @param s_ctx context to return results
* @return GNUNET_OK if could be solved, GNUNET_SYSERR on failure
*/
int
mlp_solve_mlp_problem (struct GAS_MLP_Handle *mlp, struct GAS_MLP_SolutionContext *s_ctx)
{
int res;
struct GNUNET_TIME_Relative duration;
struct GNUNET_TIME_Absolute end;
struct GNUNET_TIME_Absolute start = GNUNET_TIME_absolute_get();
/* solve MLP problem */
res = glp_intopt(mlp->prob, &mlp->control_param_mlp);
if (res == 0)
{
/* The MLP problem instance has been successfully solved. */
}
else if (res == GLP_EITLIM)
{
/* simplex iteration limit has been exceeded. */
// TODO Increase iteration limit?
}
else if (res == GLP_ETMLIM)
{
/* Time limit has been exceeded. */
// TODO Increase time limit?
}
else
{
/* Problem was ill-defined, no way to handle that */
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving MLP problem failed: %s\n", mlp_solve_to_string(res));
return GNUNET_SYSERR;
}
end = GNUNET_TIME_absolute_get ();
duration = GNUNET_TIME_absolute_get_difference (start, end);
mlp->mlp_solved++;
mlp->mlp_total_duration =+ duration.rel_value;
s_ctx->mlp_duration = duration;
GNUNET_STATISTICS_update (mlp->stats,"# MLP problem solved", 1, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# MLP execution time (ms)", duration.rel_value, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# MLP execution time average (ms)",
mlp->mlp_total_duration / mlp->mlp_solved, GNUNET_NO);
/* Analyze problem status */
res = glp_mip_status(mlp->prob);
switch (res) {
/* solution is optimal */
case GLP_OPT:
/* solution is feasible */
case GLP_FEAS:
break;
/* Problem was ill-defined, no way to handle that */
default:
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving MLP problem failed, %s\n\n", mlp_status_to_string(res));
return GNUNET_SYSERR;
break;
}
return GNUNET_OK;
}
int main(int argc, char *argv[])
{
/* structures de données propres à GLPK */
glp_prob *prob; // déclaration d'un pointeur sur le problème
int ia[1 + NBCREUX];
int ja[1 + NBCREUX];
double ar[1 + NBCREUX]; // déclaration des 3 tableaux servant à définir la partie creuse de la matrice des contraintes
int p[N+1];
p[1] = 34;
p[2] = 6; p[3] = 8; p[4] = 17; p[5] = 16; p[6] = 5; p[7] = 13; p[8] = 21; p[9] = 25; p[10] = 31; p[11] = 14; p[12] = 13; p[13] = 33; p[14] = 9; p[15] = 25; p[16] = 25;
/* variables récupérant les résultats de la résolution du problème (fonction objectif et valeur des variables) */
int i,j,pos;
double z;
double x[NBVAR];
/* Les déclarations suivantes sont optionnelles, leur but est de donner des noms aux variables et aux contraintes.
Cela permet de lire plus facilement le modèle saisi si on en demande un affichage à GLPK, ce qui est souvent utile pour détecter une erreur! */
char nomcontr[NBCONTR][8]; /* ici, les contraintes seront nommées "caisse1", "caisse2",... */
char numero[NBCONTR][3]; /* pour un nombre à deux chiffres */
char nomvar[NBVAR][3]; /* "xA", "xB", ... */
/* Création d'un problème (initialement vide) */
prob = glp_create_prob(); /* allocation mémoire pour le problème */
glp_set_prob_name(prob, "wagons"); /* affectation d'un nom (on pourrait mettre NULL) */
glp_set_obj_dir(prob, GLP_MIN); /* Il s'agit d'un problème de minimisation, on utiliserait la constante GLP_MAX dans le cas contraire */
/* Déclaration du nombre de contraintes (nombre de lignes de la matrice des contraintes) : NBCONTR */
glp_add_rows(prob, NBCONTR);
/* On commence par préciser les bornes sur les constrainte, les indices des contraintes commencent à 1 (!) dans GLPK */
for(i = 1;i <= N;i++)
{
/* partie optionnelle : donner un nom aux contraintes */
strcpy(nomcontr[i-1], "caisse");
sprintf(numero[i-1], "%d", i);
strcat(nomcontr[i-1], numero[i-1]); /* Les contraintes sont nommés "salle1", "salle2"... */
glp_set_row_name(prob, i, nomcontr[i-1]); /* Affectation du nom à la contrainte i */
/* partie indispensable : les bornes sur les contraintes */
glp_set_row_bnds(prob, i, GLP_FX, 1.0, 1.0);
}
for(i = N+1;i <= NBCONTR;i++)
{
/* partie optionnelle : donner un nom aux contraintes */
strcpy(nomcontr[i-1], "chaMax");
sprintf(numero[i-1], "%d", i);
strcat(nomcontr[i-1], numero[i-1]); /* Les contraintes sont nommés "chargemax", "chargemax2"... */
glp_set_row_name(prob, i, nomcontr[i-1]); /* Affectation du nom à la contrainte i */
// il doit manquer un bout ici
glp_set_row_bnds(prob, i, GLP_UP, 0.0, 0.0);
//<=0
// on met cmax a gauche car c'est une variable
// il aura le coeff -1 dans la mat creuse
}
/* Déclaration du nombre de variables : NBVAR */
glp_add_cols(prob, NBVAR);
/* On précise le type des variables, les indices commencent à 1 également pour les variables! */
for(i = 1;i <= NBVAR;i++)
{
if(i==NBVAR){
sprintf(nomvar[i-1],"Cm");
glp_set_col_name(prob, i , nomvar[i-1]);
glp_set_col_bnds(prob, i, GLP_LO, 0.0, 0.0);
}else{
/* partie optionnelle : donner un nom aux variables */
sprintf(nomvar[i-1],"x%d",i-1);
glp_set_col_name(prob, i , nomvar[i-1]); /* Les variables sont nommées "xA", "xB"... afin de respecter les noms de variables de l'exercice 2.2 */
/* partie obligatoire : bornes éventuelles sur les variables, et type */
glp_set_col_bnds(prob, i, GLP_DB, 0.0, 1.0); /* bornes sur les variables, comme sur les contraintes */
glp_set_col_kind(prob, i, GLP_BV); /* les variables sont par défaut continues, nous précisons ici qu'elles sont binaires avec la constante GLP_BV, on utiliserait GLP_IV pour des variables entières */
}
}
/* définition des coefficients des variables dans la fonction objectif */
for(i = 1;i <= N*M;i++) glp_set_obj_coef(prob,i,0.0); // Tous les coûts sont ici à 0! Mais on doit specifier quand meme
glp_set_obj_coef(prob,N*M+1,1.0); // 1 fois cmax
/* Définition des coefficients non-nuls dans la matrice des contraintes, autrement dit les coefficients de la matrice creuse */
/* Les indices commencent également à 1 ! */
// pour i de 1 a n
//pour i de 1 a m
/* xij intervient dans la ligne i avec un coeff 1 et dans la ligne
n+j avec un coeff pi
ia -> i et n+j
ar -> 1 et pi
ja -> xij -> (i-1)*m+j
*/
pos = 1;
for(i=1; i<=N; i++){
for(j=1; j<=M; j++){
ia[pos] = i;
ja[pos] = (i-1)*M+j; ar[pos] = 1;
pos++;
ia[pos] = N+j;
ja[pos] = (i-1)*M+j; ar[pos] = p[i];
pos++;
}
}
//Cmax a -1 !!!
for(i=N+1; i<=N+M;i++){
ia[pos] = i;
ja[pos] = N*M+1; ar[pos] = -1;
pos++;
}
/* chargement de la matrice dans le problème */
glp_load_matrix(prob,NBCREUX,ia,ja,ar);
/* Optionnel : écriture de la modélisation dans un fichier (TRES utile pour debugger!) */
glp_write_lp(prob,NULL,"wagons.lp");
/* Résolution, puis lecture des résultats */
glp_simplex(prob,NULL); glp_intopt(prob,NULL); /* Résolution */
z = glp_mip_obj_val(prob); /* Récupération de la valeur optimale. Dans le cas d'un problème en variables continues, l'appel est différent : z = glp_get_obj_val(prob); */
for(i = 0;i < NBVAR; i++) x[i] = glp_mip_col_val(prob,i+1); /* Récupération de la valeur des variables, Appel différent dans le cas d'un problème en variables continues : for(i = 0;i < p.nbvar;i++) x[i] = glp_get_col_prim(prob,i+1); */
printf("z = %lf\n",z);
for(i = 0;i < NBVAR;i++) printf("x%d = %d, ",i,(int)(x[i] + 0.5)); /* un cast est ajouté, x[i] pourrait être égal à 0.99999... */
puts("");
/* libération mémoire */
glp_delete_prob(prob);
/* J'adore qu'un plan se déroule sans accroc! */
return 0;
}
static void
maybe_check_results(const int ppl_status, const double ppl_optimum_value) {
const char* ppl_status_string;
const char* glpk_status_string;
int glpk_status;
int treat_as_lp = 0;
glp_smcp glpk_smcp;
if (!check_results)
return;
if (no_mip || glpk_lp_num_int == 0)
treat_as_lp = 1;
glp_set_obj_dir(glpk_lp, (maximize ? GLP_MAX : GLP_MIN));
glp_init_smcp(&glpk_smcp);
/* Disable GLPK output. */
glpk_smcp.msg_lev = GLP_MSG_OFF;
if (treat_as_lp) {
/* Set the problem class to LP: MIP problems are thus treated as
LP ones. */
glp_exact(glpk_lp, &glpk_smcp);
glpk_status = glp_get_status(glpk_lp);
}
else {
/* MIP case. */
glp_simplex(glpk_lp, &glpk_smcp);
glpk_status = glp_get_status(glpk_lp);
if (glpk_status != GLP_NOFEAS && glpk_status != GLP_UNBND) {
glp_iocp glpk_iocp;
glp_init_iocp(&glpk_iocp);
/* Disable GLPK output. */
glpk_iocp.msg_lev = GLP_MSG_OFF;
glp_intopt(glpk_lp, &glpk_iocp);
glpk_status = glp_mip_status(glpk_lp);
}
}
/* If no_optimization is enabled, the second case is not possibile. */
if (!((ppl_status == PPL_MIP_PROBLEM_STATUS_UNFEASIBLE
&& glpk_status == GLP_NOFEAS)
|| (ppl_status == PPL_MIP_PROBLEM_STATUS_UNBOUNDED
&& glpk_status == GLP_UNBND)
|| (ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED
&& (glpk_status == GLP_OPT
/* If no_optimization is enabled, check if the problem is
unbounded for GLPK. */
|| (no_optimization && (glpk_status == GLP_UNBND
|| glpk_status == GLP_UNDEF)))))) {
if (ppl_status == PPL_MIP_PROBLEM_STATUS_UNFEASIBLE)
ppl_status_string = "unfeasible";
else if (ppl_status == PPL_MIP_PROBLEM_STATUS_UNBOUNDED)
ppl_status_string = "unbounded";
else if (ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED)
ppl_status_string = "optimizable";
else
ppl_status_string = "<?>";
switch (glpk_status) {
case GLP_NOFEAS:
glpk_status_string = "unfeasible";
break;
case GLP_UNBND:
glpk_status_string = "unbounded";
break;
case GLP_OPT:
glpk_status_string = "optimizable";
break;
case GLP_UNDEF:
glpk_status_string = "undefined";
break;
default:
glpk_status_string = "<?>";
break;
}
error("check failed: for GLPK the problem is %s, not %s",
glpk_status_string, ppl_status_string);
check_results_failed = 1;
}
else if (!no_optimization
&& ppl_status == PPL_MIP_PROBLEM_STATUS_OPTIMIZED) {
double glpk_optimum_value
= (treat_as_lp ? glp_get_obj_val(glpk_lp) : glp_mip_obj_val(glpk_lp));
if (fabs(ppl_optimum_value - glpk_optimum_value) > check_threshold) {
error("check failed: for GLPK the problem's optimum is %.20g,"
" not %.20g", glpk_optimum_value, ppl_optimum_value);
check_results_failed = 1;
}
}
return;
}
double solve_glp_grb(glp_prob *mip, wrapper_params *par){
GLPK_out = par->glp_out;
GRB_out = par->grb_out;
double obj_val;
/** GLPK: Generate Variable indexing **/
glp_create_index(mip);
/** GLPK: Generate LP **/
glp_write_mps(mip, GLP_MPS_FILE, NULL, "tmp.mps");
/************/
/** GUROBI **/
/************/
retGRB = GRBloadenv(&env, NULL);
if (retGRB || env == NULL)
{
fprintf(stderr, "Error: could not create environment\n");
exit(1);
}
retGRB = GRBsetintparam(env, "OutputFlag", GRB_out?1:0);
if (retGRB) freeMem();
//retGRB = GRBsetintparam(env, "Sensitivity", 1);
//if (retGRB) freeMem();
/** GUROBI: Read model **/
retGRB = GRBreadmodel(env, "tmp.mps", &model);
if (retGRB) freeMem();
/** Remove utility files from disk **/
//remove("tmp.mps");
/** GUROBI: Get environment **/
mipenv = GRBgetenv(model);
if (!mipenv) freeMem();
/** GUROBI: Set parameters **/
/** GUROBI: Ask for more precision **/
retGRB = GRBsetdblparam(mipenv, "FeasibilityTol", 10E-6);
if (retGRB) freeMem();
retGRB = GRBsetdblparam(mipenv, "IntFeasTol", 10E-5);
if (retGRB) freeMem();
retGRB = GRBsetdblparam(mipenv, "MIPgap", 10E-6);
if (retGRB) freeMem();
/* * Playing with gurobi parameters and attr*/
//gurobi_set_basis();
retGRB = GRBsetintparam(mipenv, "Cuts", 3);
if (retGRB) freeMem();
retGRB = GRBsetintparam(mipenv, "RootMethod", 1);
if (retGRB) freeMem();
retGRB = GRBsetintparam(mipenv, "Symmetry", -1);
if (retGRB) freeMem();
/** GUROBI: get numvars and numrows **/
retGRB = GRBgetintattr(model, "NumVars", &numvars);
if (retGRB) freeMem();
/** Test variable names */
for(int j=0;j<numvars;j++){
retGRB = GRBgetstrattrelement(model, "VarName", j, &nameGRB);
printf("GRB Var %d Name %s\n",j,nameGRB);
}
/** GUROBI: get model type **/
retGRB = GRBgetintattr(model, "IsMIP", &GRB_IsMIP);
if (retGRB) freeMem();
/** GUROBI: Optimize model **/
retGRB = GRBoptimize(model);
if (retGRB) freeMem();
/** GUROBI: Retreive the optimization status **/
GRBgetintattr(model, "Status", &retGRB);
switch(retGRB){
case GRB_OPTIMAL:
break;
case GRB_INFEASIBLE :
fprintf(stderr, "Error GRB optimization failed with code GRB_INFEASIBLE\n");
case GRB_INF_OR_UNBD :
fprintf(stderr, "Error GRB optimization failed with code GRB_INF_OR_UNBD \n");
case GRB_UNBOUNDED :
fprintf(stderr, "Error GRB optimization failed with code GRB_UNBOUNDED \n");
case GRB_CUTOFF :
fprintf(stderr, "Error GRB optimization failed with code GRB_CUTOFF \n");
case GRB_ITERATION_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_ITERATION_LIMIT \n");
case GRB_NODE_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_NODE_LIMIT \n");
case GRB_TIME_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_TIME_LIMIT \n");
case GRB_SOLUTION_LIMIT :
fprintf(stderr, "Error GRB optimization failed with code GRB_SOLUTION_LIMIT \n");
case GRB_INTERRUPTED :
fprintf(stderr, "Error GRB optimization failed with code GRB_INTERRUPTED \n");
case GRB_SUBOPTIMAL :
fprintf(stderr, "Error GRB optimization failed with code GRB_SUBOPTIMAL \n");
case GRB_NUMERIC :
fprintf(stderr, "Error GRB optimization failed with code GRB_NUMERIC \n");
/** GUROBI: Quit in any case non optimal **/
freeMem();
}
/** GUROBI: Get obj function value **/
retGRB = GRBgetdblattr(model, "IntVio", &tmp);
if (retGRB) freeMem();
retGRB = GRBgetdblattr(model, "ObjBound", &bound);
if (retGRB) freeMem();
retGRB = GRBgetdblattr(model, "ObjVal", &tmp);
if (retGRB) freeMem();
/* ********************** */
obj_val = tmp;
/* ************ */
if (verbose) printf ("Objective %lf\n", tmp);
if (verbose) printf ("Best bound %lf\n", bound);
if (verbose) printf ("Absolute gap %lf\n", fabs(tmp - bound));
/** GUROBI: Get variable values **/
for (j = 0; j < numvars; ++j){
retGRB = GRBgetdblattrelement(model, "X", j, &tmp);
if (retGRB) freeMem();
retGRB = GRBgetstrattrelement(model, "VarName", j, &nameGRB);
printf("GRB Var %d Name %s\n",j,nameGRB);
if (retGRB) freeMem();
retGRB = GRBgetcharattrelement(model, "VType", j, &type);
if (retGRB) freeMem();
/** GLPK search variable index by name **/
col_index = glp_find_col(mip, nameGRB);
if (col_index != 0){
/** GLPK set variable bounds **/
if ((type == 'B') || (type == 'I')){
if (verbose) printf ("Variable %s is of type %c value %lf fixed to %lf\n", nameGRB, type, tmp, round(tmp));
glp_set_col_bnds(mip, col_index, GLP_FX, round(tmp), round(tmp));
}
else{
if (verbose) printf ("Variable %s is of type %c value %lf fixed to %lf\n", nameGRB, type, tmp, tmp);
glp_set_col_bnds(mip, col_index, GLP_FX, tmp, tmp);
}
}
}
if (GRB_IsMIP){
/** GLPK initialize parameters **/
iparm = (glp_iocp*) malloc(sizeof(glp_iocp));
glp_init_iocp(iparm);
iparm->presolve = GLP_ON;
iparm->mip_gap = glpk_iparm_mip_gap;
iparm->tol_int = glpk_iparm_tol_int;
iparm->tol_obj = glpk_iparm_tol_obj;
/** GLPK get the optimal integer solution **/
ret = glp_intopt(mip, iparm);
if (ret){
fprintf(stderr, "glp_intopt, Error on optimizing the model : %d \n", ret);
freeMem();
}
ret = glp_mip_status(mip);
switch (ret){
case GLP_OPT:
break;
case GLP_FEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_FEAS, code %d\n", ret);
freeMem();
case GLP_NOFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_NOFEAS, code %d\n", ret);
freeMem();
case GLP_UNDEF:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNDEF, code %d\n", ret);
freeMem();
}
}
else{
/*GLPK initialize parameters */
parm = (glp_smcp*) malloc(sizeof(glp_smcp));
glp_init_smcp(parm);
parm->meth = GLP_DUALP;
parm->tol_bnd = 10E-4;
parm->tol_dj = 10E-4;
/* GLPK get the optimal basis */
//ret = glp_simplex(mip, parm);
if (ret){
fprintf(stderr, "glp_simplex, Error on optimizing the model : %d \n", ret);
freeMem();
}
ret = glp_get_status(mip);
switch (ret){
case GLP_OPT:
break;
case GLP_FEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_FEAS, code %d\n", ret);
freeMem();
case GLP_INFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_INFEAS, code %d\n", ret);
freeMem();
case GLP_NOFEAS:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_NOFEAS, code %d\n", ret);
freeMem();
case GLP_UNBND:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNBND, code %d\n", ret);
freeMem();
case GLP_UNDEF:
fprintf(stderr, "Error GLPK simplex is not optimal, GLP_UNDEF, code %d\n", ret);
freeMem();
}
}
//GRBmodel *fmod = fixed_model(model);
//gurobi_sens_output(fmod, "/tmp/sens.sol");
GRBwrite(model, "/tmp/model.sol");
/** GUROBI: free structures **/
if (model) GRBfreemodel(model);
if (env) GRBfreeenv(env);
return obj_val;
}
static PyObject *integer(PyObject *self, PyObject *args,
PyObject *kwrds)
{
matrix *c, *h, *b=NULL, *x=NULL;
PyObject *G, *A=NULL, *IntSet=NULL, *BinSet = NULL;
PyObject *t=NULL;
pyiocp *iocpParm = NULL;;
glp_iocp *options = NULL;
glp_prob *lp;
int m, n, p, i, j, k, nnz, nnzmax, *rn=NULL, *cn=NULL;
double *a=NULL, val;
char *kwlist[] = {"c", "G", "h", "A", "b", "I", "B","iocp", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OOO|OOOOO!", kwlist, &c,
&G, &h, &A, &b, &IntSet, &BinSet,iocp_t,&iocpParm)) return NULL;
if(!iocpParm)
{
iocpParm = (pyiocp*)malloc(sizeof(*iocpParm));
glp_init_iocp(&(iocpParm->obj));
}
if(iocpParm)
{
Py_INCREF(iocpParm);
options = &iocpParm->obj;
options->presolve = 1;
}
if ((Matrix_Check(G) && MAT_ID(G) != DOUBLE) ||
(SpMatrix_Check(G) && SP_ID(G) != DOUBLE) ||
(!Matrix_Check(G) && !SpMatrix_Check(G))){
PyErr_SetString(PyExc_TypeError, "G must be a 'd' matrix");
return NULL;
}
if ((m = Matrix_Check(G) ? MAT_NROWS(G) : SP_NROWS(G)) <= 0)
err_p_int("m");
if ((n = Matrix_Check(G) ? MAT_NCOLS(G) : SP_NCOLS(G)) <= 0)
err_p_int("n");
if (!Matrix_Check(h) || h->id != DOUBLE) err_dbl_mtrx("h");
if (h->nrows != m || h->ncols != 1){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if (A){
if ((Matrix_Check(A) && MAT_ID(A) != DOUBLE) ||
(SpMatrix_Check(A) && SP_ID(A) != DOUBLE) ||
(!Matrix_Check(A) && !SpMatrix_Check(A))){
PyErr_SetString(PyExc_ValueError, "A must be a dense "
"'d' matrix or a general sparse matrix");
return NULL;
}
if ((p = Matrix_Check(A) ? MAT_NROWS(A) : SP_NROWS(A)) < 0)
err_p_int("p");
if ((Matrix_Check(A) ? MAT_NCOLS(A) : SP_NCOLS(A)) != n){
PyErr_SetString(PyExc_ValueError, "incompatible "
"dimensions");
return NULL;
}
}
else p = 0;
if (b && (!Matrix_Check(b) || b->id != DOUBLE)) err_dbl_mtrx("b");
if ((b && (b->nrows != p || b->ncols != 1)) || (!b && p !=0 )){
PyErr_SetString(PyExc_ValueError, "incompatible dimensions");
return NULL;
}
if ((IntSet) && (!PyAnySet_Check(IntSet)))
PY_ERR_TYPE("invalid integer index set");
if ((BinSet) && (!PyAnySet_Check(BinSet)))
PY_ERR_TYPE("invalid binary index set");
lp = glp_create_prob();
glp_add_rows(lp, m+p);
glp_add_cols(lp, n);
for (i=0; i<n; i++){
glp_set_obj_coef(lp, i+1, MAT_BUFD(c)[i]);
glp_set_col_bnds(lp, i+1, GLP_FR, 0.0, 0.0);
}
for (i=0; i<m; i++)
glp_set_row_bnds(lp, i+1, GLP_UP, 0.0, MAT_BUFD(h)[i]);
for (i=0; i<p; i++)
glp_set_row_bnds(lp, i+m+1, GLP_FX, MAT_BUFD(b)[i],
MAT_BUFD(b)[i]);
nnzmax = (SpMatrix_Check(G) ? SP_NNZ(G) : m*n ) +
((A && SpMatrix_Check(A)) ? SP_NNZ(A) : p*n);
a = (double *) calloc(nnzmax+1, sizeof(double));
rn = (int *) calloc(nnzmax+1, sizeof(int));
cn = (int *) calloc(nnzmax+1, sizeof(int));
if (!a || !rn || !cn){
free(a); free(rn); free(cn); glp_delete_prob(lp);
return PyErr_NoMemory();
}
nnz = 0;
if (SpMatrix_Check(G)) {
for (j=0; j<n; j++) for (k=SP_COL(G)[j]; k<SP_COL(G)[j+1]; k++)
if ((val = SP_VALD(G)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = SP_ROW(G)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<m; i++)
if ((val = MAT_BUFD(G)[i+j*m]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = i+1;
cn[1+nnz] = j+1;
nnz++;
}
if (A && SpMatrix_Check(A)){
for (j=0; j<n; j++) for (k=SP_COL(A)[j]; k<SP_COL(A)[j+1]; k++)
if ((val = SP_VALD(A)[k]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+SP_ROW(A)[k]+1;
cn[1+nnz] = j+1;
nnz++;
}
}
else for (j=0; j<n; j++) for (i=0; i<p; i++)
if ((val = MAT_BUFD(A)[i+j*p]) != 0.0){
a[1+nnz] = val;
rn[1+nnz] = m+i+1;
cn[1+nnz] = j+1;
nnz++;
}
glp_load_matrix(lp, nnz, rn, cn, a);
free(rn); free(cn); free(a);
if (!(t = PyTuple_New(2))) {
glp_delete_prob(lp);
return PyErr_NoMemory();
}
if (IntSet) {
PyObject *iter = PySequence_Fast(IntSet, "Critical error: not sequence");
for (i=0; i<PySet_GET_SIZE(IntSet); i++) {
PyObject *tmp = PySequence_Fast_GET_ITEM(iter, i);
#if PY_MAJOR_VERSION >= 3
if (!PyLong_Check(tmp)) {
#else
if (!PyInt_Check(tmp)) {
#endif
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR_TYPE("non-integer element in I");
}
#if PY_MAJOR_VERSION >= 3
int k = PyLong_AS_LONG(tmp);
#else
int k = PyInt_AS_LONG(tmp);
#endif
if ((k < 0) || (k >= n)) {
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR(PyExc_IndexError, "index element out of range in I");
}
glp_set_col_kind(lp, k+1, GLP_IV);
}
Py_DECREF(iter);
}
if (BinSet) {
PyObject *iter = PySequence_Fast(BinSet, "Critical error: not sequence");
for (i=0; i<PySet_GET_SIZE(BinSet); i++) {
PyObject *tmp = PySequence_Fast_GET_ITEM(iter, i);
#if PY_MAJOR_VERSION >= 3
if (!PyLong_Check(tmp)) {
#else
if (!PyInt_Check(tmp)) {
#endif
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR_TYPE("non-binary element in I");
}
#if PY_MAJOR_VERSION >= 3
int k = PyLong_AS_LONG(tmp);
#else
int k = PyInt_AS_LONG(tmp);
#endif
if ((k < 0) || (k >= n)) {
glp_delete_prob(lp);
Py_DECREF(iter);
PY_ERR(PyExc_IndexError, "index element out of range in B");
}
glp_set_col_kind(lp, k+1, GLP_BV);
}
Py_DECREF(iter);
}
switch (glp_intopt(lp,options)){
case 0:
x = (matrix *) Matrix_New(n,1,DOUBLE);
if (!x) {
Py_XDECREF(iocpParm);
Py_XDECREF(t);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"optimal");
set_output_string(t,"optimal");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_mip_col_val(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_ETMLIM:
x = (matrix *) Matrix_New(n,1,DOUBLE);
if (!x) {
Py_XDECREF(t);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"time limit exceeded");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_mip_col_val(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_EBOUND:
set_output_string(t,"incorrect bounds");
break;
case GLP_EFAIL:
set_output_string(t,"invalid MIP formulation");
break;
case GLP_ENOPFS:
set_output_string(t,"primal infeasible");
break;
case GLP_ENODFS:
set_output_string(t,"dual infeasible");
break;
case GLP_EMIPGAP:
set_output_string(t,"Relative mip gap tolerance reached");
break;
/*case LPX_E_ITLIM:
set_output_string(t,"maxiters exceeded");
break;*/
/*case LPX_E_SING:
set_output_string(t,"singular or ill-conditioned basis");
break;*/
default:
set_output_string(t,"unknown");
}
Py_XDECREF(iocpParm);
glp_delete_prob(lp);
PyTuple_SET_ITEM(t, 1, Py_BuildValue(""));
return (PyObject *) t;
}
static PyMethodDef glpk_functions[] = {
{"lp", (PyCFunction) simplex, METH_VARARGS|METH_KEYWORDS,
doc_simplex},
{"ilp", (PyCFunction) integer, METH_VARARGS|METH_KEYWORDS,
doc_integer},
{NULL} /* Sentinel */
};
#if PY_MAJOR_VERSION >= 3
static PyModuleDef glpk_module_def = {
PyModuleDef_HEAD_INIT,
"glpk",
glpk__doc__,
-1,
glpk_functions,
NULL, NULL, NULL, NULL
};
void addglpkConstants (void)
{
PyModule_AddIntMacro(glpk_module, GLP_ON);
PyModule_AddIntMacro(glpk_module,GLP_OFF);
/* reason codes: */
PyModule_AddIntMacro(glpk_module,GLP_IROWGEN);
PyModule_AddIntMacro(glpk_module,GLP_IBINGO);
PyModule_AddIntMacro(glpk_module,GLP_IHEUR);
PyModule_AddIntMacro(glpk_module,GLP_ICUTGEN);
PyModule_AddIntMacro(glpk_module,GLP_IBRANCH);
PyModule_AddIntMacro(glpk_module,GLP_ISELECT);
PyModule_AddIntMacro(glpk_module,GLP_IPREPRO);
/* branch selection indicator: */
PyModule_AddIntMacro(glpk_module,GLP_NO_BRNCH);
PyModule_AddIntMacro(glpk_module,GLP_DN_BRNCH);
PyModule_AddIntMacro(glpk_module,GLP_UP_BRNCH);
/* return codes: */
PyModule_AddIntMacro(glpk_module,GLP_EBADB);
PyModule_AddIntMacro(glpk_module,GLP_ESING);
PyModule_AddIntMacro(glpk_module,GLP_ECOND);
PyModule_AddIntMacro(glpk_module,GLP_EBOUND);
PyModule_AddIntMacro(glpk_module,GLP_EFAIL);
PyModule_AddIntMacro(glpk_module,GLP_EOBJLL);
PyModule_AddIntMacro(glpk_module,GLP_EOBJUL);
PyModule_AddIntMacro(glpk_module,GLP_EITLIM);
PyModule_AddIntMacro(glpk_module,GLP_ETMLIM);
PyModule_AddIntMacro(glpk_module,GLP_ENOPFS);
PyModule_AddIntMacro(glpk_module,GLP_ENODFS);
PyModule_AddIntMacro(glpk_module,GLP_EROOT);
PyModule_AddIntMacro(glpk_module,GLP_ESTOP);
PyModule_AddIntMacro(glpk_module,GLP_EMIPGAP);
PyModule_AddIntMacro(glpk_module,GLP_ENOFEAS);
PyModule_AddIntMacro(glpk_module,GLP_ENOCVG);
PyModule_AddIntMacro(glpk_module,GLP_EINSTAB);
PyModule_AddIntMacro(glpk_module,GLP_EDATA);
PyModule_AddIntMacro(glpk_module,GLP_ERANGE);
/* condition indicator: */
PyModule_AddIntMacro(glpk_module,GLP_KKT_PE);
PyModule_AddIntMacro(glpk_module,GLP_KKT_PB);
PyModule_AddIntMacro(glpk_module,GLP_KKT_DE);
PyModule_AddIntMacro(glpk_module,GLP_KKT_DB);
PyModule_AddIntMacro(glpk_module,GLP_KKT_CS);
/* MPS file format: */
PyModule_AddIntMacro(glpk_module,GLP_MPS_DECK);
PyModule_AddIntMacro(glpk_module,GLP_MPS_FILE);
/* simplex method control parameters */
/* message level: */
PyModule_AddIntMacro(glpk_module,GLP_MSG_OFF);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ERR);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ON);
PyModule_AddIntMacro(glpk_module,GLP_MSG_ALL);
PyModule_AddIntMacro(glpk_module,GLP_MSG_DBG);
/* simplex method option: */
PyModule_AddIntMacro(glpk_module,GLP_PRIMAL);
PyModule_AddIntMacro(glpk_module,GLP_DUALP);
PyModule_AddIntMacro(glpk_module,GLP_DUAL);
/* pricing technique: */
PyModule_AddIntMacro(glpk_module,GLP_PT_STD);
PyModule_AddIntMacro(glpk_module,GLP_PT_PSE);
/* ratio test technique: */
PyModule_AddIntMacro(glpk_module,GLP_RT_STD);
PyModule_AddIntMacro(glpk_module,GLP_RT_HAR);
/* interior-point solver control parameters */
/* ordering algorithm: */
PyModule_AddIntMacro(glpk_module,GLP_ORD_NONE);
PyModule_AddIntMacro(glpk_module,GLP_ORD_QMD);
PyModule_AddIntMacro(glpk_module,GLP_ORD_AMD);
PyModule_AddIntMacro(glpk_module,GLP_ORD_SYMAMD);
}
PyMODINIT_FUNC PyInit_glpk(void)
{
if (!(glpk_module = PyModule_Create(&glpk_module_def))) return NULL;
if (PyType_Ready(&iocp_t) < 0 || (PyType_Ready(&smcp_t) < 0)) return NULL;
/* Adding macros */
addglpkConstants();
/* Adding option lists as objects */
Py_INCREF(&smcp_t);
PyModule_AddObject(glpk_module,"smcp",(PyObject*)&smcp_t);
Py_INCREF(&iocp_t);
PyModule_AddObject(glpk_module,"iocp",(PyObject*)&iocp_t);
if (import_cvxopt() < 0) return NULL;
return glpk_module;
}
#else
PyMODINIT_FUNC initglpk(void)
{
glpk_module = Py_InitModule3("cvxopt.glpk", glpk_functions,
glpk__doc__);
if (PyType_Ready(&iocp_t) < 0 || (PyType_Ready(&smcp_t) < 0)) return NULL;
addglpkConstants();
Py_INCREF(&smcp_t);
PyModule_AddObject(glpk_module,"smcp",(PyObject*)&smcp_t);
Py_INCREF(&iocp_t);
PyModule_AddObject(glpk_module,"iocp",(PyObject*)&iocp_t);
if (import_cvxopt() < 0) return;
}
static PyObject* LPX_solver_integer(LPXObject *self, PyObject *args,
PyObject *keywds) {
#if GLPK_VERSION(4, 20)
PyObject *callback=NULL;
struct mip_callback_object*info=NULL;
glp_iocp cp;
glp_init_iocp(&cp);
cp.msg_lev = GLP_MSG_OFF;
// Map the keyword arguments to the appropriate entries.
static char *kwlist[] =
{"msg_lev", "br_tech", "bt_tech",
#if GLPK_VERSION(4, 21)
"pp_tech",
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
"gmi_cuts",
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
"mir_cuts",
"mip_gap",
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
"cov_cuts", "clq_cuts", "presolve", "binarize",
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
"fp_heur",
#endif // GLPK_VERSION(4, 37)
"tol_int", "tol_obj", "tm_lim", "out_frq", "out_dly",
"callback", //"cb_info", "cb_size",
NULL};
if (!PyArg_ParseTupleAndKeywords
(args, keywds, "|iii"
#if GLPK_VERSION(4, 21)
"i"
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
"i"
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
"ii"
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
"iiii"
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
"i"
#endif // GLPK_VERSION(4, 37)
"ddiiiO", kwlist, &cp.msg_lev, &cp.br_tech, &cp.bt_tech,
#if GLPK_VERSION(4, 21)
&cp.pp_tech,
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
&cp.gmi_cuts,
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
&cp.mir_cuts,
&cp.mip_gap,
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
&cp.cov_cuts, &cp.clq_cuts, &cp.presolve, &cp.binarize,
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
&cp.fp_heur,
#endif // GLPK_VERSION(4, 37)
&cp.tol_int, &cp.tol_obj, &cp.tm_lim, &cp.out_frq, &cp.out_dly,
&callback)) {
return NULL;
}
#if GLPK_VERSION(4, 24)
cp.gmi_cuts = cp.gmi_cuts ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
cp.mir_cuts = cp.mir_cuts ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
cp.cov_cuts = cp.cov_cuts ? GLP_ON : GLP_OFF;
cp.clq_cuts = cp.clq_cuts ? GLP_ON : GLP_OFF;
cp.presolve = cp.presolve ? GLP_ON : GLP_OFF;
cp.binarize = cp.binarize ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
cp.fp_heur = cp.fp_heur ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 32)
// Do checking on the various entries.
#if GLPK_VERSION(4, 32)
if (!cp.presolve && glp_get_status(LP) != GLP_OPT) {
PyErr_SetString(PyExc_RuntimeError, "integer solver requires "
"use of presolver or existing optimal basic solution");
return NULL;
}
#else
if (glp_get_status(LP) != GLP_OPT) {
PyErr_SetString(PyExc_RuntimeError, "integer solver requires "
"existing optimal basic solution");
return NULL;
}
#endif
switch (cp.msg_lev) {
case GLP_MSG_OFF: case GLP_MSG_ERR: case GLP_MSG_ON: case GLP_MSG_ALL: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for msg_lev (LPX.MSG_* are valid values)");
return NULL;
}
switch (cp.br_tech) {
case GLP_BR_FFV: case GLP_BR_LFV: case GLP_BR_MFV: case GLP_BR_DTH: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for br_tech (LPX.BR_* are valid values)");
return NULL;
}
switch (cp.bt_tech) {
case GLP_BT_DFS: case GLP_BT_BFS: case GLP_BT_BLB: case GLP_BT_BPH: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for bt_tech (LPX.BT_* are valid values)");
return NULL;
}
#if GLPK_VERSION(4, 21)
switch (cp.pp_tech) {
case GLP_PP_NONE: case GLP_PP_ROOT: case GLP_PP_ALL: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for pp_tech (LPX.PP_* are valid values)");
return NULL;
}
#endif // GLPK_VERSION(4, 21)
if (cp.tol_int<=0 || cp.tol_int>=1) {
PyErr_SetString(PyExc_ValueError, "tol_int must obey 0<tol_int<1");
return NULL;
}
if (cp.tol_obj<=0 || cp.tol_obj>=1) {
PyErr_SetString(PyExc_ValueError, "tol_obj must obey 0<tol_obj<1");
return NULL;
}
if (cp.tm_lim<0) {
PyErr_SetString(PyExc_ValueError, "tm_lim must be non-negative");
return NULL;
}
if (cp.out_frq<=0) {
PyErr_SetString(PyExc_ValueError, "out_frq must be positive");
return NULL;
}
if (cp.out_dly<0) {
PyErr_SetString(PyExc_ValueError, "out_dly must be non-negative");
return NULL;
}
#if GLPK_VERSION(4, 23)
if (cp.mip_gap<0) {
PyErr_SetString(PyExc_ValueError, "mip_gap must be non-negative");
return NULL;
}
#endif
int retval;
if (callback != NULL && callback != Py_None) {
info = (struct mip_callback_object*)
malloc(sizeof(struct mip_callback_object));
info->callback = callback;
info->py_lp = self;
cp.cb_info = info;
cp.cb_func = mip_callback;
}
retval = glp_intopt(LP, &cp);
if (info) free(info);
if (PyErr_Occurred()) {
// This should happen only if there was a problem within the
// callback function, or if the callback was not appropriate.
return NULL;
}
if (retval!=GLP_EBADB && retval!=GLP_ESING && retval!=GLP_ECOND
&& retval!=GLP_EBOUND && retval!=GLP_EFAIL)
self->last_solver = 2;
return glpsolver_retval_to_message(retval);
#else
int retval = lpx_integer(LP);
if (retval!=LPX_E_FAULT) self->last_solver = 2;
return solver_retval_to_message(retval);
#endif // GLPK_VERSION(4, 20)
}
static int solve_mip(LPX *lp, int presolve)
{ glp_iocp parm;
int ret;
glp_init_iocp(&parm);
switch (lpx_get_int_parm(lp, LPX_K_MSGLEV))
{ case 0: parm.msg_lev = GLP_MSG_OFF; break;
case 1: parm.msg_lev = GLP_MSG_ERR; break;
case 2: parm.msg_lev = GLP_MSG_ON; break;
case 3: parm.msg_lev = GLP_MSG_ALL; break;
default: xassert(lp != lp);
}
switch (lpx_get_int_parm(lp, LPX_K_BRANCH))
{ case 0: parm.br_tech = GLP_BR_FFV; break;
case 1: parm.br_tech = GLP_BR_LFV; break;
case 2: parm.br_tech = GLP_BR_DTH; break;
case 3: parm.br_tech = GLP_BR_MFV; break;
default: xassert(lp != lp);
}
switch (lpx_get_int_parm(lp, LPX_K_BTRACK))
{ case 0: parm.bt_tech = GLP_BT_DFS; break;
case 1: parm.bt_tech = GLP_BT_BFS; break;
case 2: parm.bt_tech = GLP_BT_BPH; break;
case 3: parm.bt_tech = GLP_BT_BLB; break;
default: xassert(lp != lp);
}
parm.tol_int = lpx_get_real_parm(lp, LPX_K_TOLINT);
parm.tol_obj = lpx_get_real_parm(lp, LPX_K_TOLOBJ);
if (lpx_get_real_parm(lp, LPX_K_TMLIM) < 0.0 ||
lpx_get_real_parm(lp, LPX_K_TMLIM) > 1e6)
parm.tm_lim = INT_MAX;
else
parm.tm_lim =
(int)(1000.0 * lpx_get_real_parm(lp, LPX_K_TMLIM));
parm.mip_gap = lpx_get_real_parm(lp, LPX_K_MIPGAP);
if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_GOMORY)
parm.gmi_cuts = GLP_ON;
else
parm.gmi_cuts = GLP_OFF;
if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_MIR)
parm.mir_cuts = GLP_ON;
else
parm.mir_cuts = GLP_OFF;
if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_COVER)
parm.cov_cuts = GLP_ON;
else
parm.cov_cuts = GLP_OFF;
if (lpx_get_int_parm(lp, LPX_K_USECUTS) & LPX_C_CLIQUE)
parm.clq_cuts = GLP_ON;
else
parm.clq_cuts = GLP_OFF;
parm.presolve = presolve;
if (lpx_get_int_parm(lp, LPX_K_BINARIZE))
parm.binarize = GLP_ON;
ret = glp_intopt(lp, &parm);
switch (ret)
{ case 0: ret = LPX_E_OK; break;
case GLP_ENOPFS: ret = LPX_E_NOPFS; break;
case GLP_ENODFS: ret = LPX_E_NODFS; break;
case GLP_EBOUND:
case GLP_EROOT: ret = LPX_E_FAULT; break;
case GLP_EFAIL: ret = LPX_E_SING; break;
case GLP_EMIPGAP: ret = LPX_E_MIPGAP; break;
case GLP_ETMLIM: ret = LPX_E_TMLIM; break;
default: xassert(ret != ret);
}
return ret;
}
int main(int argc,char *argv[]){
int q[] = {atoi(argv[1]),atoi(argv[2]),atoi(argv[3])}; //queue length
int n[] = {atoi(argv[4]),atoi(argv[5]),atoi(argv[6])}; //different request number
float cmax = atof(argv[7]); //bandwidth limitation
glp_prob *mip = glp_create_prob();
glp_set_prob_name(mip, "sample");
glp_set_obj_dir(mip, GLP_MAX);
glp_add_rows(mip, 8);
glp_set_row_name(mip, 1, "n1");
if (n[0]==0)
glp_set_row_bnds(mip, 1, GLP_FX, 0.0, n[0]);
else
glp_set_row_bnds(mip, 1, GLP_DB, 0.0, n[0]);
glp_set_row_name(mip, 2, "n2");
if (n[1]==0)
glp_set_row_bnds(mip, 2, GLP_FX, 0.0, n[1]);
else
glp_set_row_bnds(mip, 2, GLP_DB, 0.0, n[1]);
glp_set_row_name(mip, 3, "n3");
if (n[2]==0)
glp_set_row_bnds(mip, 3, GLP_FX, 0.0, n[2]);
else
glp_set_row_bnds(mip, 3, GLP_DB, 0.0, n[2]);
glp_set_row_name(mip, 4, "c1");
glp_set_row_bnds(mip, 4, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 5, "c2");
glp_set_row_bnds(mip, 5, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 6, "c3");
glp_set_row_bnds(mip, 6, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 7, "c4");
glp_set_row_bnds(mip, 7, GLP_DB, 0.0, cmax);
glp_set_row_name(mip, 8, "c5");
glp_set_row_bnds(mip, 8, GLP_DB, 0.0, cmax);
glp_add_cols(mip, 15);
glp_set_col_name(mip, 1, "x11");
glp_set_col_bnds(mip, 1, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 1, q[0]); //queue length
glp_set_col_kind(mip, 1, GLP_IV);
glp_set_col_name(mip, 2, "x12");
glp_set_col_bnds(mip, 2, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 2, q[1]);
glp_set_col_kind(mip, 2, GLP_IV);
glp_set_col_name(mip, 3, "x13");
glp_set_col_bnds(mip, 3, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 3, q[2]);
glp_set_col_kind(mip, 3, GLP_IV);
glp_set_col_name(mip, 4, "x21");
glp_set_col_bnds(mip, 4, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 4, q[0]);
glp_set_col_kind(mip, 4, GLP_IV);
glp_set_col_name(mip, 5, "x22");
glp_set_col_bnds(mip, 5, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 5, q[1]);
glp_set_col_kind(mip, 5, GLP_IV);
glp_set_col_name(mip, 6, "x23");
glp_set_col_bnds(mip, 6, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 6, q[2]);
glp_set_col_kind(mip, 6, GLP_IV);
glp_set_col_name(mip, 7, "x31");
glp_set_col_bnds(mip, 7, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 7, q[0]);
glp_set_col_kind(mip, 7, GLP_IV);
glp_set_col_name(mip, 8, "x32");
glp_set_col_bnds(mip, 8, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 8, q[1]);
glp_set_col_kind(mip, 8, GLP_IV);
glp_set_col_name(mip, 9, "x33");
glp_set_col_bnds(mip, 9, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 9, q[2]);
glp_set_col_kind(mip, 9, GLP_IV);
glp_set_col_name(mip, 10, "x41");
glp_set_col_bnds(mip, 10, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 10, q[0]);
glp_set_col_kind(mip, 10, GLP_IV);
glp_set_col_name(mip, 11, "x42");
glp_set_col_bnds(mip, 11, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 11, q[1]);
glp_set_col_kind(mip, 11, GLP_IV);
glp_set_col_name(mip, 12, "x43");
glp_set_col_bnds(mip, 12, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 12, q[2]);
glp_set_col_kind(mip, 12, GLP_IV);
glp_set_col_name(mip, 13, "x51");
glp_set_col_bnds(mip, 13, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 13, q[0]);
glp_set_col_kind(mip, 13, GLP_IV);
glp_set_col_name(mip, 14, "x52");
glp_set_col_bnds(mip, 14, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 14, q[1]);
glp_set_col_kind(mip, 14, GLP_IV);
glp_set_col_name(mip, 15, "x53");
glp_set_col_bnds(mip, 15, GLP_LO, 0, 0);
glp_set_obj_coef(mip, 15, q[2]);
glp_set_col_kind(mip, 15, GLP_IV);
int ia[1+30], ja[1+30];
double ar[1+30];
int i=1,j=1,k=1;
for (i=1;i<4;i++)
for (j=1;j<6;j++){
ia[k]=i,ja[k]=(j-1)*3+i,ar[k]=1;
k++;
}
ia[16]=4,ja[16]=1,ar[16]=10;
ia[17]=4,ja[17]=2,ar[17]=1;
ia[18]=4,ja[18]=3,ar[18]=0.1;
ia[19]=5,ja[19]=4,ar[19]=10;
ia[20]=5,ja[20]=5,ar[20]=1;
ia[21]=5,ja[21]=6,ar[21]=0.1;
ia[22]=6,ja[22]=7,ar[22]=10;
ia[23]=6,ja[23]=8,ar[23]=1;
ia[24]=6,ja[24]=9,ar[24]=0.1;
ia[25]=7,ja[25]=10,ar[25]=10;
ia[26]=7,ja[26]=11,ar[26]=1;
ia[27]=7,ja[27]=12,ar[27]=0.1;
ia[28]=8,ja[28]=13,ar[28]=10;
ia[29]=8,ja[29]=14,ar[29]=1;
ia[30]=8,ja[30]=15,ar[30]=0.1;
/*
for (i=1;i<31;i++){
printf("%d,%d,%f\n",ia[i],ja[i],ar[i]);
}
*/
glp_load_matrix(mip, 30, ia, ja, ar);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
int err = glp_intopt(mip, &parm);
//glp_simplex(mip, NULL);
// double t = glp_mip_obj_val(mip);
int result[15]={0};
for (i=0;i<15;i++){
result[i] = glp_mip_col_val(mip, i+1);
}
printf("\n");
//display the result
for (i=0;i<14;i++){
printf("%d,",result[i]);
}
printf("%d\n",result[14]);
glp_delete_prob(mip);
return 0;
}
void inline instrumentert::instrument_minimum_interference_inserter(
const std::set<event_grapht::critical_cyclet>& set_of_cycles)
{
/* Idea:
We solve this by a linear programming approach,
using for instance glpk lib.
Input: the edges to instrument E, the cycles C_j
Pb: min sum_{e_i in E} d(e_i).x_i
s.t. for all j, sum_{e_i in C_j} >= 1,
where e_i is a pair to potentially instrument,
x_i is a Boolean stating whether we instrument
e_i, and d() is the cost of an instrumentation.
Output: the x_i, saying which pairs to instrument
For this instrumentation, we propose:
d(poW*)=1
d(poRW)=d(rfe)=2
d(poRR)=3
This function can be refined with the actual times
we get in experimenting the different pairs in a
single IRIW.
*/
#ifdef HAVE_GLPK
/* first, identify all the unsafe pairs */
std::set<event_grapht::critical_cyclet::delayt> edges;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
for(std::set<event_grapht::critical_cyclet::delayt>::const_iterator e_i=
C_j->unsafe_pairs.begin();
e_i!=C_j->unsafe_pairs.end();
++e_i)
edges.insert(*e_i);
glp_prob* lp;
glp_iocp parm;
glp_init_iocp(&parm);
parm.msg_lev=GLP_MSG_OFF;
parm.presolve=GLP_ON;
lp=glp_create_prob();
glp_set_prob_name(lp, "instrumentation optimisation");
glp_set_obj_dir(lp, GLP_MIN);
message.debug() << "edges: "<<edges.size()<<" cycles:"<<set_of_cycles.size()
<< messaget::eom;
/* sets the variables and coefficients */
glp_add_cols(lp, edges.size());
unsigned i=0;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
++i;
std::string name="e_"+i2string(i);
glp_set_col_name(lp, i, name.c_str());
glp_set_col_bnds(lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, i, cost(*e_i));
glp_set_col_kind(lp, i, GLP_BV);
}
/* sets the constraints (soundness): one per cycle */
glp_add_rows(lp, set_of_cycles.size());
i=0;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
{
++i;
std::string name="C_"+i2string(i);
glp_set_row_name(lp, i, name.c_str());
glp_set_row_bnds(lp, i, GLP_LO, 1.0, 0.0); /* >= 1*/
}
const unsigned mat_size=set_of_cycles.size()*edges.size();
message.debug() << "size of the system: " << mat_size
<< messaget::eom;
int* imat=(int*)malloc(sizeof(int)*(mat_size+1));
int* jmat=(int*)malloc(sizeof(int)*(mat_size+1));
double* vmat=(double*)malloc(sizeof(double)*(mat_size+1));
/* fills the constraints coeff */
/* tables read from 1 in glpk -- first row/column ignored */
unsigned col=1;
unsigned row=1;
i=1;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
row=1;
for(std::set<event_grapht::critical_cyclet>::iterator
C_j=set_of_cycles.begin();
C_j!=set_of_cycles.end();
++C_j)
{
imat[i]=row;
jmat[i]=col;
if(C_j->unsafe_pairs.find(*e_i)!=C_j->unsafe_pairs.end())
vmat[i]=1.0;
else
vmat[i]=0.0;
++i;
++row;
}
++col;
}
#ifdef DEBUG
for(i=1; i<=mat_size; ++i)
message.statistics() <<i<<"["<<imat[i]<<","<<jmat[i]<<"]="<<vmat[i]
<< messaget::eom;
#endif
/* solves MIP by branch-and-cut */
glp_load_matrix(lp, mat_size, imat, jmat, vmat);
glp_intopt(lp, &parm);
/* loads results (x_i) */
message.statistics() << "minimal cost: " << glp_mip_obj_val(lp)
<< messaget::eom;
i=0;
for(std::set<event_grapht::critical_cyclet::delayt>::iterator
e_i=edges.begin();
e_i!=edges.end();
++e_i)
{
++i;
if(glp_mip_col_val(lp, i)>=1)
{
const abstract_eventt& first_ev=egraph[e_i->first];
var_to_instr.insert(first_ev.variable);
id2loc.insert(
std::pair<irep_idt,source_locationt>(first_ev.variable,first_ev.source_location));
if(!e_i->is_po)
{
const abstract_eventt& second_ev=egraph[e_i->second];
var_to_instr.insert(second_ev.variable);
id2loc.insert(
std::pair<irep_idt,source_locationt>(second_ev.variable,second_ev.source_location));
}
}
}
glp_delete_prob(lp);
free(imat);
free(jmat);
free(vmat);
#else
throw "Sorry, minimum interference option requires glpk; "
"please recompile goto-instrument with glpk.";
#endif
}
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;
}
int main()
{
// Variáveis auxiliares
int i, j, constraintNumber, *constraintIndices;
double *constraintCoefficients;
// Aloca os vetores utilizados para criar as restrições do problema
// ***********************************************************************************************
// ATENÇÃO ===> É importante dizer que estes vetores serão utilizados da posição 1 em diante
// Ou seja, no GLPK você aloca uma posição a mais e descarta a posição 0 dos vetores.
// ***********************************************************************************************
constraintIndices = (int*)malloc((n+1)*sizeof(int));
constraintCoefficients = (double*)malloc((n+1)*sizeof(double));
// Cria um modelo com nenhuma variável e nenhuma restrição
glp_prob *model = glp_create_prob();
// Define o sentido da otimização que, para este problema, é minimização
glp_set_obj_dir(model, GLP_MIN);
// Cria as variáveis (colunas) no modelo
// Para este problema são necessárias n*n variáveis
// Estas n*n variáveis são definidas pelo GLPK através dos indices que vão de 1 até n*n (x1, x2, ..., x(n*n))
// Portanto, neste momento é importante determinar qual variável no GLPK (índice) representará qual variável x[i,j]
// Para tanto, fazemos o mapeamento das variáveis x[i,j] utilizando a fórmula (i-1)*n + j
// Isto é, a variável x[i,j] será representada pela variável de índice (i-1)*n + j no modelo do GLPK
// Note que é imprescindível que cada índice (variável do GLPK) seja associado a no máximo uma variável x[i,j]
// Caso contrário, uma variável do GLPK pode representar duas variáveis x[i,j] diferentes que assumem valores distintos nas soluções ótimas
// Neste caso, o modelo estará incorreto
glp_add_cols(model, n*n);
// Ajuste dos tipos, limitantes e coeficientes da função objetivo das variáveis do modelo
for (i = 1; i <= n; i++)
for (j = 1; j <= n; j++)
{
// Define o tipo da variável como sendo binária (em outros modelos poderia ser contínua (GLP_CV) ou inteira (GLP_IV))
glp_set_col_kind(model, (i-1)*n + j, GLP_BV);
// Define o limitante inferior (0) e superior (1) da variável
// Consultem no manual as outras forma para definir apenas o limitante inferior ou superior
glp_set_col_bnds(model, (i-1)*n + j, GLP_DB, 0.0, 1.0);
// Define o coeficiente da variável na função objetivo
glp_set_obj_coef(model, (i-1)*n + j, c[i-1][j-1]);
}
// Cria no modelo 2n restrições (linhas) nulas (com os coeficientes e limitantes zerados)
// Ou seja, neste momento é criada uma matriz de zeros que correspondem aos coeficientes das restrições do modelo
// O próximo passo será modificar esta matriz de tal forma que ela represente as restrições do problema descrito
glp_add_rows(model, 2*n);
// Esta variável define qual das restrições (qual linha da matriz de coeficientes) estamos modificando
// ************************************************************************************************************
// ATENÇÃO: perceba que as restrições (linhas), assim como as variáveis (colunas), são indexadas a partir de 1.
// ************************************************************************************************************
constraintNumber = 1;
// Preenchimento das restrições limitando a soma das linhas:
// sum{j in 1..n} w[i,j]*x[i,j] <= u[i] para i in 1..n
for (i = 1; i <= n; i++)
{
// Define o limite superior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_UP, 0.0, u[i-1]);
for (j = 1; j <= n; j++)
{
// Ajusta o índice da variável que será informado à rotina do GLPK
constraintIndices[j] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
// ******************************************************************************************************
// ATENÇÃO: perceba que na matriz w os índices e colunas são indexados a partir de ZERO !
// ******************************************************************************************************
constraintCoefficients[j] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Preenchimento das restrições limitando a soma das colunas:
// sum{i in 1..n} w[i,j]*x[i,j] >= l[i] para j in 1..n
for (j = 1; j <= n; j++)
{
// Define o limite inferior (RHS) da restrição
glp_set_row_bnds(model, constraintNumber, GLP_LO, l[j-1], 0.0);
for (i = 1; i <= n; i++)
{
// Ajusta o índice da variável que será informado a rotina do GLPK
constraintIndices[i] = (i-1)*n + j;
// Ajusta o coeficiente da variável cujo índice foi definido na linha anterior para ser informado ao GLPK
constraintCoefficients[i] = w[i-1][j-1];
}
// Passa ao GLPK a restrição que acabou de ser definida nos vetores constraintIndices e constraintCoefficients
glp_set_mat_row(model, constraintNumber, n, constraintIndices, constraintCoefficients);
// atualiza o indice da próxima restrição a ser inserida
constraintNumber++;
}
// Define os parâmetros que serão passados ao resolvedor
glp_iocp param;
glp_init_iocp(¶m);
// Ativa o presolver
param.presolve = GLP_ON;
// Resolve o modelo
int status = glp_intopt(model, ¶m);
// Verifica se houve algum erro durante a otimização
if (status)
{
printf("Ocorreu um erro durante o processo de otimizacao.\n");
}
else
{
// Verifica se o método encontrou uma solução
status = glp_mip_status(model);
if ((status == GLP_OPT) || (status == GLP_FEAS))
{
// Imprime a solução encontrada
if (status == GLP_OPT)
printf("Solucao otima encontrada!\n");
else
printf("A solucao encontrada pode nao ser otima!\n");
printf("Custo da solucao: %f\n", glp_mip_obj_val(model));
for (i = 1; i <= n; i++)
{
for (j = 1; j <= n; j++)
printf("%f ", glp_mip_col_val(model, (i-1)*n + j));
printf("\n");
}
}
else
{
printf("Nenhuma solucao foi encontrada!\n");
}
}
// Desaloca os vetores
free(constraintIndices);
free(constraintCoefficients);
return 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;
}
void ILP::sample() {
PPReader& reader = handler->get_reader();
// Read in all the data to this->data
point *p;
while ((p = reader.get())) {
data.push_back(*p);
}
delete p;
// Encode the problem into ILP
uint N = data.size();
glp_prob *lp = glp_create_prob();
glp_set_obj_dir(lp, GLP_MIN);
glp_add_rows(lp, 1 + 3*N*(N-1)/2);
glp_add_cols(lp, N + N*(N-1)/2);
vector<int> ia(1, 0);
vector<int> ja(1, 0);
vector<double> ar(1, 0.0);
for (uint i = 1; i <= N; i++) {
glp_set_obj_coef(lp, i, 0);
glp_set_col_bnds(lp, i, GLP_DB, 0.0, 1.0);
glp_set_col_kind(lp, i, GLP_BV);
}
for (uint i = 1; i <= N; i++) {
ia.push_back(1);
ja.push_back(i);
ar.push_back(1.0);
}
glp_set_row_bnds(lp, 1, GLP_FX, K, K);
// Set:
// 1. coefficient for objective
// 2. row bounds
// 3. column bounds
// 4. coefficient matrix for constraints
uint k = 1;
for (uint i = 1; i <= N-1; i++) {
for (uint j = i+1; j <= N; j++) {
point a = data[i-1];
point b = data[j-1];
// object coefficient and column bounds
double l = handler->loss(handler->dist(a, b));
glp_set_obj_coef(lp, N+k, l);
glp_set_col_bnds(lp, N+k, GLP_DB, 0.0, 1.0);
glp_set_col_kind(lp, N+k, GLP_BV);
// row bounds
glp_set_row_bnds(lp, 1 + (k-1)*3 + 1, GLP_LO, 0.0, 1.0);
glp_set_row_bnds(lp, 1 + (k-1)*3 + 2, GLP_LO, 0.0, 1.0);
glp_set_row_bnds(lp, 1 + (k-1)*3 + 3, GLP_LO, -1.0, 1.0);
// coefficient matrix
ia.push_back(1 + (k-1)*3 + 1);
ja.push_back(i);
ar.push_back(1.0);
ia.push_back(1 + (k-1)*3 + 1);
ja.push_back(N+k);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 2);
ja.push_back(j);
ar.push_back(1.0);
ia.push_back(1 + (k-1)*3 + 2);
ja.push_back(N+k);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(i);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(j);
ar.push_back(-1.0);
ia.push_back(1 + (k-1)*3 + 3);
ja.push_back(N+k);
ar.push_back(1.0);
k++;
}
}
int* ia_a = &ia[0];
int* ja_a = &ja[0];
double* ar_a = &ar[0];
glp_load_matrix(lp, ia.size()-1, ia_a, ja_a, ar_a);
glp_iocp parm;
glp_init_iocp(&parm);
parm.presolve = GLP_ON;
parm.msg_lev = GLP_MSG_OFF;
int ret = glp_intopt(lp, &parm);
// cout << "program returned: " << ret << endl;
// Collect processed results
for (uint i = 1; i <= N; i++) {
double x = glp_mip_col_val(lp, i);
if (x == 1.0) {
sampled.push_back(data[i-1]);
}
}
}
int glp_main(int argc, const char *argv[])
{ /* stand-alone LP/MIP solver */
struct csa _csa, *csa = &_csa;
int ret;
xlong_t start;
/* perform initialization */
csa->prob = glp_create_prob();
glp_get_bfcp(csa->prob, &csa->bfcp);
glp_init_smcp(&csa->smcp);
csa->smcp.presolve = GLP_ON;
glp_init_iocp(&csa->iocp);
csa->iocp.presolve = GLP_ON;
csa->tran = NULL;
csa->graph = NULL;
csa->format = FMT_MPS_FILE;
csa->in_file = NULL;
csa->ndf = 0;
csa->out_dpy = NULL;
csa->solution = SOL_BASIC;
csa->in_res = NULL;
csa->dir = 0;
csa->scale = 1;
csa->out_sol = NULL;
csa->out_res = NULL;
csa->out_bnds = NULL;
csa->check = 0;
csa->new_name = NULL;
csa->out_mps = NULL;
csa->out_freemps = NULL;
csa->out_cpxlp = NULL;
csa->out_pb = NULL;
csa->out_npb = NULL;
csa->log_file = NULL;
csa->crash = USE_ADV_BASIS;
csa->exact = 0;
csa->xcheck = 0;
csa->nomip = 0;
/* parse command-line parameters */
ret = parse_cmdline(csa, argc, argv);
if (ret < 0)
{ ret = EXIT_SUCCESS;
goto done;
}
if (ret > 0)
{ ret = EXIT_FAILURE;
goto done;
}
/*--------------------------------------------------------------*/
/* remove all output files specified in the command line */
if (csa->out_dpy != NULL) remove(csa->out_dpy);
if (csa->out_sol != NULL) remove(csa->out_sol);
if (csa->out_res != NULL) remove(csa->out_res);
if (csa->out_bnds != NULL) remove(csa->out_bnds);
if (csa->out_mps != NULL) remove(csa->out_mps);
if (csa->out_freemps != NULL) remove(csa->out_freemps);
if (csa->out_cpxlp != NULL) remove(csa->out_cpxlp);
if (csa->out_pb != NULL) remove(csa->out_pb);
if (csa->out_npb != NULL) remove(csa->out_npb);
if (csa->log_file != NULL) remove(csa->log_file);
/*--------------------------------------------------------------*/
/* open log file, if required */
if (csa->log_file != NULL)
{ if (lib_open_log(csa->log_file))
{ xprintf("Unable to create log file\n");
ret = EXIT_FAILURE;
goto done;
}
}
/*--------------------------------------------------------------*/
/* read problem data from the input file */
if (csa->in_file == NULL)
{ xprintf("No input problem file specified; try %s --help\n",
argv[0]);
ret = EXIT_FAILURE;
goto done;
}
if (csa->format == FMT_MPS_DECK)
{ ret = glp_read_mps(csa->prob, GLP_MPS_DECK, NULL,
csa->in_file);
if (ret != 0)
err1: { xprintf("MPS file processing error\n");
ret = EXIT_FAILURE;
goto done;
}
}
else if (csa->format == FMT_MPS_FILE)
{ ret = glp_read_mps(csa->prob, GLP_MPS_FILE, NULL,
csa->in_file);
if (ret != 0) goto err1;
}
else if (csa->format == FMT_CPLEX_LP)
{ ret = glp_read_lp(csa->prob, NULL, csa->in_file);
if (ret != 0)
{ xprintf("CPLEX LP file processing error\n");
ret = EXIT_FAILURE;
goto done;
}
}
else if (csa->format == FMT_MATHPROG)
{ int k;
/* allocate the translator workspace */
csa->tran = glp_mpl_alloc_wksp();
/* read model section and optional data section */
if (glp_mpl_read_model(csa->tran, csa->in_file, csa->ndf > 0))
err2: { xprintf("MathProg model processing error\n");
ret = EXIT_FAILURE;
goto done;
}
/* read optional data section(s), if necessary */
for (k = 1; k <= csa->ndf; k++)
{ if (glp_mpl_read_data(csa->tran, csa->in_data[k]))
goto err2;
}
/* generate the model */
if (glp_mpl_generate(csa->tran, csa->out_dpy)) goto err2;
/* build the problem instance from the model */
glp_mpl_build_prob(csa->tran, csa->prob);
}
else if (csa->format == FMT_MIN_COST)
{ csa->graph = glp_create_graph(sizeof(v_data), sizeof(a_data));
ret = glp_read_mincost(csa->graph, offsetof(v_data, rhs),
offsetof(a_data, low), offsetof(a_data, cap),
offsetof(a_data, cost), csa->in_file);
if (ret != 0)
{ xprintf("DIMACS file processing error\n");
ret = EXIT_FAILURE;
goto done;
}
glp_mincost_lp(csa->prob, csa->graph, GLP_ON,
offsetof(v_data, rhs), offsetof(a_data, low),
offsetof(a_data, cap), offsetof(a_data, cost));
glp_set_prob_name(csa->prob, csa->in_file);
}
else if (csa->format == FMT_MAX_FLOW)
{ int s, t;
csa->graph = glp_create_graph(sizeof(v_data), sizeof(a_data));
ret = glp_read_maxflow(csa->graph, &s, &t,
offsetof(a_data, cap), csa->in_file);
if (ret != 0)
{ xprintf("DIMACS file processing error\n");
ret = EXIT_FAILURE;
goto done;
}
glp_maxflow_lp(csa->prob, csa->graph, GLP_ON, s, t,
offsetof(a_data, cap));
glp_set_prob_name(csa->prob, csa->in_file);
}
else
xassert(csa != csa);
/*--------------------------------------------------------------*/
/* change problem name, if required */
if (csa->new_name != NULL)
glp_set_prob_name(csa->prob, csa->new_name);
/* change optimization direction, if required */
if (csa->dir != 0)
glp_set_obj_dir(csa->prob, csa->dir);
/* order rows and columns of the constraint matrix */
lpx_order_matrix(csa->prob);
/*--------------------------------------------------------------*/
/* write problem data in fixed MPS format, if required */
if (csa->out_mps != NULL)
{ ret = glp_write_mps(csa->prob, GLP_MPS_DECK, NULL,
csa->out_mps);
if (ret != 0)
{ xprintf("Unable to write problem in fixed MPS format\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write problem data in free MPS format, if required */
if (csa->out_freemps != NULL)
{ ret = glp_write_mps(csa->prob, GLP_MPS_FILE, NULL,
csa->out_freemps);
if (ret != 0)
{ xprintf("Unable to write problem in free MPS format\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write problem data in CPLEX LP format, if required */
if (csa->out_cpxlp != NULL)
{ ret = glp_write_lp(csa->prob, NULL, csa->out_cpxlp);
if (ret != 0)
{ xprintf("Unable to write problem in CPLEX LP format\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write problem data in OPB format, if required */
if (csa->out_pb != NULL)
{ ret = lpx_write_pb(csa->prob, csa->out_pb, 0, 0);
if (ret != 0)
{ xprintf("Unable to write problem in OPB format\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write problem data in normalized OPB format, if required */
if (csa->out_npb != NULL)
{ ret = lpx_write_pb(csa->prob, csa->out_npb, 1, 1);
if (ret != 0)
{ xprintf(
"Unable to write problem in normalized OPB format\n");
ret = EXIT_FAILURE;
goto done;
}
}
/*--------------------------------------------------------------*/
/* if only problem data check is required, skip computations */
if (csa->check)
{ ret = EXIT_SUCCESS;
goto done;
}
/*--------------------------------------------------------------*/
/* determine the solution type */
if (!csa->nomip &&
glp_get_num_int(csa->prob) + glp_get_num_bin(csa->prob) > 0)
{ if (csa->solution == SOL_INTERIOR)
{ xprintf("Interior-point method is not able to solve MIP pro"
"blem; use --simplex\n");
ret = EXIT_FAILURE;
goto done;
}
csa->solution = SOL_INTEGER;
}
/*--------------------------------------------------------------*/
/* if solution is provided, read it and skip computations */
if (csa->in_res != NULL)
{ if (csa->solution == SOL_BASIC)
ret = glp_read_sol(csa->prob, csa->in_res);
else if (csa->solution == SOL_INTERIOR)
ret = glp_read_ipt(csa->prob, csa->in_res);
else if (csa->solution == SOL_INTEGER)
ret = glp_read_mip(csa->prob, csa->in_res);
else
xassert(csa != csa);
if (ret != 0)
{ xprintf("Unable to read problem solution\n");
ret = EXIT_FAILURE;
goto done;
}
goto skip;
}
/*--------------------------------------------------------------*/
/* scale the problem data, if required */
if (csa->scale)
{ if (csa->solution == SOL_BASIC && !csa->smcp.presolve ||
csa->solution == SOL_INTERIOR ||
csa->solution == SOL_INTEGER && !csa->iocp.presolve)
glp_scale_prob(csa->prob, GLP_SF_AUTO);
}
/* construct starting LP basis */
if (csa->solution == SOL_BASIC && !csa->smcp.presolve ||
csa->solution == SOL_INTEGER && !csa->iocp.presolve)
{ if (csa->crash == USE_STD_BASIS)
glp_std_basis(csa->prob);
else if (csa->crash == USE_ADV_BASIS)
glp_adv_basis(csa->prob, 0);
else if (csa->crash == USE_CPX_BASIS)
glp_cpx_basis(csa->prob);
else
xassert(csa != csa);
}
/*--------------------------------------------------------------*/
/* solve the problem */
start = xtime();
if (csa->solution == SOL_BASIC)
{ if (!csa->exact)
{ glp_set_bfcp(csa->prob, &csa->bfcp);
glp_simplex(csa->prob, &csa->smcp);
if (csa->xcheck)
{ if (csa->smcp.presolve &&
glp_get_status(csa->prob) != GLP_OPT)
xprintf("If you need to check final basis for non-opt"
"imal solution, use --nopresol\n");
else
glp_exact(csa->prob, &csa->smcp);
}
if (csa->out_sol != NULL || csa->out_res != NULL)
{ if (csa->smcp.presolve &&
glp_get_status(csa->prob) != GLP_OPT)
xprintf("If you need actual output for non-optimal solut"
"ion, use --nopresol\n");
}
}
else
glp_exact(csa->prob, &csa->smcp);
}
else if (csa->solution == SOL_INTERIOR)
glp_interior(csa->prob, NULL);
else if (csa->solution == SOL_INTEGER)
{ if (!csa->iocp.presolve)
{ glp_set_bfcp(csa->prob, &csa->bfcp);
glp_simplex(csa->prob, &csa->smcp);
}
glp_intopt(csa->prob, &csa->iocp);
}
else
xassert(csa != csa);
/*--------------------------------------------------------------*/
/* display statistics */
xprintf("Time used: %.1f secs\n", xdifftime(xtime(), start));
{ xlong_t tpeak;
char buf[50];
lib_mem_usage(NULL, NULL, NULL, &tpeak);
xprintf("Memory used: %.1f Mb (%s bytes)\n",
xltod(tpeak) / 1048576.0, xltoa(tpeak, buf));
}
/*--------------------------------------------------------------*/
skip: /* postsolve the model, if necessary */
if (csa->tran != NULL)
{ if (csa->solution == SOL_BASIC)
ret = glp_mpl_postsolve(csa->tran, csa->prob, GLP_SOL);
else if (csa->solution == SOL_INTERIOR)
ret = glp_mpl_postsolve(csa->tran, csa->prob, GLP_IPT);
else if (csa->solution == SOL_INTEGER)
ret = glp_mpl_postsolve(csa->tran, csa->prob, GLP_MIP);
else
xassert(csa != csa);
if (ret != 0)
{ xprintf("Model postsolving error\n");
ret = EXIT_FAILURE;
goto done;
}
}
/*--------------------------------------------------------------*/
/* write problem solution in printable format, if required */
if (csa->out_sol != NULL)
{ if (csa->solution == SOL_BASIC)
ret = lpx_print_sol(csa->prob, csa->out_sol);
else if (csa->solution == SOL_INTERIOR)
ret = lpx_print_ips(csa->prob, csa->out_sol);
else if (csa->solution == SOL_INTEGER)
ret = lpx_print_mip(csa->prob, csa->out_sol);
else
xassert(csa != csa);
if (ret != 0)
{ xprintf("Unable to write problem solution\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write problem solution in printable format, if required */
if (csa->out_res != NULL)
{ if (csa->solution == SOL_BASIC)
ret = glp_write_sol(csa->prob, csa->out_res);
else if (csa->solution == SOL_INTERIOR)
ret = glp_write_ipt(csa->prob, csa->out_res);
else if (csa->solution == SOL_INTEGER)
ret = glp_write_mip(csa->prob, csa->out_res);
else
xassert(csa != csa);
if (ret != 0)
{ xprintf("Unable to write problem solution\n");
ret = EXIT_FAILURE;
goto done;
}
}
/* write sensitivity bounds information, if required */
if (csa->out_bnds != NULL)
{ if (csa->solution == SOL_BASIC)
{ ret = lpx_print_sens_bnds(csa->prob, csa->out_bnds);
if (ret != 0)
{ xprintf("Unable to write sensitivity bounds information "
"\n");
ret = EXIT_FAILURE;
goto done;
}
}
else
xprintf("Cannot write sensitivity bounds information for in"
"terior-point or MIP solution\n");
}
/*--------------------------------------------------------------*/
/* all seems to be ok */
ret = EXIT_SUCCESS;
/*--------------------------------------------------------------*/
done: /* delete the LP/MIP problem object */
if (csa->prob != NULL)
glp_delete_prob(csa->prob);
/* free the translator workspace, if necessary */
if (csa->tran != NULL)
glp_mpl_free_wksp(csa->tran);
/* delete the network problem object, if necessary */
if (csa->graph != NULL)
glp_delete_graph(csa->graph);
xassert(gmp_pool_count() == 0);
gmp_free_mem();
/* close log file, if necessary */
if (csa->log_file != NULL) lib_close_log();
/* check that no memory blocks are still allocated */
{ int count;
xlong_t total;
lib_mem_usage(&count, NULL, &total, NULL);
if (count != 0)
xerror("Error: %d memory block(s) were lost\n", count);
xassert(count == 0);
xassert(total.lo == 0 && total.hi == 0);
}
/* free the library environment */
lib_free_env();
/* return to the control program */
return ret;
}
