void NUMlinprog_run (NUMlinprog me) {
try {
glp_smcp parm;
glp_init_smcp (& parm);
parm. msg_lev = GLP_MSG_OFF;
my status = glp_simplex (my linearProgram, & parm);
switch (my status) {
case GLP_EBADB: Melder_throw (U"Unable to start the search, because the initial basis is invalid.");
case GLP_ESING: Melder_throw (U"Unable to start the search, because the basis matrix is singular.");
case GLP_ECOND: Melder_throw (U"Unable to start the search, because the basis matrix is ill-conditioned.");
case GLP_EBOUND: Melder_throw (U"Unable to start the search, because some variables have incorrect bounds.");
case GLP_EFAIL: Melder_throw (U"Search prematurely terminated due to solver failure.");
case GLP_EOBJLL: Melder_throw (U"Search prematurely terminated: lower limit reached.");
case GLP_EOBJUL: Melder_throw (U"Search prematurely terminated: upper limit reached.");
case GLP_EITLIM: Melder_throw (U"Search prematurely terminated: iteration limit exceeded.");
case GLP_ETMLIM: Melder_throw (U"Search prematurely terminated: time limit exceeded.");
case GLP_ENOPFS: Melder_throw (U"The problem has no primal feasible solution.");
case GLP_ENODFS: Melder_throw (U"The problem has no dual feasible solution.");
default: break;
}
my status = glp_get_status (my linearProgram);
switch (my status) {
case GLP_INFEAS: Melder_throw (U"Solution is infeasible.");
case GLP_NOFEAS: Melder_throw (U"Problem has no feasible solution.");
case GLP_UNBND: Melder_throw (U"Problem has unbounded solution.");
case GLP_UNDEF: Melder_throw (U"Solution is undefined.");
default: break;
}
if (my status == GLP_FEAS) {
Melder_warning (U"Linear programming solution is feasible but not optimal.");
}
} catch (MelderError) {
Melder_throw (U"Linear programming: not run.");
}
}
/* Initialisation of smcp object */
static int
smcp_init(pysmcp *self, PyObject *args, PyObject *kwds)
{
/*static char *kwlist[] = {"number", NULL};*/
static char *kwlist[] = { "msg_lev", "meth", "pricing", "r_test", "tol_bnd", "tol_dj", "tol_piv", "obj_ll", "obj_ul", "it_lim", "tm_lim", "out_frq", "out_dly", "presolve" };
glp_init_smcp(&self->obj);
if (! PyArg_ParseTupleAndKeywords(args, kwds, "|iiiidddddiiiii", kwlist,
&self->obj.msg_lev,
&self->obj.meth,
&self->obj.pricing,
&self->obj.r_test,
&self->obj.tol_bnd,
&self->obj.tol_dj,
&self->obj.tol_piv,
&self->obj.obj_ll,
&self->obj.obj_ul,
&self->obj.it_lim,
&self->obj.tm_lim,
&self->obj.out_frq,
&self->obj.out_dly,
&self->obj.presolve))
return -1;
return 0;
}
void printResult(glp_prob * Prob, FILE * out) {
int i;
char buf[AUXSIZE];
glp_smcp * param = malloc(sizeof(glp_smcp));
glp_init_smcp(param);
param->msg_lev = GLP_MSG_ERR;
param->presolve = GLP_ON;
int solution[MAXSIZE];
for( i = 0 ; i < n ; ++i )
solution[planes[i].pos] = i;
mapSolution(Prob,solution);
glp_simplex(Prob,param);
if( simpleOutput ) {
time(&final);
fprintf(out,"%i & %i \\\\ \n",(int)glp_get_obj_val(Prob),(int)difftime(final,initial));
return;
}
fprintf(out,"Best found solution's value: %lf\n\n",glp_get_obj_val(Prob));
double time;
for( i = 0 ; i < n ; ++i ) {
sprintf(buf,"x%i",solution[i]);
time = glp_get_col_prim(Prob, glp_find_col(Prob, buf));
fprintf(out,"The %i-th airplane to arrive is airplane %i, at the time %lf\n",
i+1,solution[i]+1,time);
}
}
int c_glp_solve_simplex(glp_prob *lp, int msg_lev, int tm_lim, int presolve){
glp_smcp smcp;
glp_init_smcp (&smcp);
smcp.msg_lev = msg_lev;
smcp.tm_lim = tm_lim;
smcp.presolve = presolve ? GLP_ON : GLP_OFF;
glp_adv_basis(lp, 0);
return glp_simplex(lp, &smcp);
}
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_);
}
int main(void)
{ glp_prob *P;
glp_smcp parm;
P = glp_create_prob();
glp_read_mps(P, GLP_MPS_DECK, NULL, "25fv47.mps");
glp_init_smcp(&parm);
parm.meth = GLP_DUAL;
glp_simplex(P, &parm);
glp_print_sol(P, "25fv47.txt");
glp_delete_prob(P);
return 0;
}
/* init glp parameters. These should be changeable some way */
static void init_glp_parameters(void){
glp_init_smcp(&parm);
parm.meth = GLP_DUAL; // PRIMAL,DUAL,DUALP
parm.msg_lev = GLP_MSG_ERR; // ORR,ON,ALL,ERR
parm.pricing = GLP_PT_PSE; // PSE,STD
parm.r_test = GLP_RT_HAR; // HAR,STD
parm.it_lim = 80000; // iteration limit
parm.tm_lim = 10000; // time limit 10 seconds
parm.out_frq = 80000; // output frequency
parm.presolve = GLP_ON; // presolve, helps on numerical instability
glp_term_out(GLP_OFF); // no terminal output
}
static void fill_smcp(LPX *lp, glp_smcp *parm)
{ glp_init_smcp(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_DUAL))
{ case 0: parm->meth = GLP_PRIMAL; break;
case 1: parm->meth = GLP_DUAL; break;
default: xassert(lp != lp);
}
switch (lpx_get_int_parm(lp, LPX_K_PRICE))
{ case 0: parm->pricing = GLP_PT_STD; break;
case 1: parm->pricing = GLP_PT_PSE; break;
default: xassert(lp != lp);
}
if (lpx_get_real_parm(lp, LPX_K_RELAX) == 0.0)
parm->r_test = GLP_RT_STD;
else
parm->r_test = GLP_RT_HAR;
parm->tol_bnd = lpx_get_real_parm(lp, LPX_K_TOLBND);
parm->tol_dj = lpx_get_real_parm(lp, LPX_K_TOLDJ);
parm->tol_piv = lpx_get_real_parm(lp, LPX_K_TOLPIV);
parm->obj_ll = lpx_get_real_parm(lp, LPX_K_OBJLL);
parm->obj_ul = lpx_get_real_parm(lp, LPX_K_OBJUL);
if (lpx_get_int_parm(lp, LPX_K_ITLIM) < 0)
parm->it_lim = INT_MAX;
else
parm->it_lim = lpx_get_int_parm(lp, LPX_K_ITLIM);
if (lpx_get_real_parm(lp, LPX_K_TMLIM) < 0.0)
parm->tm_lim = INT_MAX;
else
parm->tm_lim =
(int)(1000.0 * lpx_get_real_parm(lp, LPX_K_TMLIM));
parm->out_frq = lpx_get_int_parm(lp, LPX_K_OUTFRQ);
parm->out_dly =
(int)(1000.0 * lpx_get_real_parm(lp, LPX_K_OUTDLY));
switch (lpx_get_int_parm(lp, LPX_K_PRESOL))
{ case 0: parm->presolve = GLP_OFF; break;
case 1: parm->presolve = GLP_ON; break;
default: xassert(lp != lp);
}
return;
}
/* 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 glp_simplex(glp_prob *P, const glp_smcp *parm)
{ /* solve LP problem with the simplex method */
glp_smcp _parm;
int i, j, ret;
/* check problem object */
if (P == NULL || P->magic != GLP_PROB_MAGIC)
xerror("glp_simplex: P = %p; invalid problem object\n", P);
if (P->tree != NULL && P->tree->reason != 0)
xerror("glp_simplex: operation not allowed\n");
/* check control parameters */
if (parm == NULL)
parm = &_parm, glp_init_smcp((glp_smcp *)parm);
if (!(parm->msg_lev == GLP_MSG_OFF ||
parm->msg_lev == GLP_MSG_ERR ||
parm->msg_lev == GLP_MSG_ON ||
parm->msg_lev == GLP_MSG_ALL ||
parm->msg_lev == GLP_MSG_DBG))
xerror("glp_simplex: msg_lev = %d; invalid parameter\n",
parm->msg_lev);
if (!(parm->meth == GLP_PRIMAL ||
parm->meth == GLP_DUALP ||
parm->meth == GLP_DUAL))
xerror("glp_simplex: meth = %d; invalid parameter\n",
parm->meth);
if (!(parm->pricing == GLP_PT_STD ||
parm->pricing == GLP_PT_PSE))
xerror("glp_simplex: pricing = %d; invalid parameter\n",
parm->pricing);
if (!(parm->r_test == GLP_RT_STD ||
parm->r_test == GLP_RT_HAR))
xerror("glp_simplex: r_test = %d; invalid parameter\n",
parm->r_test);
if (!(0.0 < parm->tol_bnd && parm->tol_bnd < 1.0))
xerror("glp_simplex: tol_bnd = %g; invalid parameter\n",
parm->tol_bnd);
if (!(0.0 < parm->tol_dj && parm->tol_dj < 1.0))
xerror("glp_simplex: tol_dj = %g; invalid parameter\n",
parm->tol_dj);
if (!(0.0 < parm->tol_piv && parm->tol_piv < 1.0))
xerror("glp_simplex: tol_piv = %g; invalid parameter\n",
parm->tol_piv);
if (parm->it_lim < 0)
xerror("glp_simplex: it_lim = %d; invalid parameter\n",
parm->it_lim);
if (parm->tm_lim < 0)
xerror("glp_simplex: tm_lim = %d; invalid parameter\n",
parm->tm_lim);
if (parm->out_frq < 1)
xerror("glp_simplex: out_frq = %d; invalid parameter\n",
parm->out_frq);
if (parm->out_dly < 0)
xerror("glp_simplex: out_dly = %d; invalid parameter\n",
parm->out_dly);
if (!(parm->presolve == GLP_ON || parm->presolve == GLP_OFF))
xerror("glp_simplex: presolve = %d; invalid parameter\n",
parm->presolve);
/* basic solution is currently undefined */
P->pbs_stat = P->dbs_stat = GLP_UNDEF;
P->obj_val = 0.0;
P->some = 0;
/* check bounds of double-bounded variables */
for (i = 1; i <= P->m; i++)
{ GLPROW *row = P->row[i];
if (row->type == GLP_DB && row->lb >= row->ub)
{ if (parm->msg_lev >= GLP_MSG_ERR)
xprintf("glp_simplex: row %d: lb = %g, ub = %g; incorrec"
"t bounds\n", i, row->lb, row->ub);
ret = GLP_EBOUND;
goto done;
}
}
for (j = 1; j <= P->n; j++)
{ GLPCOL *col = P->col[j];
if (col->type == GLP_DB && col->lb >= col->ub)
{ if (parm->msg_lev >= GLP_MSG_ERR)
xprintf("glp_simplex: column %d: lb = %g, ub = %g; incor"
"rect bounds\n", j, col->lb, col->ub);
ret = GLP_EBOUND;
goto done;
}
}
/* solve LP problem */
if (parm->msg_lev >= GLP_MSG_ALL)
{ xprintf("GLPK Simplex Optimizer, v%s\n", glp_version());
xprintf("%d row%s, %d column%s, %d non-zero%s\n",
P->m, P->m == 1 ? "" : "s", P->n, P->n == 1 ? "" : "s",
P->nnz, P->nnz == 1 ? "" : "s");
}
if (P->nnz == 0)
trivial_lp(P, parm), ret = 0;
else if (!parm->presolve)
ret = solve_lp(P, parm);
else
ret = preprocess_and_solve_lp(P, parm);
done: /* return to the application program */
return ret;
}
static double eval_degrad(glp_prob *P, int j, double bnd)
{ /* compute degradation of the objective on fixing x[j] at given
value with a limited number of dual simplex iterations */
/* this routine fixes column x[j] at specified value bnd,
solves resulting LP, and returns a lower bound to degradation
of the objective, degrad >= 0 */
glp_prob *lp;
glp_smcp parm;
int ret;
double degrad;
/* the current basis must be optimal */
xassert(glp_get_status(P) == GLP_OPT);
/* create a copy of P */
lp = glp_create_prob();
glp_copy_prob(lp, P, 0);
/* fix column x[j] at specified value */
glp_set_col_bnds(lp, j, GLP_FX, bnd, bnd);
/* try to solve resulting LP */
glp_init_smcp(&parm);
parm.msg_lev = GLP_MSG_OFF;
parm.meth = GLP_DUAL;
parm.it_lim = 30;
parm.out_dly = 1000;
parm.meth = GLP_DUAL;
ret = glp_simplex(lp, &parm);
if (ret == 0 || ret == GLP_EITLIM)
{ if (glp_get_prim_stat(lp) == GLP_NOFEAS)
{ /* resulting LP has no primal feasible solution */
degrad = DBL_MAX;
}
else if (glp_get_dual_stat(lp) == GLP_FEAS)
{ /* resulting basis is optimal or at least dual feasible,
so we have the correct lower bound to degradation */
if (P->dir == GLP_MIN)
degrad = lp->obj_val - P->obj_val;
else if (P->dir == GLP_MAX)
degrad = P->obj_val - lp->obj_val;
else
xassert(P != P);
/* degradation cannot be negative by definition */
/* note that the lower bound to degradation may be close
to zero even if its exact value is zero due to round-off
errors on computing the objective value */
if (degrad < 1e-6 * (1.0 + 0.001 * fabs(P->obj_val)))
degrad = 0.0;
}
else
{ /* the final basis reported by the simplex solver is dual
infeasible, so we cannot determine a non-trivial lower
bound to degradation */
degrad = 0.0;
}
}
else
{ /* the simplex solver failed */
degrad = 0.0;
}
/* delete the copy of P */
glp_delete_prob(lp);
return degrad;
}
/*
R is the random contraint data in row major memory layout
ridx is an N array of integers
soln is an array of length (n+1) soln[n] is t (objective value)
active_constr is an N-length 0-1 array
*/
void solve_lp(int N, int n, double* R, int* ridx, double* soln, int* active_constr)
{
double tol = 1.0e-14;
int size = (N+1)*(n+1) + 1; // We add one because GLPK indexes arrays
// starting at 1 instead of 0.
glp_prob *lp;
int* ia = malloc(size * sizeof(int));
int* ja = malloc(size * sizeof(int));
double* ar = malloc(size * sizeof(double));
int i, j;
lp = glp_create_prob();
glp_set_prob_name(lp, "portfolio");
glp_set_obj_dir(lp, GLP_MAX);
glp_add_rows(lp, N+1);
// Sampled constraints are ">= 0"
for (i = 1; i <= N; i++) {
glp_set_row_bnds(lp, i, GLP_LO, 0.0, 0.0);
}
// Sum = 1 constraint
glp_set_row_name(lp, N+1, "sum");
glp_set_row_bnds(lp, N+1, GLP_FX, 1.0, 1.0);
glp_add_cols(lp, n+1);
// Nonnegative variables y
for (i = 1; i <= n; i++) {
glp_set_col_bnds(lp, i, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, i, 0.0);
}
// Free variable t
glp_set_col_name(lp, n+1, "t");
glp_set_col_bnds(lp, n+1, GLP_FR, 0.0, 0.0);
glp_set_obj_coef(lp, n+1, 1.0);
// for (i = 0; i < N*(n-1); i++) {
// printf("%d: %g\n", i, R[i]);
// }
int idx = 1;
// Sampled constraints
for (i = 1; i <= N; i++) {
// Uncertain assets
for (j = 1; j < n; j++) {
ia[idx] = i;
ja[idx] = j;
ar[idx] = R[ ridx[(i-1)] * (n-1) + (j-1) ];
idx += 1;
}
// Fixed return asset
ia[idx] = i;
ja[idx] = n;
ar[idx] = 1.05;
idx += 1;
// t
ia[idx] = i;
ja[idx] = n+1;
ar[idx] = -1.0;
idx += 1;
}
// Sum = 1 constraint
for (i = 1; i <= n; i++) {
ia[idx] = N+1;
ja[idx] = i;
ar[idx] = 1.0;
idx += 1;
}
// t
ia[idx] = N+1;
ja[idx] = n+1;
ar[idx] = 0.0;
idx += 1;
// for (i = 1; i < size; i++) {
// printf("%d %d %g\n", ia[i], ja[i], ar[i]);
// }
glp_load_matrix(lp, size-1, ia, ja, ar);
// glp_scale_prob(lp, GLP_SF_AUTO);
glp_smcp param;
glp_init_smcp(¶m);
param.meth = GLP_PRIMAL;
//glp_std_basis(lp);
glp_simplex(lp, ¶m);
double z = glp_get_obj_val(lp);
// printf("z = %g\n", z);
if (soln) {
for (i = 0; i < n; i++) {
double y = glp_get_col_prim(lp, i+1);
soln[i] = y;
// printf("y%d = %g\n", i, y);
}
double t = glp_get_col_prim(lp, n+1);
soln[n] = t;
// printf("t = %g\n", glp_get_col_prim(lp, n+1));
}
for (i = 1; i <= N; i++) {
double slack = glp_get_row_prim(lp, i);
active_constr[i-1] = fabs(slack) < tol ? 1 : 0;
// printf("constr%d %d\n", i, active_constr[i-1]);
}
glp_delete_prob(lp);
// glp_free_env();
free(ia);
free(ja);
free(ar);
}
static PyObject *simplex(PyObject *self, PyObject *args,
PyObject *kwrds)
{
matrix *c, *h, *b=NULL, *x=NULL, *z=NULL, *y=NULL;
PyObject *G, *A=NULL, *t=NULL;
glp_prob *lp;
glp_smcp *options = NULL;
pysmcp *smcpParm = NULL;
int m, n, p, i, j, k, nnz, nnzmax, *rn=NULL, *cn=NULL;
double *a=NULL, val;
char *kwlist[] = {"c", "G", "h", "A", "b","options", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwrds, "OOO|OOO!", kwlist, &c,
&G, &h, &A, &b,&smcp_t,&smcpParm)) return NULL;
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(!smcpParm)
{
smcpParm = (pysmcp*)malloc(sizeof(*smcpParm));
glp_init_smcp(&(smcpParm->obj));
}
if(smcpParm)
{
Py_INCREF(smcpParm);
options = &smcpParm->obj;
options->presolve = 1;
}
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(A ? 4 : 3))){
glp_delete_prob(lp);
return PyErr_NoMemory();
}
switch (glp_simplex(lp,options)){
case 0:
x = (matrix *) Matrix_New(n,1,DOUBLE);
z = (matrix *) Matrix_New(m,1,DOUBLE);
if (A) y = (matrix *) Matrix_New(p,1,DOUBLE);
if (!x || !z || (A && !y)){
Py_XDECREF(x);
Py_XDECREF(z);
Py_XDECREF(y);
Py_XDECREF(t);
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
return PyErr_NoMemory();
}
set_output_string(t,"optimal");
for (i=0; i<n; i++)
MAT_BUFD(x)[i] = glp_get_col_prim(lp, i+1);
PyTuple_SET_ITEM(t, 1, (PyObject *) x);
for (i=0; i<m; i++)
MAT_BUFD(z)[i] = -glp_get_row_dual(lp, i+1);
PyTuple_SET_ITEM(t, 2, (PyObject *) z);
if (A){
for (i=0; i<p; i++)
MAT_BUFD(y)[i] = -glp_get_row_dual(lp, m+i+1);
PyTuple_SET_ITEM(t, 3, (PyObject *) y);
}
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
return (PyObject *) t;
case GLP_EBADB:
set_output_string(t,"incorrect initial basis");
break;
case GLP_ESING:
set_output_string(t,"singular initial basis matrix");
break;
case GLP_ECOND:
set_output_string(t,"ill-conditioned initial basis matrix");
break;
case GLP_EBOUND:
set_output_string(t,"incorrect bounds");
break;
case GLP_EFAIL:
set_output_string(t,"solver failure");
break;
case GLP_EOBJLL:
set_output_string(t,"objective function reached lower limit");
break;
case GLP_EOBJUL:
set_output_string(t,"objective function reached upper limit");
break;
case GLP_EITLIM:
set_output_string(t,"iteration limit exceeded");
break;
case GLP_ETMLIM:
set_output_string(t,"time limit exceeded");
break;
case GLP_ENOPFS:
set_output_string(t,"primal infeasible");
break;
case GLP_ENODFS:
set_output_string(t,"dual infeasible");
break;
default:
set_output_string(t,"unknown");
break;
}
Py_XDECREF(smcpParm);
glp_delete_prob(lp);
PyTuple_SET_ITEM(t, 1, Py_BuildValue(""));
PyTuple_SET_ITEM(t, 2, Py_BuildValue(""));
if (A) PyTuple_SET_ITEM(t, 3, Py_BuildValue(""));
return (PyObject *) t;
}
int max_flow_lp(int nn, int ne, const int beg[/*1+ne*/],
const int end[/*1+ne*/], const int cap[/*1+ne*/], int s, int t,
int x[/*1+ne*/])
{ glp_prob *lp;
glp_smcp smcp;
int i, k, nz, flow, *rn, *cn;
double temp, *aa;
/* create LP problem instance */
lp = glp_create_prob();
/* create LP rows; i-th row is the conservation condition of the
* flow at i-th node, i = 1, ..., nn */
glp_add_rows(lp, nn);
for (i = 1; i <= nn; i++)
glp_set_row_bnds(lp, i, GLP_FX, 0.0, 0.0);
/* create LP columns; k-th column is the elementary flow thru
* k-th edge, k = 1, ..., ne; the last column with the number
* ne+1 is the total flow through the network, which goes along
* a dummy feedback edge from the sink to the source */
glp_add_cols(lp, ne+1);
for (k = 1; k <= ne; k++)
{ xassert(cap[k] > 0);
glp_set_col_bnds(lp, k, GLP_DB, -cap[k], +cap[k]);
}
glp_set_col_bnds(lp, ne+1, GLP_FR, 0.0, 0.0);
/* build the constraint matrix; structurally this matrix is the
* incidence matrix of the network, so each its column (including
* the last column for the dummy edge) has exactly two non-zero
* entries */
rn = xalloc(1+2*(ne+1), sizeof(int));
cn = xalloc(1+2*(ne+1), sizeof(int));
aa = xalloc(1+2*(ne+1), sizeof(double));
nz = 0;
for (k = 1; k <= ne; k++)
{ /* x[k] > 0 means the elementary flow thru k-th edge goes from
* node beg[k] to node end[k] */
nz++, rn[nz] = beg[k], cn[nz] = k, aa[nz] = -1.0;
nz++, rn[nz] = end[k], cn[nz] = k, aa[nz] = +1.0;
}
/* total flow thru the network goes from the sink to the source
* along the dummy feedback edge */
nz++, rn[nz] = t, cn[nz] = ne+1, aa[nz] = -1.0;
nz++, rn[nz] = s, cn[nz] = ne+1, aa[nz] = +1.0;
/* check the number of non-zero entries */
xassert(nz == 2*(ne+1));
/* load the constraint matrix into the LP problem object */
glp_load_matrix(lp, nz, rn, cn, aa);
xfree(rn);
xfree(cn);
xfree(aa);
/* objective function is the total flow through the network to
* be maximized */
glp_set_obj_dir(lp, GLP_MAX);
glp_set_obj_coef(lp, ne + 1, 1.0);
/* solve LP instance with the (primal) simplex method */
glp_term_out(0);
glp_adv_basis(lp, 0);
glp_term_out(1);
glp_init_smcp(&smcp);
smcp.msg_lev = GLP_MSG_ON;
smcp.out_dly = 5000;
xassert(glp_simplex(lp, &smcp) == 0);
xassert(glp_get_status(lp) == GLP_OPT);
/* obtain optimal elementary flows thru edges of the network */
/* (note that the constraint matrix is unimodular and the data
* are integral, so all elementary flows in basic solution should
* also be integral) */
for (k = 1; k <= ne; k++)
{ temp = glp_get_col_prim(lp, k);
x[k] = (int)floor(temp + .5);
xassert(fabs(x[k] - temp) <= 1e-6);
}
/* obtain the maximum flow thru the original network which is the
* flow thru the dummy feedback edge */
temp = glp_get_col_prim(lp, ne+1);
flow = (int)floor(temp + .5);
xassert(fabs(flow - temp) <= 1e-6);
/* delete LP problem instance */
glp_delete_prob(lp);
/* return to the calling program */
return flow;
}
/**
* Init the MLP problem solving component
*
* @param cfg the GNUNET_CONFIGURATION_Handle handle
* @param stats the GNUNET_STATISTICS handle
* @param max_duration maximum numbers of iterations for the LP/MLP Solver
* @param max_iterations maximum time limit for the LP/MLP Solver
* @return struct GAS_MLP_Handle * on success, NULL on fail
*/
struct GAS_MLP_Handle *
GAS_mlp_init (const struct GNUNET_CONFIGURATION_Handle *cfg,
const struct GNUNET_STATISTICS_Handle *stats,
struct GNUNET_TIME_Relative max_duration,
unsigned int max_iterations)
{
struct GAS_MLP_Handle * mlp = GNUNET_malloc (sizeof (struct GAS_MLP_Handle));
double D;
double R;
double U;
unsigned long long tmp;
unsigned int b_min;
unsigned int n_min;
struct GNUNET_TIME_Relative i_exec;
int c;
char * quota_out_str;
char * quota_in_str;
/* Init GLPK environment */
int res = glp_init_env();
switch (res) {
case 0:
GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "GLPK: `%s'\n",
"initialization successful");
break;
case 1:
GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "GLPK: `%s'\n",
"environment is already initialized");
break;
case 2:
GNUNET_log (GNUNET_ERROR_TYPE_ERROR, "Could not init GLPK: `%s'\n",
"initialization failed (insufficient memory)");
GNUNET_free(mlp);
return NULL;
break;
case 3:
GNUNET_log (GNUNET_ERROR_TYPE_ERROR, "Could not init GLPK: `%s'\n",
"initialization failed (unsupported programming model)");
GNUNET_free(mlp);
return NULL;
break;
default:
break;
}
/* Create initial MLP problem */
mlp->prob = glp_create_prob();
GNUNET_assert (mlp->prob != NULL);
mlp->BIG_M = (double) BIG_M_VALUE;
/* Get diversity coefficient from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"COEFFICIENT_D",
&tmp))
D = (double) tmp / 100;
else
D = 1.0;
/* Get proportionality coefficient from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"COEFFICIENT_R",
&tmp))
R = (double) tmp / 100;
else
R = 1.0;
/* Get utilization coefficient from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"COEFFICIENT_U",
&tmp))
U = (double) tmp / 100;
else
U = 1.0;
/* Get quality metric coefficients from configuration */
int i_delay = -1;
int i_distance = -1;
int q[GNUNET_ATS_QualityPropertiesCount] = GNUNET_ATS_QualityProperties;
for (c = 0; c < GNUNET_ATS_QualityPropertiesCount; c++)
{
/* initialize quality coefficients with default value 1.0 */
mlp->co_Q[c] = 1.0;
mlp->q[c] = q[c];
if (q[c] == GNUNET_ATS_QUALITY_NET_DELAY)
i_delay = c;
if (q[c] == GNUNET_ATS_QUALITY_NET_DISTANCE)
i_distance = c;
}
if ((i_delay != -1) && (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"COEFFICIENT_QUALITY_DELAY",
&tmp)))
mlp->co_Q[i_delay] = (double) tmp / 100;
else
mlp->co_Q[i_delay] = 1.0;
if ((i_distance != -1) && (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"COEFFICIENT_QUALITY_DISTANCE",
&tmp)))
mlp->co_Q[i_distance] = (double) tmp / 100;
else
mlp->co_Q[i_distance] = 1.0;
/* Get minimum bandwidth per used address from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"MIN_BANDWIDTH",
&tmp))
b_min = tmp;
else
{
b_min = ntohl (GNUNET_CONSTANTS_DEFAULT_BW_IN_OUT.value__);
}
/* Get minimum number of connections from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_size (cfg, "ats",
"MIN_CONNECTIONS",
&tmp))
n_min = tmp;
else
n_min = 4;
/* Init network quotas */
int quotas[GNUNET_ATS_NetworkTypeCount] = GNUNET_ATS_NetworkType;
for (c = 0; c < GNUNET_ATS_NetworkTypeCount; c++)
{
mlp->quota_index[c] = quotas[c];
static char * entry_in = NULL;
static char * entry_out = NULL;
unsigned long long quota_in = 0;
unsigned long long quota_out = 0;
switch (quotas[c]) {
case GNUNET_ATS_NET_UNSPECIFIED:
entry_out = "UNSPECIFIED_QUOTA_OUT";
entry_in = "UNSPECIFIED_QUOTA_IN";
break;
case GNUNET_ATS_NET_LOOPBACK:
entry_out = "LOOPBACK_QUOTA_OUT";
entry_in = "LOOPBACK_QUOTA_IN";
break;
case GNUNET_ATS_NET_LAN:
entry_out = "LAN_QUOTA_OUT";
entry_in = "LAN_QUOTA_IN";
break;
case GNUNET_ATS_NET_WAN:
entry_out = "WAN_QUOTA_OUT";
entry_in = "WAN_QUOTA_IN";
break;
case GNUNET_ATS_NET_WLAN:
entry_out = "WLAN_QUOTA_OUT";
entry_in = "WLAN_QUOTA_IN";
break;
default:
break;
}
if ((entry_in == NULL) || (entry_out == NULL))
continue;
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_string(cfg, "ats", entry_out, "a_out_str))
{
if (0 == strcmp(quota_out_str, BIG_M_STRING) ||
(GNUNET_SYSERR == GNUNET_STRINGS_fancy_size_to_bytes (quota_out_str, "a_out)))
quota_out = mlp->BIG_M;
GNUNET_free (quota_out_str);
quota_out_str = NULL;
}
else if (GNUNET_ATS_NET_UNSPECIFIED == quotas[c])
{
quota_out = mlp->BIG_M;
}
else
{
quota_out = mlp->BIG_M;
}
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_string(cfg, "ats", entry_in, "a_in_str))
{
if (0 == strcmp(quota_in_str, BIG_M_STRING) ||
(GNUNET_SYSERR == GNUNET_STRINGS_fancy_size_to_bytes (quota_in_str, "a_in)))
quota_in = mlp->BIG_M;
GNUNET_free (quota_in_str);
quota_in_str = NULL;
}
else if (GNUNET_ATS_NET_UNSPECIFIED == quotas[c])
{
quota_in = mlp->BIG_M;
}
else
{
quota_in = mlp->BIG_M;
}
/* Check if defined quota could make problem unsolvable */
if (((n_min * b_min) > quota_out) && (GNUNET_ATS_NET_UNSPECIFIED != quotas[c]))
{
GNUNET_log (GNUNET_ERROR_TYPE_ERROR, "Inconsistent quota configuration value `%s': "
"outbound quota (%u Bps) too small for combination of minimum connections and minimum bandwidth per peer (%u * %u Bps = %u)\n", entry_out, quota_out, n_min, b_min, n_min * b_min);
GAS_mlp_done(mlp);
mlp = NULL;
return NULL;
}
GNUNET_log (GNUNET_ERROR_TYPE_DEBUG, "Found `%s' quota %llu and `%s' quota %llu\n",
entry_out, quota_out, entry_in, quota_in);
GNUNET_STATISTICS_update ((struct GNUNET_STATISTICS_Handle *) stats, entry_out, quota_out, GNUNET_NO);
GNUNET_STATISTICS_update ((struct GNUNET_STATISTICS_Handle *) stats, entry_in, quota_in, GNUNET_NO);
mlp->quota_out[c] = quota_out;
mlp->quota_in[c] = quota_in;
}
/* Get minimum number of connections from configuration */
if (GNUNET_OK == GNUNET_CONFIGURATION_get_value_time (cfg, "ats",
"ATS_EXEC_INTERVAL",
&i_exec))
mlp->exec_interval = i_exec;
else
mlp->exec_interval = GNUNET_TIME_relative_multiply(GNUNET_TIME_UNIT_SECONDS, 30);
mlp->stats = (struct GNUNET_STATISTICS_Handle *) stats;
mlp->max_iterations = max_iterations;
mlp->max_exec_duration = max_duration;
mlp->auto_solve = GNUNET_YES;
/* Redirect GLPK output to GNUnet logging */
glp_error_hook((void *) mlp, &mlp_term_hook);
/* Init LP solving parameters */
glp_init_smcp(&mlp->control_param_lp);
mlp->control_param_lp.msg_lev = GLP_MSG_OFF;
#if VERBOSE_GLPK
mlp->control_param_lp.msg_lev = GLP_MSG_ALL;
#endif
mlp->control_param_lp.it_lim = max_iterations;
mlp->control_param_lp.tm_lim = max_duration.rel_value;
/* Init MLP solving parameters */
glp_init_iocp(&mlp->control_param_mlp);
mlp->control_param_mlp.msg_lev = GLP_MSG_OFF;
#if VERBOSE_GLPK
mlp->control_param_mlp.msg_lev = GLP_MSG_ALL;
#endif
mlp->control_param_mlp.tm_lim = max_duration.rel_value;
mlp->last_execution = GNUNET_TIME_UNIT_FOREVER_ABS;
mlp->co_D = D;
mlp->co_R = R;
mlp->co_U = U;
mlp->b_min = b_min;
mlp->n_min = n_min;
mlp->m_q = GNUNET_ATS_QualityPropertiesCount;
mlp->semaphore = GNUNET_NO;
return mlp;
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
// Obsolete
//lib_set_fault_hook (NULL, glpk_fault_hook);
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
glp_prob *lp = glp_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (lpx_write_cpxlp (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (lpx_write_mps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (lpx_write_freemps (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpkcc: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (glpIntParam[1] && (! glpIntParam[16] || lpsolver != 1))
lpx_scale_prob (lp);
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
lpx_adv_basis (lp);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters
if (lpsolver==1){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
if (glpIntParam[2]==0) sParam.meth=GLP_PRIMAL;
else sParam.meth=GLP_DUALP;
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
//sParam.r_test not available
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++)
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
for (int i = 0; i < NRealP; i++)
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
// Choose simplex method ('S') or interior point method ('T') to solve the problem
if (lpsolver == 1)
method = 'S';
else
method = 'T';
int errnum;
switch (method){
case 'S': {
if (isMIP){
method = 'I';
errnum = lpx_intopt (lp);
}
else{
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
}
}
break;
case 'T': errnum = lpx_interior(lp); break;
default: xassert (method != method);
}
/* errnum assumes the following results:
errnum = 0 <=> No errors
errnum = 1 <=> Iteration limit exceeded.
errnum = 2 <=> Numerical problems with basis matrix.
*/
if (errnum == LPX_E_OK || errnum==100){
// Get status and object value
if (isMIP)
{
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else
{
if (lpsolver == 1)
{
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else
{
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP)
{
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else
{
/* Primal values */
for (int i = 0; i < n; i++)
{
if (lpsolver == 1)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++)
{
if (lpsolver == 1) lambda[i] = glp_get_row_dual (lp, i+1);
else lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++)
{
if (lpsolver == 1) redcosts[i] = glp_get_col_dual (lp, i+1);
else redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
glp_ulong tpeak;
lib_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=(double)(4294967296.0 * tpeak.hi + tpeak.lo) / (1024);
glp_delete_prob (lp);
return 0;
}
glp_delete_prob (lp);
*status = errnum;
return errnum;
}
int main(int argc, char** argv)
{
/*==================================================*/
/* Variables */
data d;
int* M;
/* GLPK */
int *ia, *ja;
double *ar;
double z;
double *x;
int notZeroCount;
/* Misc */
int i, pos;
/* Check up */
if(argc != 2)
{
printf("ERROR : no data file !\n");
exit(1);
}
/* Initialization */
filereader(argv[1], &d);
if(d.nbjour < 1)
{
printf("Obvious...\n");
return 0;
}
M = (int*) malloc ((d.nbjour +1)* sizeof(int));
M[d.nbjour] = d.d[d.nbjour];
for (i = d.nbjour-1; i >=0; --i)
{
M[i] = d.d[i] + M[i+1];
}
/* Problem creation*/
glp_prob *prob;
prob = glp_create_prob();
glp_set_prob_name(prob, "ULS");
glp_set_obj_dir(prob, GLP_MIN);
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;
/* Number of constraints : 2 * nbjour +2 */
glp_add_rows(prob, 2*d.nbjour +2);
for (i = 1; i <= d.nbjour; ++i)
{
glp_set_row_bnds(prob, i, GLP_FX, d.d[i], d.d[i]);
}
for (i = d.nbjour+1; i <= 2*d.nbjour; ++i)
{
glp_set_row_bnds(prob, i, GLP_LO, 0, 0);
}
glp_set_row_bnds(prob, 2*d.nbjour+1, GLP_FX, 0.0, 0.0);
glp_set_row_bnds(prob, 2*d.nbjour+2, GLP_FX, 0.0, 0.0);
/* Number of variables : 3*(nbjour +1)*/
glp_add_cols(prob, 3*(d.nbjour+1));
for (i = 0; i < d.nbjour +1; ++i)
{
glp_set_col_bnds(prob, i*3 +1, GLP_LO, 0.0, 0.0);
glp_set_col_bnds(prob, i*3 +2, GLP_LO, 0.0, 0.0);
glp_set_col_bnds(prob, i*3 +3, GLP_DB, 0.0, 1.0);
}
for (i = 1; i <= 3*(d.nbjour+1); ++i)
{
glp_set_col_kind(prob, i, GLP_CV);
}
/* Coefficients of the economic function */
glp_set_obj_coef(prob, 1, 0);
glp_set_obj_coef(prob, 2, 0);
glp_set_obj_coef(prob, 3, 0);
for (i = 1; i <=d.nbjour; ++i)
{
glp_set_obj_coef(prob, 3*i+1, d.p[i]);
glp_set_obj_coef(prob, 3*i+2, d.h[i]);
glp_set_obj_coef(prob, 3*i+3, d.f[i]);
}
/* Matrix */
notZeroCount = 5 * d.nbjour + 2;
ia = (int *) malloc ((1+notZeroCount) * sizeof(int));
ja = (int *) malloc ((1+notZeroCount) * sizeof(int));
ar = (double *) malloc ((1+notZeroCount) * sizeof(double));
pos = 1;
for (i = 1; i <= d.nbjour; ++i)
{
ia[pos] = i;
ia[pos+1] = i;
ia[pos+2] = i;
ja[pos] = i*3-1;
ja[pos+1] = i*3+1;
ja[pos+2] = i*3+2;
ar[pos] = 1.0;
ar[pos+1] = 1.0;
ar[pos+2] = -1.0;
pos += 3;
}
for (i = 1; i <= d.nbjour; ++i)
{
ia[pos] = i + d.nbjour;
ia[pos+1] = i + d.nbjour;
ja[pos] = i*3+1;
ja[pos+1] = i*3+3;
ar[pos] = -1.0;
ar[pos+1] = M[i];
pos += 2;
}
ia[pos] = 2*d.nbjour +1;
ia[pos+1] = 2 * d.nbjour+2;
ja[pos] = 3*(d.nbjour+1)-1;
ja[pos+1] = 2;
ar[pos] = 1.0;
ar[pos+1] = 1.0;
pos += 2;
glp_load_matrix(prob, notZeroCount, ia, ja , ar);
/* Writing in a file */
glp_write_lp(prob, NULL, "ULS.lp");
/* Branch and bound */
node* res = branchAndBound(prob);
displayNode(res);
}
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;
}
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* LPX_solver_simplex(LPXObject *self, PyObject *args,
PyObject *keywds) {
#if GLPK_VERSION(4, 18)
glp_smcp cp;
// Set all to GLPK defaults, except for the message level, which
// inexplicably has a default "verbose" setting.
glp_init_smcp(&cp);
cp.msg_lev = GLP_MSG_OFF;
// Map the keyword arguments to the appropriate entries.
static char *kwlist[] =
{"msg_lev", "meth", "pricing", "r_test", "tol_bnd", "tol_dj", "tol_piv",
"obj_ll", "obj_ul", "it_lim", "tm_lim", "out_frq", "out_dly", "presolve",
NULL};
if (!PyArg_ParseTupleAndKeywords
(args, keywds, "|iiiidddddiiiii", kwlist, &cp.msg_lev, &cp.meth,
&cp.pricing, &cp.r_test, &cp.tol_bnd, &cp.tol_dj, &cp.tol_piv,
&cp.obj_ll, &cp.obj_ul, &cp.it_lim, &cp.tm_lim, &cp.out_frq,
&cp.out_dly, &cp.presolve)) {
return NULL;
}
cp.presolve = cp.presolve ? GLP_ON : GLP_OFF;
// Do checking on the various entries.
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.meth) {
case GLP_PRIMAL: case GLP_DUALP: break;
#if GLPK_VERSION(4, 31)
case GLP_DUAL: break;
#endif
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for meth (LPX.PRIMAL, LPX.DUAL, "
"LPX.DUALP valid values)");
return NULL;
}
switch (cp.pricing) {
case GLP_PT_STD: case GLP_PT_PSE: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for pricing (LPX.PT_STD, LPX.PT_PSE valid values)");
return NULL;
}
switch (cp.r_test) {
case GLP_RT_STD: case GLP_RT_HAR: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for ratio test (LPX.RT_STD, LPX.RT_HAR valid values)");
return NULL;
}
if (cp.tol_bnd<=0 || cp.tol_bnd>=1) {
PyErr_SetString(PyExc_ValueError, "tol_bnd must obey 0<tol_bnd<1");
return NULL;
}
if (cp.tol_dj<=0 || cp.tol_dj>=1) {
PyErr_SetString(PyExc_ValueError, "tol_dj must obey 0<tol_dj<1");
return NULL;
}
if (cp.tol_piv<=0 || cp.tol_piv>=1) {
PyErr_SetString(PyExc_ValueError, "tol_piv must obey 0<tol_piv<1");
return NULL;
}
if (cp.it_lim<0) {
PyErr_SetString(PyExc_ValueError, "it_lim must be non-negative");
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;
}
// All the checks are complete. Call the simplex solver.
int retval = glp_simplex(LP, &cp);
if (retval!=GLP_EBADB && retval!=GLP_ESING && retval!=GLP_ECOND
&& retval!=GLP_EBOUND && retval!=GLP_EFAIL)
self->last_solver = 0;
return glpsolver_retval_to_message(retval);
#else
int retval = lpx_simplex(LP);
if (retval!=LPX_E_FAULT) self->last_solver = 0;
return solver_retval_to_message(retval);
#endif
}
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;
}
static int preprocess_and_solve_mip(glp_prob *P, const glp_iocp *parm)
{ /* solve MIP using the preprocessor */
ENV *env = get_env_ptr();
int term_out = env->term_out;
NPP *npp;
glp_prob *mip = NULL;
glp_bfcp bfcp;
glp_smcp smcp;
int ret;
if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("Preprocessing...\n");
/* create preprocessor workspace */
npp = npp_create_wksp();
/* load original problem into the preprocessor workspace */
npp_load_prob(npp, P, GLP_OFF, GLP_MIP, GLP_OFF);
/* process MIP prior to applying the branch-and-bound method */
if (!term_out || parm->msg_lev < GLP_MSG_ALL)
env->term_out = GLP_OFF;
else
env->term_out = GLP_ON;
ret = npp_integer(npp, parm);
env->term_out = term_out;
if (ret == 0)
;
else if (ret == GLP_ENOPFS)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("PROBLEM HAS NO PRIMAL FEASIBLE SOLUTION\n");
}
else if (ret == GLP_ENODFS)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("LP RELAXATION HAS NO DUAL FEASIBLE SOLUTION\n");
}
else
xassert(ret != ret);
if (ret != 0) goto done;
/* build transformed MIP */
mip = glp_create_prob();
npp_build_prob(npp, mip);
/* if the transformed MIP is empty, it has empty solution, which
is optimal */
if (mip->m == 0 && mip->n == 0)
{ mip->mip_stat = GLP_OPT;
mip->mip_obj = mip->c0;
if (parm->msg_lev >= GLP_MSG_ALL)
{ xprintf("Objective value = %17.9e\n", mip->mip_obj);
xprintf("INTEGER OPTIMAL SOLUTION FOUND BY MIP PREPROCESSOR"
"\n");
}
goto post;
}
/* display some statistics */
if (parm->msg_lev >= GLP_MSG_ALL)
{ int ni = glp_get_num_int(mip);
int nb = glp_get_num_bin(mip);
char s[50];
xprintf("%d row%s, %d column%s, %d non-zero%s\n",
mip->m, mip->m == 1 ? "" : "s", mip->n, mip->n == 1 ? "" :
"s", mip->nnz, mip->nnz == 1 ? "" : "s");
if (nb == 0)
strcpy(s, "none of");
else if (ni == 1 && nb == 1)
strcpy(s, "");
else if (nb == 1)
strcpy(s, "one of");
else if (nb == ni)
strcpy(s, "all of");
else
sprintf(s, "%d of", nb);
xprintf("%d integer variable%s, %s which %s binary\n",
ni, ni == 1 ? "" : "s", s, nb == 1 ? "is" : "are");
}
/* inherit basis factorization control parameters */
glp_get_bfcp(P, &bfcp);
glp_set_bfcp(mip, &bfcp);
/* scale the transformed problem */
if (!term_out || parm->msg_lev < GLP_MSG_ALL)
env->term_out = GLP_OFF;
else
env->term_out = GLP_ON;
glp_scale_prob(mip,
GLP_SF_GM | GLP_SF_EQ | GLP_SF_2N | GLP_SF_SKIP);
env->term_out = term_out;
/* build advanced initial basis */
if (!term_out || parm->msg_lev < GLP_MSG_ALL)
env->term_out = GLP_OFF;
else
env->term_out = GLP_ON;
glp_adv_basis(mip, 0);
env->term_out = term_out;
/* solve initial LP relaxation */
if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("Solving LP relaxation...\n");
glp_init_smcp(&smcp);
smcp.msg_lev = parm->msg_lev;
mip->it_cnt = P->it_cnt;
ret = glp_simplex(mip, &smcp);
P->it_cnt = mip->it_cnt;
if (ret != 0)
{ if (parm->msg_lev >= GLP_MSG_ERR)
xprintf("glp_intopt: cannot solve LP relaxation\n");
ret = GLP_EFAIL;
goto done;
}
/* check status of the basic solution */
ret = glp_get_status(mip);
if (ret == GLP_OPT)
ret = 0;
else if (ret == GLP_NOFEAS)
ret = GLP_ENOPFS;
else if (ret == GLP_UNBND)
ret = GLP_ENODFS;
else
xassert(ret != ret);
if (ret != 0) goto done;
/* solve the transformed MIP */
mip->it_cnt = P->it_cnt;
#if 0 /* 11/VII-2013 */
ret = solve_mip(mip, parm);
#else
if (parm->use_sol)
{ mip->mip_stat = P->mip_stat;
mip->mip_obj = P->mip_obj;
}
ret = solve_mip(mip, parm, P, npp);
#endif
P->it_cnt = mip->it_cnt;
/* only integer feasible solution can be postprocessed */
if (!(mip->mip_stat == GLP_OPT || mip->mip_stat == GLP_FEAS))
{ P->mip_stat = mip->mip_stat;
goto done;
}
/* postprocess solution from the transformed MIP */
post: npp_postprocess(npp, mip);
/* the transformed MIP is no longer needed */
glp_delete_prob(mip), mip = NULL;
/* store solution to the original problem */
npp_unload_sol(npp, P);
done: /* delete the transformed MIP, if it exists */
if (mip != NULL) glp_delete_prob(mip);
/* delete preprocessor workspace */
npp_delete_wksp(npp);
return ret;
}
void ios_feas_pump(glp_tree *T)
{ glp_prob *P = T->mip;
int n = P->n;
glp_prob *lp = NULL;
struct VAR *var = NULL;
RNG *rand = NULL;
GLPCOL *col;
glp_smcp parm;
int j, k, new_x, nfail, npass, nv, ret, stalling;
double dist, tol;
xassert(glp_get_status(P) == GLP_OPT);
/* this heuristic is applied only once on the root level */
if (!(T->curr->level == 0 && T->curr->solved == 1)) goto done;
/* determine number of binary variables */
nv = 0;
for (j = 1; j <= n; j++)
{ col = P->col[j];
/* if x[j] is continuous, skip it */
if (col->kind == GLP_CV) continue;
/* if x[j] is fixed, skip it */
if (col->type == GLP_FX) continue;
/* x[j] is non-fixed integer */
xassert(col->kind == GLP_IV);
if (col->type == GLP_DB && col->lb == 0.0 && col->ub == 1.0)
{ /* x[j] is binary */
nv++;
}
else
{ /* x[j] is general integer */
if (T->parm->msg_lev >= GLP_MSG_ALL)
xprintf("FPUMP heuristic cannot be applied due to genera"
"l integer variables\n");
goto done;
}
}
/* there must be at least one binary variable */
if (nv == 0) goto done;
if (T->parm->msg_lev >= GLP_MSG_ALL)
xprintf("Applying FPUMP heuristic...\n");
/* build the list of binary variables */
var = xcalloc(1+nv, sizeof(struct VAR));
k = 0;
for (j = 1; j <= n; j++)
{ col = P->col[j];
if (col->kind == GLP_IV && col->type == GLP_DB)
var[++k].j = j;
}
xassert(k == nv);
/* create working problem object */
lp = glp_create_prob();
more: /* copy the original problem object to keep it intact */
glp_copy_prob(lp, P, GLP_OFF);
/* we are interested to find an integer feasible solution, which
is better than the best known one */
if (P->mip_stat == GLP_FEAS)
{ int *ind;
double *val, bnd;
/* add a row and make it identical to the objective row */
glp_add_rows(lp, 1);
ind = xcalloc(1+n, sizeof(int));
val = xcalloc(1+n, sizeof(double));
for (j = 1; j <= n; j++)
{ ind[j] = j;
val[j] = P->col[j]->coef;
}
glp_set_mat_row(lp, lp->m, n, ind, val);
xfree(ind);
xfree(val);
/* introduce upper (minimization) or lower (maximization)
bound to the original objective function; note that this
additional constraint is not violated at the optimal point
to LP relaxation */
#if 0 /* modified by xypron <*****@*****.**> */
if (P->dir == GLP_MIN)
{ bnd = P->mip_obj - 0.10 * (1.0 + fabs(P->mip_obj));
if (bnd < P->obj_val) bnd = P->obj_val;
glp_set_row_bnds(lp, lp->m, GLP_UP, 0.0, bnd - P->c0);
}
else if (P->dir == GLP_MAX)
{ bnd = P->mip_obj + 0.10 * (1.0 + fabs(P->mip_obj));
if (bnd > P->obj_val) bnd = P->obj_val;
glp_set_row_bnds(lp, lp->m, GLP_LO, bnd - P->c0, 0.0);
}
else
xassert(P != P);
#else
bnd = 0.1 * P->obj_val + 0.9 * P->mip_obj;
/* xprintf("bnd = %f\n", bnd); */
if (P->dir == GLP_MIN)
glp_set_row_bnds(lp, lp->m, GLP_UP, 0.0, bnd - P->c0);
else if (P->dir == GLP_MAX)
glp_set_row_bnds(lp, lp->m, GLP_LO, bnd - P->c0, 0.0);
else
xassert(P != P);
#endif
}
/* reset pass count */
npass = 0;
/* invalidate the rounded point */
for (k = 1; k <= nv; k++)
var[k].x = -1;
pass: /* next pass starts here */
npass++;
if (T->parm->msg_lev >= GLP_MSG_ALL)
xprintf("Pass %d\n", npass);
/* initialize minimal distance between the basic point and the
rounded one obtained during this pass */
dist = DBL_MAX;
/* reset failure count (the number of succeeded iterations failed
to improve the distance) */
nfail = 0;
/* if it is not the first pass, perturb the last rounded point
rather than construct it from the basic solution */
if (npass > 1)
{ double rho, temp;
if (rand == NULL)
rand = rng_create_rand();
for (k = 1; k <= nv; k++)
{ j = var[k].j;
col = lp->col[j];
rho = rng_uniform(rand, -0.3, 0.7);
if (rho < 0.0) rho = 0.0;
temp = fabs((double)var[k].x - col->prim);
if (temp + rho > 0.5) var[k].x = 1 - var[k].x;
}
goto skip;
}
loop: /* innermost loop begins here */
/* round basic solution (which is assumed primal feasible) */
stalling = 1;
for (k = 1; k <= nv; k++)
{ col = lp->col[var[k].j];
if (col->prim < 0.5)
{ /* rounded value is 0 */
new_x = 0;
}
else
{ /* rounded value is 1 */
new_x = 1;
}
if (var[k].x != new_x)
{ stalling = 0;
var[k].x = new_x;
}
}
/* if the rounded point has not changed (stalling), choose and
flip some its entries heuristically */
if (stalling)
{ /* compute d[j] = |x[j] - round(x[j])| */
for (k = 1; k <= nv; k++)
{ col = lp->col[var[k].j];
var[k].d = fabs(col->prim - (double)var[k].x);
}
/* sort the list of binary variables by descending d[j] */
qsort(&var[1], nv, sizeof(struct VAR), fcmp);
/* choose and flip some rounded components */
for (k = 1; k <= nv; k++)
{ if (k >= 5 && var[k].d < 0.35 || k >= 10) break;
var[k].x = 1 - var[k].x;
}
}
skip: /* check if the time limit has been exhausted */
if (T->parm->tm_lim < INT_MAX &&
(double)(T->parm->tm_lim - 1) <=
1000.0 * xdifftime(xtime(), T->tm_beg)) goto done;
/* build the objective, which is the distance between the current
(basic) point and the rounded one */
lp->dir = GLP_MIN;
lp->c0 = 0.0;
for (j = 1; j <= n; j++)
lp->col[j]->coef = 0.0;
for (k = 1; k <= nv; k++)
{ j = var[k].j;
if (var[k].x == 0)
lp->col[j]->coef = +1.0;
else
{ lp->col[j]->coef = -1.0;
lp->c0 += 1.0;
}
}
/* minimize the distance with the simplex method */
glp_init_smcp(&parm);
if (T->parm->msg_lev <= GLP_MSG_ERR)
parm.msg_lev = T->parm->msg_lev;
else if (T->parm->msg_lev <= GLP_MSG_ALL)
{ parm.msg_lev = GLP_MSG_ON;
parm.out_dly = 10000;
}
ret = glp_simplex(lp, &parm);
if (ret != 0)
{ if (T->parm->msg_lev >= GLP_MSG_ERR)
xprintf("Warning: glp_simplex returned %d\n", ret);
goto done;
}
ret = glp_get_status(lp);
if (ret != GLP_OPT)
{ if (T->parm->msg_lev >= GLP_MSG_ERR)
xprintf("Warning: glp_get_status returned %d\n", ret);
goto done;
}
if (T->parm->msg_lev >= GLP_MSG_DBG)
xprintf("delta = %g\n", lp->obj_val);
/* check if the basic solution is integer feasible; note that it
may be so even if the minimial distance is positive */
tol = 0.3 * T->parm->tol_int;
for (k = 1; k <= nv; k++)
{ col = lp->col[var[k].j];
if (tol < col->prim && col->prim < 1.0 - tol) break;
}
if (k > nv)
{ /* okay; the basic solution seems to be integer feasible */
double *x = xcalloc(1+n, sizeof(double));
for (j = 1; j <= n; j++)
{ x[j] = lp->col[j]->prim;
if (P->col[j]->kind == GLP_IV) x[j] = floor(x[j] + 0.5);
}
#if 1 /* modified by xypron <*****@*****.**> */
/* reset direction and right-hand side of objective */
lp->c0 = P->c0;
lp->dir = P->dir;
/* fix integer variables */
for (k = 1; k <= nv; k++)
#if 0 /* 18/VI-2013; fixed by mao
* this bug causes numerical instability, because column statuses
* are not changed appropriately */
{ lp->col[var[k].j]->lb = x[var[k].j];
lp->col[var[k].j]->ub = x[var[k].j];
lp->col[var[k].j]->type = GLP_FX;
}
#else
glp_set_col_bnds(lp, var[k].j, GLP_FX, x[var[k].j], 0.);
#endif
/* copy original objective function */
for (j = 1; j <= n; j++)
lp->col[j]->coef = P->col[j]->coef;
/* solve original LP and copy result */
ret = glp_simplex(lp, &parm);
if (ret != 0)
{ if (T->parm->msg_lev >= GLP_MSG_ERR)
xprintf("Warning: glp_simplex returned %d\n", ret);
goto done;
}
ret = glp_get_status(lp);
if (ret != GLP_OPT)
{ if (T->parm->msg_lev >= GLP_MSG_ERR)
xprintf("Warning: glp_get_status returned %d\n", ret);
goto done;
}
for (j = 1; j <= n; j++)
if (P->col[j]->kind != GLP_IV) x[j] = lp->col[j]->prim;
#endif
ret = glp_ios_heur_sol(T, x);
xfree(x);
if (ret == 0)
{ /* the integer solution is accepted */
if (ios_is_hopeful(T, T->curr->bound))
{ /* it is reasonable to apply the heuristic once again */
goto more;
}
else
{ /* the best known integer feasible solution just found
is close to optimal solution to LP relaxation */
goto done;
}
}
}
static bool Graph_solve(Graph& graph, size_t loops, PositionList* position_tbl) {
glp_prob* lp = glp_create_prob();
glp_set_prob_name(lp, "scaffold");
glp_set_obj_dir(lp, GLP_MIN);
size_t rows = 0, cols = 0, vals = 0;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
++cols; // var x_i
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
rows += 3;
cols += 2; // var e_i_j; var E_i_j;
vals += 7;
}
}
glp_add_rows(lp, rows);
glp_add_cols(lp, cols);
std::map< std::pair< size_t, size_t >, size_t > mapping;
{
size_t row = 1, col = 1;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
mapping[std::make_pair(i->first, -1)] = col;
glp_set_col_bnds(lp, col++, GLP_LO, 0.0, 0.0); // x_i >= 0
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
glp_set_row_bnds(lp, row++, GLP_FX, j->second, j->second);
glp_set_row_bnds(lp, row++, GLP_LO, 0.0, 0.0);
glp_set_row_bnds(lp, row++, GLP_LO, 0.0, 0.0);
mapping[std::make_pair(i->first, j->first)] = col;
++col; // var e_i
glp_set_obj_coef(lp, col++, 1.0);
}
}
}
LOG4CXX_TRACE(logger, boost::format(" rows = %d, cols = %d, vals = %d") % rows % cols % vals);
int* ia = new int[vals + 1];
int* ja = new int[vals + 1];
double* ra = new double[vals + 1];
{
size_t l = 1, row = 1, col = 1;
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
for (Children::const_iterator j = i->second.children.begin(); j != i->second.children.end(); ++j) {
// x_j - x_i + e_i_j = d_i_j
ia[l] = row;
ja[l] = mapping[std::make_pair(j->first, -1)];
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, -1)];
ra[l] = -1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = 1.0;
++l;
++row;
// E_i_j + e_i_j >= 0
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 1;
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = 1.0;
++l;
++row;
// E_i_j - e_i_j >= 0
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 1;
ra[l] = 1.0;
++l;
ia[l] = row;
ja[l] = mapping[std::make_pair(i->first, j->first)] + 0;
ra[l] = -1.0;
++l;
++row;
}
}
}
glp_load_matrix(lp, vals, ia, ja, ra);
glp_smcp parm;
glp_init_smcp(&parm);
parm.it_lim = loops;
//parm.pricing = GLP_PT_PSE;
//parm.presolve = GLP_ON;
parm.msg_lev = GLP_MSG_ERR;
glp_simplex(lp, &parm);
double z = glp_get_obj_val(lp);
LOG4CXX_TRACE(logger, boost::format("z = %f") % z);
for (Graph::const_iterator i = graph.begin(); i != graph.end(); ++i) {
size_t val = glp_get_col_prim(lp, mapping[std::make_pair(i->first, -1)]);
if (position_tbl != NULL) {
(*position_tbl)[i->first] = val;
}
LOG4CXX_TRACE(logger, boost::format("x\t%d\t%d") % i->first % val);
}
delete[] ra;
delete[] ja;
delete[] ia;
glp_delete_prob(lp);
return true;
}
extern void* jl_glpkw__smcp_init()
{
glp_smcp * smcp = calloc(1, sizeof(glp_smcp));
glp_init_smcp(smcp);
return (void*) smcp;
}
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;
}
int main(int argc, char * argv[]) {
int i,j;
time(&initial);
srand(SEED);
/* Default values */
outFile = stdout;
maxAlpha = 2;
maxIter = 100;
maxTime = 30;
randomSeed = SEED;
simpleOutput = 0;
/* Read arguments */
if( argc > 7 )
argc = 7;
switch(argc) {
case 7:
simpleOutput = atoi(argv[6]);
case 6:
if( !(randomSeed = atoi(argv[5])) )
leave(argv[0]);
case 5:
if( !(maxTime = atoi(argv[4])) )
leave(argv[0]);
case 4:
if( !(maxIter = atoi(argv[3])) )
leave(argv[0]);
case 3:
if( !(maxAlpha = atoi(argv[2])) )
leave(argv[0]);
case 2:
if( simpleOutput ) {
if( !(outFile = fopen(argv[1],"a")) )
leave(argv[0]);
break;
}
if( !(outFile = fopen(argv[1],"w")) )
leave(argv[0]);
}
readInput(stdin);
/* Initiate positions */
for( i = 0 ; i < n ; ++i ) {
pOrd[i].ideal = planes[i].ideal;
pOrd[i].pos = i;
}
qsort (pOrd, n, sizeof(struct planeOrder), compIdealT);
for( i = 0 ; i < n ; ++i ) {
planes[pOrd[i].pos].pos = i;
}
/* Create lp instance */
glp_prob * Prob;
Prob = glp_create_prob();
glp_set_prob_name(Prob, "Airplane Landing Problem");
glp_set_obj_name(Prob, "Cost");
/* Create basic constraints */
for( i = 0 ; i < n ; ++i ) {
addBasicRestriction(Prob,i);
}
glp_create_index(Prob);
/* Create separation constraints and order variables (&ij) if necessary */
for( i = 0 ; i < n ; ++i ) {
for( j = i+1 ; j < n ; ++j ) {
if( planes[i].latest >= planes[j].earliest &&
planes[j].latest >= planes[i].earliest ) {
addOrderConstraint(Prob,i,j);
} else if ( planes[i].latest < planes[j].earliest &&
planes[i].latest + planes[i].sep[j] >= planes[j].earliest ) {
addSeparationConstraint(Prob, i, j);
} else if ( planes[j].latest < planes[i].earliest &&
planes[j].latest + planes[j].sep[i] >= planes[i].earliest ) {
addSeparationConstraint(Prob, j, i);
}
}
}
/* Write problem in MPS format so glpsol can (try to) solve it */
glp_write_mps(Prob, GLP_MPS_FILE, NULL,"mpsProblem.txt");
glp_delete_index(Prob);
glp_create_index(Prob);
/* GRASP */
/* Data to handle glp solving, time checking and solution generating */
glp_smcp * param = malloc(sizeof(glp_smcp));
glp_init_smcp(param);
param->msg_lev = GLP_MSG_ERR;
int solution[MAXSIZE], timeAux[MAXSIZE], t;
double currResult = DBL_MAX, bestResult = DBL_MAX;
alpha = 0;
time_t start, curr;
time(&start);
for( t = 0 ; t < maxIter ; ++t ) {
/* Greedy solution generation */
while(createSolution(solution,timeAux,0))
alpha = n;
/* Building the right constraints */
mapSolution(Prob,solution);
/* Solving with glpsol */
param->presolve = GLP_ON;
glp_simplex(Prob,param);
param->presolve = GLP_OFF;
currResult = glp_get_obj_val(Prob);
/* Local search using the first increase */
for( i = 0 ; i < n-1 ; ++i ) {
/* Swap two adjacent planes */
swapConstraint(Prob,i,solution,0);
glp_simplex(Prob,param);
/* Check for improvements */
if( GLP_OPT == glp_get_status(Prob) && glp_get_obj_val(Prob) < currResult ) {
currResult = glp_get_obj_val(Prob);
/* Changing the solution */
int swp;
swp = solution[i];
solution[i] = solution[i+1];
solution[i+1] = swp;
/* Restarting */
i = -1;
} else
swapConstraint(Prob,i,solution,1);
}
/* Checking improvements */
if( bestResult > currResult ) {
bestResult = currResult;
for( i = 0 ; i < n ; ++i )
planes[solution[i]].pos = i;
}
/* Choosing alpha */
alpha = rand()%(maxAlpha+1);
/* Is our time up? */
time(&curr);
if( difftime(curr,start) > maxTime )
break;
}
/* Print Answer */
printResult(Prob, stdout);
if( outFile ) {
printResult(Prob, outFile);
fclose(outFile);
}
return 0;
}
bool glpk_wrapper::is_sat() {
if (solver_type == SIMPLEX || solver_type == EXACT) {
int status = glp_get_status(lp);
if (status == GLP_UNDEF || changed) {
glp_smcp parm;
glp_init_smcp(&parm);
parm.msg_lev = GLP_MSG_OFF;
// always try first the normal simple (get close to an optimal solution in double precision)
int solved = glp_simplex(lp, &parm);
// TODO(dzufferey) should we always fall back on exact when the normal simplex failed ?
if (solver_type == EXACT || solved != 0) {
solved = glp_exact(lp, &parm);
}
if (solved != 0) {
switch (solved) {
case GLP_EBADB:
throw std::runtime_error("GLPK simplex failed: GLP_EBADB");
case GLP_ESING:
throw std::runtime_error("GLPK simplex failed: GLP_ESING");
case GLP_ECOND:
throw std::runtime_error("GLPK simplex failed: GLP_ECOND");
case GLP_EBOUND:
throw std::runtime_error("GLPK simplex failed: GLP_EBOUND");
case GLP_EFAIL:
throw std::runtime_error("GLPK simplex failed: GLP_EFAIL");
case GLP_EOBJLL:
throw std::runtime_error("GLPK simplex failed: GLP_EOBJLL");
case GLP_EOBJUL:
throw std::runtime_error("GLPK simplex failed: GLP_EOBJUL");
case GLP_EITLIM:
throw std::runtime_error("GLPK simplex failed: GLP_EITLIM");
case GLP_ETMLIM:
throw std::runtime_error("GLPK simplex failed: GLP_ETMLIM");
case GLP_ENOPFS:
throw std::runtime_error("GLPK simplex failed: GLP_ENOPFS");
case GLP_ENODFS:
throw std::runtime_error("GLPK simplex failed: GLP_ENODFS");
default:
throw std::runtime_error("GLPK simplex failed");
}
}
status = glp_get_status(lp);
changed = false;
}
return (status == GLP_OPT || status == GLP_FEAS || status == GLP_UNBND);
} else {
assert(solver_type == INTERIOR);
int status = glp_ipt_status(lp);
if (status == GLP_UNDEF || changed) {
glp_iptcp parm;
glp_init_iptcp(&parm);
parm.msg_lev = GLP_MSG_OFF;
int solved = glp_interior(lp, &parm);
if (solved != 0) {
switch (solved) {
case GLP_EFAIL:
throw std::runtime_error("GLPK interior-point failed: GLP_EFAIL");
case GLP_ENOCVG:
throw std::runtime_error("GLPK interior-point failed: GLP_ENOCVG");
case GLP_EOBJUL:
throw std::runtime_error("GLPK interior-point failed: GLP_EOBJUL");
case GLP_EITLIM:
throw std::runtime_error("GLPK interior-point failed: GLP_EITLIM");
case GLP_EINSTAB:
throw std::runtime_error("GLPK interior-point failed: GLP_EINSTAB");
default:
throw std::runtime_error("GLPK interior-point failed");
}
}
status = glp_ipt_status(lp);
changed = false;
}
return status == GLP_OPT;
}
}
