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.");
}
}
bool glpk_wrapper::is_solution_unbounded() {
assert(is_sat());
if (solver_type == SIMPLEX || solver_type == EXACT) {
int status = glp_get_status(lp);
return status == GLP_UNBND;
} else {
return false;
}
}
static int solve_mip(glp_prob *P, const glp_iocp *parm)
{ /* solve MIP directly without using the preprocessor */
glp_tree *T;
int ret;
/* optimal basis to LP relaxation must be provided */
if (glp_get_status(P) != GLP_OPT)
{ if (parm->msg_lev >= GLP_MSG_ERR)
xprintf("glp_intopt: optimal basis to initial LP relaxation"
" not provided\n");
ret = GLP_EROOT;
goto done;
}
/* it seems all is ok */
if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("Integer optimization begins...\n");
/* create the branch-and-bound tree */
T = ios_create_tree(P, parm);
/* solve the problem instance */
ret = ios_driver(T);
/* delete the branch-and-bound tree */
ios_delete_tree(T);
/* analyze exit code reported by the mip driver */
if (ret == 0)
{ if (P->mip_stat == GLP_FEAS)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("INTEGER OPTIMAL SOLUTION FOUND\n");
P->mip_stat = GLP_OPT;
}
else
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("PROBLEM HAS NO INTEGER FEASIBLE SOLUTION\n");
P->mip_stat = GLP_NOFEAS;
}
}
else if (ret == GLP_EMIPGAP)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("RELATIVE MIP GAP TOLERANCE REACHED; SEARCH TERMINA"
"TED\n");
}
else if (ret == GLP_ETMLIM)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("TIME LIMIT EXCEEDED; SEARCH TERMINATED\n");
}
else if (ret == GLP_EFAIL)
{ if (parm->msg_lev >= GLP_MSG_ERR)
xprintf("glp_intopt: cannot solve current LP relaxation\n");
}
else if (ret == GLP_ESTOP)
{ if (parm->msg_lev >= GLP_MSG_ALL)
xprintf("SEARCH TERMINATED BY APPLICATION\n");
}
else
xassert(ret != ret);
done: return ret;
}
int CMyProblem::PrintLPSolution(ostream &out)
{
glp_create_index(lp);
out << "LP solution" << endl;
out << "Dir;" << ((glp_get_obj_dir(lp)==GLP_MIN) ? "min" : "max") << endl;
out << "f; " << glp_get_obj_val(lp) << ";/*" << RealResult() << "*/" << endl;
out << "Status;" << DecodeStatus(glp_get_status(lp)) << endl;
PrintSolArray(lp,"x",out);
PrintSolArray(lp,"y",out);
glp_delete_index(lp);
return 0;
}
/* call the LP solver; mult is either +1.0 or -1.0 */
static char *invoke_lp(void)
{int glp_res;
glp_res=glp_simplex(P,&parm);
switch(glp_res){
case 0: glp_res=glp_get_status(P); break;
case GLP_ENOPFS: // no primal feasible solution
glp_res=GLP_NOFEAS; break;
default: return glp_return_msg(glp_res);
}
return (glp_res==GLP_OPT ? EXPR_TRUE :
glp_res==GLP_NOFEAS ? EXPR_FALSE :
glp_status_msg(glp_res));
}
int lpx_get_status(glp_prob *lp)
{ /* retrieve generic status of basic solution */
int status;
switch (glp_get_status(lp))
{ case GLP_OPT: status = LPX_OPT; break;
case GLP_FEAS: status = LPX_FEAS; break;
case GLP_INFEAS: status = LPX_INFEAS; break;
case GLP_NOFEAS: status = LPX_NOFEAS; break;
case GLP_UNBND: status = LPX_UNBND; break;
case GLP_UNDEF: status = LPX_UNDEF; break;
default: xassert(lp != lp);
}
return status;
}
static PyObject* LPX_getstatus(LPXObject *self, void *closure) {
int status;
switch (self->last_solver) {
case -1:
case 0: status=glp_get_status(LP); break;
case 1: status=glp_ipt_status(LP); break;
case 2: status=glp_mip_status(LP); break;
default:
PyErr_SetString(PyExc_RuntimeError,
"bad internal state for last solver identifier");
return NULL;
}
return glpstatus2string(status);
}
int glp_print_sol(glp_prob *P, const char *fname)
{ /* write basic solution in printable format */
glp_file *fp;
GLPROW *row;
GLPCOL *col;
int i, j, t, ae_ind, re_ind, ret;
double ae_max, re_max;
xprintf("Writing basic solution to '%s'...\n", fname);
fp = glp_open(fname, "w");
if (fp == NULL)
{ xprintf("Unable to create '%s' - %s\n", fname, get_err_msg());
ret = 1;
goto done;
}
xfprintf(fp, "%-12s%s\n", "Problem:",
P->name == NULL ? "" : P->name);
xfprintf(fp, "%-12s%d\n", "Rows:", P->m);
xfprintf(fp, "%-12s%d\n", "Columns:", P->n);
xfprintf(fp, "%-12s%d\n", "Non-zeros:", P->nnz);
t = glp_get_status(P);
xfprintf(fp, "%-12s%s\n", "Status:",
t == GLP_OPT ? "OPTIMAL" :
t == GLP_FEAS ? "FEASIBLE" :
t == GLP_INFEAS ? "INFEASIBLE (INTERMEDIATE)" :
t == GLP_NOFEAS ? "INFEASIBLE (FINAL)" :
t == GLP_UNBND ? "UNBOUNDED" :
t == GLP_UNDEF ? "UNDEFINED" : "???");
xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:",
P->obj == NULL ? "" : P->obj,
P->obj == NULL ? "" : " = ", P->obj_val,
P->dir == GLP_MIN ? "MINimum" :
P->dir == GLP_MAX ? "MAXimum" : "???");
xfprintf(fp, "\n");
xfprintf(fp, " No. Row name St Activity Lower bound "
" Upper bound Marginal\n");
xfprintf(fp, "------ ------------ -- ------------- ------------- "
"------------- -------------\n");
for (i = 1; i <= P->m; i++)
{ row = P->row[i];
xfprintf(fp, "%6d ", i);
if (row->name == NULL || strlen(row->name) <= 12)
xfprintf(fp, "%-12s ", row->name == NULL ? "" : row->name);
else
xfprintf(fp, "%s\n%20s", row->name, "");
xfprintf(fp, "%s ",
row->stat == GLP_BS ? "B " :
row->stat == GLP_NL ? "NL" :
row->stat == GLP_NU ? "NU" :
row->stat == GLP_NF ? "NF" :
row->stat == GLP_NS ? "NS" : "??");
xfprintf(fp, "%13.6g ",
fabs(row->prim) <= 1e-9 ? 0.0 : row->prim);
if (row->type == GLP_LO || row->type == GLP_DB ||
row->type == GLP_FX)
xfprintf(fp, "%13.6g ", row->lb);
else
xfprintf(fp, "%13s ", "");
if (row->type == GLP_UP || row->type == GLP_DB)
xfprintf(fp, "%13.6g ", row->ub);
else
xfprintf(fp, "%13s ", row->type == GLP_FX ? "=" : "");
if (row->stat != GLP_BS)
{ if (fabs(row->dual) <= 1e-9)
xfprintf(fp, "%13s", "< eps");
else
xfprintf(fp, "%13.6g ", row->dual);
}
xfprintf(fp, "\n");
}
xfprintf(fp, "\n");
xfprintf(fp, " No. Column name St Activity Lower bound "
" Upper bound Marginal\n");
xfprintf(fp, "------ ------------ -- ------------- ------------- "
"------------- -------------\n");
for (j = 1; j <= P->n; j++)
{ col = P->col[j];
xfprintf(fp, "%6d ", j);
if (col->name == NULL || strlen(col->name) <= 12)
xfprintf(fp, "%-12s ", col->name == NULL ? "" : col->name);
else
xfprintf(fp, "%s\n%20s", col->name, "");
xfprintf(fp, "%s ",
col->stat == GLP_BS ? "B " :
col->stat == GLP_NL ? "NL" :
col->stat == GLP_NU ? "NU" :
col->stat == GLP_NF ? "NF" :
col->stat == GLP_NS ? "NS" : "??");
xfprintf(fp, "%13.6g ",
fabs(col->prim) <= 1e-9 ? 0.0 : col->prim);
if (col->type == GLP_LO || col->type == GLP_DB ||
col->type == GLP_FX)
xfprintf(fp, "%13.6g ", col->lb);
else
xfprintf(fp, "%13s ", "");
if (col->type == GLP_UP || col->type == GLP_DB)
xfprintf(fp, "%13.6g ", col->ub);
else
xfprintf(fp, "%13s ", col->type == GLP_FX ? "=" : "");
if (col->stat != GLP_BS)
{ if (fabs(col->dual) <= 1e-9)
xfprintf(fp, "%13s", "< eps");
else
xfprintf(fp, "%13.6g ", col->dual);
}
xfprintf(fp, "\n");
}
xfprintf(fp, "\n");
xfprintf(fp, "Karush-Kuhn-Tucker optimality conditions:\n");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_SOL, GLP_KKT_PE, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.PE: max.abs.err = %.2e on row %d\n",
ae_max, ae_ind);
xfprintf(fp, " max.rel.err = %.2e on row %d\n",
re_max, re_ind);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS WRONG");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_SOL, GLP_KKT_PB, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.PB: max.abs.err = %.2e on %s %d\n",
ae_max, ae_ind <= P->m ? "row" : "column",
ae_ind <= P->m ? ae_ind : ae_ind - P->m);
xfprintf(fp, " max.rel.err = %.2e on %s %d\n",
re_max, re_ind <= P->m ? "row" : "column",
re_ind <= P->m ? re_ind : re_ind - P->m);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "PRIMAL SOLUTION IS INFEASIBL"
"E");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_SOL, GLP_KKT_DE, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.DE: max.abs.err = %.2e on column %d\n",
ae_max, ae_ind == 0 ? 0 : ae_ind - P->m);
xfprintf(fp, " max.rel.err = %.2e on column %d\n",
re_max, re_ind == 0 ? 0 : re_ind - P->m);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS WRONG");
xfprintf(fp, "\n");
glp_check_kkt(P, GLP_SOL, GLP_KKT_DB, &ae_max, &ae_ind, &re_max,
&re_ind);
xfprintf(fp, "KKT.DB: max.abs.err = %.2e on %s %d\n",
ae_max, ae_ind <= P->m ? "row" : "column",
ae_ind <= P->m ? ae_ind : ae_ind - P->m);
xfprintf(fp, " max.rel.err = %.2e on %s %d\n",
re_max, re_ind <= P->m ? "row" : "column",
re_ind <= P->m ? re_ind : re_ind - P->m);
xfprintf(fp, "%8s%s\n", "",
re_max <= 1e-9 ? "High quality" :
re_max <= 1e-6 ? "Medium quality" :
re_max <= 1e-3 ? "Low quality" : "DUAL SOLUTION IS INFEASIBLE")
;
xfprintf(fp, "\n");
xfprintf(fp, "End of output\n");
#if 0 /* FIXME */
xfflush(fp);
#endif
if (glp_ioerr(fp))
{ xprintf("Write error on '%s' - %s\n", fname, get_err_msg());
ret = 1;
goto done;
}
ret = 0;
done:
if (fp != NULL) glp_close(fp);
return ret;
}
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;
}
int c_simplex_sparse(int m, int n, DMAT(c), DMAT(b), DVEC(s)) {
glp_prob *lp;
lp = glp_create_prob();
glp_set_obj_dir(lp, GLP_MAX);
int i,j,k;
int tot = cr - n;
glp_add_rows(lp, m);
glp_add_cols(lp, n);
//printf("%d %d\n",m,n);
// the first n values
for (k=1;k<=n;k++) {
glp_set_obj_coef(lp, k, AT(c, k-1, 2));
//printf("%d %f\n",k,AT(c, k-1, 2));
}
int * ia = malloc((1+tot)*sizeof(int));
int * ja = malloc((1+tot)*sizeof(int));
double * ar = malloc((1+tot)*sizeof(double));
for (k=1; k<= tot; k++) {
ia[k] = rint(AT(c,k-1+n,0));
ja[k] = rint(AT(c,k-1+n,1));
ar[k] = AT(c,k-1+n,2);
//printf("%d %d %f\n",ia[k],ja[k],ar[k]);
}
glp_load_matrix(lp, tot, ia, ja, ar);
int t;
for (i=1;i<=m;i++) {
switch((int)rint(AT(b,i-1,0))) {
case 0: { t = GLP_FR; break; }
case 1: { t = GLP_LO; break; }
case 2: { t = GLP_UP; break; }
case 3: { t = GLP_DB; break; }
default: { t = GLP_FX; break; }
}
glp_set_row_bnds(lp, i, t , AT(b,i-1,1), AT(b,i-1,2));
}
for (j=1;j<=n;j++) {
switch((int)rint(AT(b,m+j-1,0))) {
case 0: { t = GLP_FR; break; }
case 1: { t = GLP_LO; break; }
case 2: { t = GLP_UP; break; }
case 3: { t = GLP_DB; break; }
default: { t = GLP_FX; break; }
}
glp_set_col_bnds(lp, j, t , AT(b,m+j-1,1), AT(b,m+j-1,2));
}
glp_term_out(0);
glp_simplex(lp, NULL);
sp[0] = glp_get_status(lp);
sp[1] = glp_get_obj_val(lp);
for (k=1; k<=n; k++) {
sp[k+1] = glp_get_col_prim(lp, k);
}
glp_delete_prob(lp);
free(ia);
free(ja);
free(ar);
return 0;
}
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;
}
OptSolutionData* GLPKRunSolver(int ProbType) {
OptSolutionData* NewSolution = NULL;
int NumVariables = glp_get_num_cols(GLPKModel);
int Status = 0;
if (ProbType == MILP) {
Status = glp_simplex(GLPKModel, NULL); // Use default settings
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
Status = glp_intopt(GLPKModel, NULL); // Use default settings
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
NewSolution = new OptSolutionData;
Status = glp_mip_status(GLPKModel);
if (Status == GLP_UNDEF || Status == GLP_NOFEAS) {
NewSolution->Status = INFEASIBLE;
return NewSolution;
} else if (Status == GLP_FEAS) {
NewSolution->Status = UNBOUNDED;
return NewSolution;
} else if (Status == GLP_OPT) {
NewSolution->Status = SUCCESS;
} else {
delete NewSolution;
FErrorFile() << "Problem status unrecognized." << endl;
FlushErrorFile();
return NULL;
}
NewSolution->Objective = glp_mip_obj_val(GLPKModel);
NewSolution->SolutionData.resize(NumVariables);
for (int i=0; i < NumVariables; i++) {
NewSolution->SolutionData[i] = glp_mip_col_val(GLPKModel, i+1);
}
} else if (ProbType == LP) {
//First we check the basis matrix to ensure it is not singular
if (glp_warm_up(GLPKModel) != 0) {
glp_adv_basis(GLPKModel, 0);
}
Status = glp_simplex(GLPKModel, NULL); // Use default settings
if (Status == GLP_EBADB) { /* the basis is invalid; build some valid basis */
glp_adv_basis(GLPKModel, 0);
Status = glp_simplex(GLPKModel, NULL); // Use default settings
}
if (Status != 0) {
FErrorFile() << "Failed to optimize problem." << endl;
FlushErrorFile();
return NULL;
}
NewSolution = new OptSolutionData;
Status = glp_get_status(GLPKModel);
if (Status == GLP_INFEAS || Status == GLP_NOFEAS || Status == GLP_UNDEF) {
cout << "Model is infeasible" << endl;
FErrorFile() << "Model is infeasible" << endl;
FlushErrorFile();
NewSolution->Status = INFEASIBLE;
return NewSolution;
} else if (Status == GLP_FEAS || Status == GLP_UNBND) {
cout << "Model is unbounded" << endl;
FErrorFile() << "Model is unbounded" << endl;
FlushErrorFile();
NewSolution->Status = UNBOUNDED;
return NewSolution;
} else if (Status == GLP_OPT) {
NewSolution->Status = SUCCESS;
} else {
delete NewSolution;
FErrorFile() << "Problem status unrecognized." << endl;
FlushErrorFile();
return NULL;
}
NewSolution->Objective = glp_get_obj_val(GLPKModel);
NewSolution->SolutionData.resize(NumVariables);
for (int i=0; i < NumVariables; i++) {
NewSolution->SolutionData[i] = glp_get_col_prim(GLPKModel, i+1);
}
} else {
FErrorFile() << "Optimization problem type cannot be handled by GLPK solver." << endl;
FlushErrorFile();
return NULL;
}
return NewSolution;
}
static PyObject* LPX_write(LPXObject *self, PyObject *args, PyObject *keywds)
{
static char* kwlist[] = {"mps", "freemps", "prob", "sol", "sens_bnds",
"ips", "mip", NULL};
char* fnames[] = {NULL,NULL,NULL,NULL,NULL,NULL,NULL};
char* fname;
const char* err_msg = "writer for '%s' failed to write to '%s'";
int rv;
rv = PyArg_ParseTupleAndKeywords(args, keywds, "|sssssss", kwlist,
fnames,fnames+1,fnames+2,fnames+3, fnames+4,fnames+5,fnames+6);
if (!rv)
return NULL;
fname = fnames[0];
if (fname != NULL) {
rv = glp_write_mps(LP, GLP_MPS_DECK, NULL, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[0], fname);
return NULL;
}
}
fname = fnames[1];
if (fname != NULL) {
rv = glp_write_mps(LP, GLP_MPS_FILE, NULL, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[1], fname);
return NULL;
}
}
fname = fnames[2];
if (fname != NULL) {
rv = glp_write_lp(LP, NULL, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[2], fname);
return NULL;
}
}
fname = fnames[3];
if (fname != NULL) {
rv = glp_print_sol(LP, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[3], fname);
return NULL;
}
}
fname = fnames[4];
if (fname != NULL) {
if (glp_get_status(LP) == GLP_OPT && !glp_bf_exists(LP))
glp_factorize(LP);
rv = glp_print_ranges(LP, 0, NULL, 0, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[4], fname);
return NULL;
}
}
fname = fnames[5];
if (fname != NULL) {
rv = glp_print_ipt(LP, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[5], fname);
return NULL;
}
}
fname = fnames[6];
if (fname != NULL) {
glp_print_mip(LP, fname);
if (rv != 0) {
PyErr_Format(PyExc_RuntimeError, err_msg, kwlist[6], fname);
return NULL;
}
}
Py_RETURN_NONE;
}
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;
}
}
}
int DBWorker::_RememberRun(CMyProblem &P, int idobjectives, const char* modelfile, double time, bool mip, int idruns)
{
int idrun = -1;
try {
sql::PreparedStatement *PrepStmt;
sql::ResultSet *res;
PrepStmt = con->prepareStatement(
"INSERT INTO runs(idobjectives,modelfile,runtype,res_status,res_value,time_in_seconds,idruns) VALUES(?,?,?,?,?,?,?)"
);
PrepStmt->setInt(1, idobjectives);
PrepStmt->setString(2, modelfile);
PrepStmt->setString(3, mip ? "mip" : "lp");
if(!mip)
{
PrepStmt->setInt(4, glp_get_status(P.GetProblem()));
PrepStmt->setDouble(5, glp_get_obj_val(P.GetProblem()));
PrepStmt->setNull(7,0);
}
else
{
PrepStmt->setInt(4, glp_mip_status(P.GetProblem()));
PrepStmt->setDouble(5, glp_mip_obj_val(P.GetProblem()));
PrepStmt->setInt(7,idruns);
}
PrepStmt->setDouble(6, time);
PrepStmt->execute();
delete PrepStmt;
if(!mip)
{
PrepStmt = con->prepareStatement(
"SELECT LAST_INSERT_ID()"
);
res = PrepStmt->executeQuery();
delete PrepStmt;
res->next();
idrun = res->getInt(1);
delete res;
}
else
{
idrun = idruns;
}
cout << "Run ID " << idrun << endl;
PrepStmt = con->prepareStatement(
"INSERT INTO results(idrun,var_name,i,j,value,runtype) VALUES(?,?,?,?,?,?)"
);
PrepStmt->setInt(1, idrun);
PrepStmt->setString(6, mip ? "mip" : "lp");
vector<vector<double>> arr;
for (int vvv=1; vvv>=0; vvv--)
{
char* var_name = (vvv ? "x" : "y");
GetSolArray(P.GetProblem(),var_name,arr,mip);
PrepStmt->setString(2, var_name);
int i=1;
for (std::vector<vector<double>>::iterator it = arr.begin() ; it != arr.end(); ++it)
{
int j=1;
for (std::vector<double>::iterator it2 = (*it).begin() ; it2 != (*it).end(); ++it2)
{
PrepStmt->setInt(3, i);
PrepStmt->setInt(4, j);
PrepStmt->setDouble(5, *it2);
PrepStmt->execute();
j++;
}
i++;
}
}
delete PrepStmt;
}
catch (sql::SQLException &e) {
SQLError(e);
}
return idrun;
}
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 lpx_print_sens_bnds(LPX *lp, const char *fname)
{ /* write bounds sensitivity information */
if (glp_get_status(lp) == GLP_OPT && !glp_bf_exists(lp))
glp_factorize(lp);
return glp_print_ranges(lp, 0, NULL, 0, fname);
}
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;
}
static int branch_drtom(glp_tree *T, int *_next)
{ glp_prob *mip = T->mip;
int m = mip->m;
int n = mip->n;
char *non_int = T->non_int;
int j, jj, k, t, next, kase, len, stat, *ind;
double x, dk, alfa, delta_j, delta_k, delta_z, dz_dn, dz_up,
dd_dn, dd_up, degrad, *val;
/* basic solution of LP relaxation must be optimal */
xassert(glp_get_status(mip) == GLP_OPT);
/* allocate working arrays */
ind = xcalloc(1+n, sizeof(int));
val = xcalloc(1+n, sizeof(double));
/* nothing has been chosen so far */
jj = 0, degrad = -1.0;
/* walk through the list of columns (structural variables) */
for (j = 1; j <= n; j++)
{ /* if j-th column is not marked as fractional, skip it */
if (!non_int[j]) continue;
/* obtain (fractional) value of j-th column in basic solution
of LP relaxation */
x = glp_get_col_prim(mip, j);
/* since the value of j-th column is fractional, the column is
basic; compute corresponding row of the simplex table */
len = glp_eval_tab_row(mip, m+j, ind, val);
/* the following fragment computes a change in the objective
function: delta Z = new Z - old Z, where old Z is the
objective value in the current optimal basis, and new Z is
the objective value in the adjacent basis, for two cases:
1) if new upper bound ub' = floor(x[j]) is introduced for
j-th column (down branch);
2) if new lower bound lb' = ceil(x[j]) is introduced for
j-th column (up branch);
since in both cases the solution remaining dual feasible
becomes primal infeasible, one implicit simplex iteration
is performed to determine the change delta Z;
it is obvious that new Z, which is never better than old Z,
is a lower (minimization) or upper (maximization) bound of
the objective function for down- and up-branches. */
for (kase = -1; kase <= +1; kase += 2)
{ /* if kase < 0, the new upper bound of x[j] is introduced;
in this case x[j] should decrease in order to leave the
basis and go to its new upper bound */
/* if kase > 0, the new lower bound of x[j] is introduced;
in this case x[j] should increase in order to leave the
basis and go to its new lower bound */
/* apply the dual ratio test in order to determine which
auxiliary or structural variable should enter the basis
to keep dual feasibility */
k = glp_dual_rtest(mip, len, ind, val, kase, 1e-9);
if (k != 0) k = ind[k];
/* if no non-basic variable has been chosen, LP relaxation
of corresponding branch being primal infeasible and dual
unbounded has no primal feasible solution; in this case
the change delta Z is formally set to infinity */
if (k == 0)
{ delta_z =
(T->mip->dir == GLP_MIN ? +DBL_MAX : -DBL_MAX);
goto skip;
}
/* row of the simplex table that corresponds to non-basic
variable x[k] choosen by the dual ratio test is:
x[j] = ... + alfa * x[k] + ...
where alfa is the influence coefficient (an element of
the simplex table row) */
/* determine the coefficient alfa */
for (t = 1; t <= len; t++) if (ind[t] == k) break;
xassert(1 <= t && t <= len);
alfa = val[t];
/* since in the adjacent basis the variable x[j] becomes
non-basic, knowing its value in the current basis we can
determine its change delta x[j] = new x[j] - old x[j] */
delta_j = (kase < 0 ? floor(x) : ceil(x)) - x;
/* and knowing the coefficient alfa we can determine the
corresponding change delta x[k] = new x[k] - old x[k],
where old x[k] is a value of x[k] in the current basis,
and new x[k] is a value of x[k] in the adjacent basis */
delta_k = delta_j / alfa;
/* Tomlin noticed that if the variable x[k] is of integer
kind, its change cannot be less (eventually) than one in
the magnitude */
if (k > m && glp_get_col_kind(mip, k-m) != GLP_CV)
{ /* x[k] is structural integer variable */
if (fabs(delta_k - floor(delta_k + 0.5)) > 1e-3)
{ if (delta_k > 0.0)
delta_k = ceil(delta_k); /* +3.14 -> +4 */
else
delta_k = floor(delta_k); /* -3.14 -> -4 */
}
}
/* now determine the status and reduced cost of x[k] in the
current basis */
if (k <= m)
{ stat = glp_get_row_stat(mip, k);
dk = glp_get_row_dual(mip, k);
}
else
{ stat = glp_get_col_stat(mip, k-m);
dk = glp_get_col_dual(mip, k-m);
}
/* if the current basis is dual degenerate, some reduced
costs which are close to zero may have wrong sign due to
round-off errors, so correct the sign of d[k] */
switch (T->mip->dir)
{ case GLP_MIN:
if (stat == GLP_NL && dk < 0.0 ||
stat == GLP_NU && dk > 0.0 ||
stat == GLP_NF) dk = 0.0;
break;
case GLP_MAX:
if (stat == GLP_NL && dk > 0.0 ||
stat == GLP_NU && dk < 0.0 ||
stat == GLP_NF) dk = 0.0;
break;
default:
xassert(T != T);
}
/* now knowing the change of x[k] and its reduced cost d[k]
we can compute the corresponding change in the objective
function delta Z = new Z - old Z = d[k] * delta x[k];
note that due to Tomlin's modification new Z can be even
worse than in the adjacent basis */
delta_z = dk * delta_k;
skip: /* new Z is never better than old Z, therefore the change
delta Z is always non-negative (in case of minimization)
or non-positive (in case of maximization) */
switch (T->mip->dir)
{ case GLP_MIN: xassert(delta_z >= 0.0); break;
case GLP_MAX: xassert(delta_z <= 0.0); break;
default: xassert(T != T);
}
/* save the change in the objective fnction for down- and
up-branches, respectively */
if (kase < 0) dz_dn = delta_z; else dz_up = delta_z;
}
/* thus, in down-branch no integer feasible solution can be
better than Z + dz_dn, and in up-branch no integer feasible
solution can be better than Z + dz_up, where Z is value of
the objective function in the current basis */
/* following the heuristic by Driebeck and Tomlin we choose a
column (i.e. structural variable) which provides largest
degradation of the objective function in some of branches;
besides, we select the branch with smaller degradation to
be solved next and keep other branch with larger degradation
in the active list hoping to minimize the number of further
backtrackings */
if (degrad < fabs(dz_dn) || degrad < fabs(dz_up))
{ jj = j;
if (fabs(dz_dn) < fabs(dz_up))
{ /* select down branch to be solved next */
next = GLP_DN_BRNCH;
degrad = fabs(dz_up);
}
else
{ /* select up branch to be solved next */
next = GLP_UP_BRNCH;
degrad = fabs(dz_dn);
}
/* save the objective changes for printing */
dd_dn = dz_dn, dd_up = dz_up;
/* if down- or up-branch has no feasible solution, we does
not need to consider other candidates (in principle, the
corresponding branch could be pruned right now) */
if (degrad == DBL_MAX) break;
}
}
/* free working arrays */
xfree(ind);
xfree(val);
/* something must be chosen */
xassert(1 <= jj && jj <= n);
#if 1 /* 02/XI-2009 */
if (degrad < 1e-6 * (1.0 + 0.001 * fabs(mip->obj_val)))
{ jj = branch_mostf(T, &next);
goto done;
}
#endif
if (T->parm->msg_lev >= GLP_MSG_DBG)
{ xprintf("branch_drtom: column %d chosen to branch on\n", jj);
if (fabs(dd_dn) == DBL_MAX)
xprintf("branch_drtom: down-branch is infeasible\n");
else
xprintf("branch_drtom: down-branch bound is %.9e\n",
lpx_get_obj_val(mip) + dd_dn);
if (fabs(dd_up) == DBL_MAX)
xprintf("branch_drtom: up-branch is infeasible\n");
else
xprintf("branch_drtom: up-branch bound is %.9e\n",
lpx_get_obj_val(mip) + dd_up);
}
done: *_next = next;
return jj;
}
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;
}
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 glp_print_ranges(glp_prob *P, int len, const int list[],
int flags, const char *fname)
{ /* print sensitivity analysis report */
glp_file *fp = NULL;
GLPROW *row;
GLPCOL *col;
int m, n, pass, k, t, numb, type, stat, var1, var2, count, page,
ret;
double lb, ub, slack, coef, prim, dual, value1, value2, coef1,
coef2, obj1, obj2;
const char *name, *limit;
char buf[13+1];
/* sanity checks */
if (P == NULL || P->magic != GLP_PROB_MAGIC)
xerror("glp_print_ranges: P = %p; invalid problem object\n",
P);
m = P->m, n = P->n;
if (len < 0)
xerror("glp_print_ranges: len = %d; invalid list length\n",
len);
if (len > 0)
{ if (list == NULL)
xerror("glp_print_ranges: list = %p: invalid parameter\n",
list);
for (t = 1; t <= len; t++)
{ k = list[t];
if (!(1 <= k && k <= m+n))
xerror("glp_print_ranges: list[%d] = %d; row/column numb"
"er out of range\n", t, k);
}
}
if (flags != 0)
xerror("glp_print_ranges: flags = %d; invalid parameter\n",
flags);
if (fname == NULL)
xerror("glp_print_ranges: fname = %p; invalid parameter\n",
fname);
if (glp_get_status(P) != GLP_OPT)
{ xprintf("glp_print_ranges: optimal basic solution required\n");
ret = 1;
goto done;
}
if (!glp_bf_exists(P))
{ xprintf("glp_print_ranges: basis factorization required\n");
ret = 2;
goto done;
}
/* start reporting */
xprintf("Write sensitivity analysis report to '%s'...\n", fname);
fp = glp_open(fname, "w");
if (fp == NULL)
{ xprintf("Unable to create '%s' - %s\n", fname, get_err_msg());
ret = 3;
goto done;
}
page = count = 0;
for (pass = 1; pass <= 2; pass++)
for (t = 1; t <= (len == 0 ? m+n : len); t++)
{ if (t == 1) count = 0;
k = (len == 0 ? t : list[t]);
if (pass == 1 && k > m || pass == 2 && k <= m)
continue;
if (count == 0)
{ xfprintf(fp, "GLPK %-4s - SENSITIVITY ANALYSIS REPORT%73sPa"
"ge%4d\n", glp_version(), "", ++page);
xfprintf(fp, "\n");
xfprintf(fp, "%-12s%s\n", "Problem:",
P->name == NULL ? "" : P->name);
xfprintf(fp, "%-12s%s%s%.10g (%s)\n", "Objective:",
P->obj == NULL ? "" : P->obj,
P->obj == NULL ? "" : " = ", P->obj_val,
P->dir == GLP_MIN ? "MINimum" :
P->dir == GLP_MAX ? "MAXimum" : "???");
xfprintf(fp, "\n");
xfprintf(fp, "%6s %-12s %2s %13s %13s %13s %13s %13s %13s "
"%s\n", "No.", pass == 1 ? "Row name" : "Column name",
"St", "Activity", pass == 1 ? "Slack" : "Obj coef",
"Lower bound", "Activity", "Obj coef", "Obj value at",
"Limiting");
xfprintf(fp, "%6s %-12s %2s %13s %13s %13s %13s %13s %13s "
"%s\n", "", "", "", "", "Marginal", "Upper bound",
"range", "range", "break point", "variable");
xfprintf(fp, "------ ------------ -- ------------- --------"
"----- ------------- ------------- ------------- ------"
"------- ------------\n");
}
if (pass == 1)
{ numb = k;
xassert(1 <= numb && numb <= m);
row = P->row[numb];
name = row->name;
type = row->type;
lb = glp_get_row_lb(P, numb);
ub = glp_get_row_ub(P, numb);
coef = 0.0;
stat = row->stat;
prim = row->prim;
if (type == GLP_FR)
slack = - prim;
else if (type == GLP_LO)
slack = lb - prim;
else if (type == GLP_UP || type == GLP_DB || type == GLP_FX)
slack = ub - prim;
dual = row->dual;
}
else
{ numb = k - m;
xassert(1 <= numb && numb <= n);
col = P->col[numb];
name = col->name;
lb = glp_get_col_lb(P, numb);
ub = glp_get_col_ub(P, numb);
coef = col->coef;
stat = col->stat;
prim = col->prim;
slack = 0.0;
dual = col->dual;
}
if (stat != GLP_BS)
{ glp_analyze_bound(P, k, &value1, &var1, &value2, &var2);
if (stat == GLP_NF)
coef1 = coef2 = coef;
else if (stat == GLP_NS)
coef1 = -DBL_MAX, coef2 = +DBL_MAX;
else if (stat == GLP_NL && P->dir == GLP_MIN ||
stat == GLP_NU && P->dir == GLP_MAX)
coef1 = coef - dual, coef2 = +DBL_MAX;
else
coef1 = -DBL_MAX, coef2 = coef - dual;
if (value1 == -DBL_MAX)
{ if (dual < -1e-9)
obj1 = +DBL_MAX;
else if (dual > +1e-9)
obj1 = -DBL_MAX;
else
obj1 = P->obj_val;
}
else
obj1 = P->obj_val + dual * (value1 - prim);
if (value2 == +DBL_MAX)
{ if (dual < -1e-9)
obj2 = -DBL_MAX;
else if (dual > +1e-9)
obj2 = +DBL_MAX;
else
obj2 = P->obj_val;
}
else
obj2 = P->obj_val + dual * (value2 - prim);
}
else
{ glp_analyze_coef(P, k, &coef1, &var1, &value1, &coef2,
&var2, &value2);
if (coef1 == -DBL_MAX)
{ if (prim < -1e-9)
obj1 = +DBL_MAX;
else if (prim > +1e-9)
obj1 = -DBL_MAX;
else
obj1 = P->obj_val;
}
else
obj1 = P->obj_val + (coef1 - coef) * prim;
if (coef2 == +DBL_MAX)
{ if (prim < -1e-9)
obj2 = -DBL_MAX;
else if (prim > +1e-9)
obj2 = +DBL_MAX;
else
obj2 = P->obj_val;
}
else
obj2 = P->obj_val + (coef2 - coef) * prim;
}
/*** first line ***/
/* row/column number */
xfprintf(fp, "%6d", numb);
/* row/column name */
xfprintf(fp, " %-12.12s", name == NULL ? "" : name);
if (name != NULL && strlen(name) > 12)
xfprintf(fp, "%s\n%6s %12s", name+12, "", "");
/* row/column status */
xfprintf(fp, " %2s",
stat == GLP_BS ? "BS" : stat == GLP_NL ? "NL" :
stat == GLP_NU ? "NU" : stat == GLP_NF ? "NF" :
stat == GLP_NS ? "NS" : "??");
/* row/column activity */
xfprintf(fp, " %s", format(buf, prim));
/* row slack, column objective coefficient */
xfprintf(fp, " %s", format(buf, k <= m ? slack : coef));
/* row/column lower bound */
xfprintf(fp, " %s", format(buf, lb));
/* row/column activity range */
xfprintf(fp, " %s", format(buf, value1));
/* row/column objective coefficient range */
xfprintf(fp, " %s", format(buf, coef1));
/* objective value at break point */
xfprintf(fp, " %s", format(buf, obj1));
/* limiting variable name */
if (var1 != 0)
{ if (var1 <= m)
limit = glp_get_row_name(P, var1);
else
limit = glp_get_col_name(P, var1 - m);
if (limit != NULL)
xfprintf(fp, " %s", limit);
}
xfprintf(fp, "\n");
/*** second line ***/
xfprintf(fp, "%6s %-12s %2s %13s", "", "", "", "");
/* row/column reduced cost */
xfprintf(fp, " %s", format(buf, dual));
/* row/column upper bound */
xfprintf(fp, " %s", format(buf, ub));
/* row/column activity range */
xfprintf(fp, " %s", format(buf, value2));
/* row/column objective coefficient range */
xfprintf(fp, " %s", format(buf, coef2));
/* objective value at break point */
xfprintf(fp, " %s", format(buf, obj2));
/* limiting variable name */
if (var2 != 0)
{ if (var2 <= m)
limit = glp_get_row_name(P, var2);
else
limit = glp_get_col_name(P, var2 - m);
if (limit != NULL)
xfprintf(fp, " %s", limit);
}
xfprintf(fp, "\n");
xfprintf(fp, "\n");
/* print 10 items per page */
count = (count + 1) % 10;
}
xfprintf(fp, "End of report\n");
#if 0 /* FIXME */
xfflush(fp);
#endif
if (glp_ioerr(fp))
{ xprintf("Write error on '%s' - %s\n", fname, get_err_msg());
ret = 4;
goto done;
}
ret = 0;
done:
if (fp != NULL) glp_close(fp);
return ret;
}
/**
* Solves the LP problem
*
* @param mlp the MLP Handle
* @param s_ctx context to return results
* @return GNUNET_OK if could be solved, GNUNET_SYSERR on failure
*/
static int
mlp_solve_lp_problem (struct GAS_MLP_Handle *mlp, struct GAS_MLP_SolutionContext *s_ctx)
{
int res;
struct GNUNET_TIME_Relative duration;
struct GNUNET_TIME_Absolute end;
struct GNUNET_TIME_Absolute start = GNUNET_TIME_absolute_get();
/* LP presolver?
* Presolver is required if the problem was modified and an existing
* valid basis is now invalid */
if (mlp->presolver_required == GNUNET_YES)
mlp->control_param_lp.presolve = GLP_ON;
else
mlp->control_param_lp.presolve = GLP_OFF;
/* Solve LP problem to have initial valid solution */
lp_solv:
res = glp_simplex(mlp->prob, &mlp->control_param_lp);
if (res == 0)
{
/* The LP problem instance has been successfully solved. */
}
else if (res == GLP_EITLIM)
{
/* simplex iteration limit has been exceeded. */
// TODO Increase iteration limit?
}
else if (res == GLP_ETMLIM)
{
/* Time limit has been exceeded. */
// TODO Increase time limit?
}
else
{
/* Problem was ill-defined, retry with presolver */
if (mlp->presolver_required == GNUNET_NO)
{
mlp->presolver_required = GNUNET_YES;
goto lp_solv;
}
else
{
/* Problem was ill-defined, no way to handle that */
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving LP problem failed: %i %s\n", res, mlp_solve_to_string(res));
return GNUNET_SYSERR;
}
}
end = GNUNET_TIME_absolute_get ();
duration = GNUNET_TIME_absolute_get_difference (start, end);
mlp->lp_solved++;
mlp->lp_total_duration =+ duration.rel_value;
s_ctx->lp_duration = duration;
GNUNET_STATISTICS_update (mlp->stats,"# LP problem solved", 1, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# LP execution time (ms)", duration.rel_value, GNUNET_NO);
GNUNET_STATISTICS_set (mlp->stats,"# LP execution time average (ms)",
mlp->lp_total_duration / mlp->lp_solved, GNUNET_NO);
/* Analyze problem status */
res = glp_get_status (mlp->prob);
switch (res) {
/* solution is optimal */
case GLP_OPT:
/* solution is feasible */
case GLP_FEAS:
break;
/* Problem was ill-defined, no way to handle that */
default:
GNUNET_log_from (GNUNET_ERROR_TYPE_DEBUG,
"ats-mlp",
"Solving LP problem failed, no solution: %s\n", mlp_status_to_string(res));
return GNUNET_SYSERR;
break;
}
/* solved sucessfully, no presolver required next time */
mlp->presolver_required = GNUNET_NO;
return GNUNET_OK;
}
static PyObject* LPX_solver_integer(LPXObject *self, PyObject *args,
PyObject *keywds) {
#if GLPK_VERSION(4, 20)
PyObject *callback=NULL;
struct mip_callback_object*info=NULL;
glp_iocp cp;
glp_init_iocp(&cp);
cp.msg_lev = GLP_MSG_OFF;
// Map the keyword arguments to the appropriate entries.
static char *kwlist[] =
{"msg_lev", "br_tech", "bt_tech",
#if GLPK_VERSION(4, 21)
"pp_tech",
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
"gmi_cuts",
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
"mir_cuts",
"mip_gap",
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
"cov_cuts", "clq_cuts", "presolve", "binarize",
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
"fp_heur",
#endif // GLPK_VERSION(4, 37)
"tol_int", "tol_obj", "tm_lim", "out_frq", "out_dly",
"callback", //"cb_info", "cb_size",
NULL};
if (!PyArg_ParseTupleAndKeywords
(args, keywds, "|iii"
#if GLPK_VERSION(4, 21)
"i"
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
"i"
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
"ii"
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
"iiii"
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
"i"
#endif // GLPK_VERSION(4, 37)
"ddiiiO", kwlist, &cp.msg_lev, &cp.br_tech, &cp.bt_tech,
#if GLPK_VERSION(4, 21)
&cp.pp_tech,
#endif // GLPK_VERSION(4, 21)
#if GLPK_VERSION(4, 24)
&cp.gmi_cuts,
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
&cp.mir_cuts,
&cp.mip_gap,
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
&cp.cov_cuts, &cp.clq_cuts, &cp.presolve, &cp.binarize,
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
&cp.fp_heur,
#endif // GLPK_VERSION(4, 37)
&cp.tol_int, &cp.tol_obj, &cp.tm_lim, &cp.out_frq, &cp.out_dly,
&callback)) {
return NULL;
}
#if GLPK_VERSION(4, 24)
cp.gmi_cuts = cp.gmi_cuts ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 24)
#if GLPK_VERSION(4, 23)
cp.mir_cuts = cp.mir_cuts ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 23)
#if GLPK_VERSION(4, 32)
cp.cov_cuts = cp.cov_cuts ? GLP_ON : GLP_OFF;
cp.clq_cuts = cp.clq_cuts ? GLP_ON : GLP_OFF;
cp.presolve = cp.presolve ? GLP_ON : GLP_OFF;
cp.binarize = cp.binarize ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 32)
#if GLPK_VERSION(4, 37)
cp.fp_heur = cp.fp_heur ? GLP_ON : GLP_OFF;
#endif // GLPK_VERSION(4, 32)
// Do checking on the various entries.
#if GLPK_VERSION(4, 32)
if (!cp.presolve && glp_get_status(LP) != GLP_OPT) {
PyErr_SetString(PyExc_RuntimeError, "integer solver requires "
"use of presolver or existing optimal basic solution");
return NULL;
}
#else
if (glp_get_status(LP) != GLP_OPT) {
PyErr_SetString(PyExc_RuntimeError, "integer solver requires "
"existing optimal basic solution");
return NULL;
}
#endif
switch (cp.msg_lev) {
case GLP_MSG_OFF: case GLP_MSG_ERR: case GLP_MSG_ON: case GLP_MSG_ALL: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for msg_lev (LPX.MSG_* are valid values)");
return NULL;
}
switch (cp.br_tech) {
case GLP_BR_FFV: case GLP_BR_LFV: case GLP_BR_MFV: case GLP_BR_DTH: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for br_tech (LPX.BR_* are valid values)");
return NULL;
}
switch (cp.bt_tech) {
case GLP_BT_DFS: case GLP_BT_BFS: case GLP_BT_BLB: case GLP_BT_BPH: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for bt_tech (LPX.BT_* are valid values)");
return NULL;
}
#if GLPK_VERSION(4, 21)
switch (cp.pp_tech) {
case GLP_PP_NONE: case GLP_PP_ROOT: case GLP_PP_ALL: break;
default:
PyErr_SetString
(PyExc_ValueError,
"invalid value for pp_tech (LPX.PP_* are valid values)");
return NULL;
}
#endif // GLPK_VERSION(4, 21)
if (cp.tol_int<=0 || cp.tol_int>=1) {
PyErr_SetString(PyExc_ValueError, "tol_int must obey 0<tol_int<1");
return NULL;
}
if (cp.tol_obj<=0 || cp.tol_obj>=1) {
PyErr_SetString(PyExc_ValueError, "tol_obj must obey 0<tol_obj<1");
return NULL;
}
if (cp.tm_lim<0) {
PyErr_SetString(PyExc_ValueError, "tm_lim must be non-negative");
return NULL;
}
if (cp.out_frq<=0) {
PyErr_SetString(PyExc_ValueError, "out_frq must be positive");
return NULL;
}
if (cp.out_dly<0) {
PyErr_SetString(PyExc_ValueError, "out_dly must be non-negative");
return NULL;
}
#if GLPK_VERSION(4, 23)
if (cp.mip_gap<0) {
PyErr_SetString(PyExc_ValueError, "mip_gap must be non-negative");
return NULL;
}
#endif
int retval;
if (callback != NULL && callback != Py_None) {
info = (struct mip_callback_object*)
malloc(sizeof(struct mip_callback_object));
info->callback = callback;
info->py_lp = self;
cp.cb_info = info;
cp.cb_func = mip_callback;
}
retval = glp_intopt(LP, &cp);
if (info) free(info);
if (PyErr_Occurred()) {
// This should happen only if there was a problem within the
// callback function, or if the callback was not appropriate.
return NULL;
}
if (retval!=GLP_EBADB && retval!=GLP_ESING && retval!=GLP_ECOND
&& retval!=GLP_EBOUND && retval!=GLP_EFAIL)
self->last_solver = 2;
return glpsolver_retval_to_message(retval);
#else
int retval = lpx_integer(LP);
if (retval!=LPX_E_FAULT) self->last_solver = 2;
return solver_retval_to_message(retval);
#endif // GLPK_VERSION(4, 20)
}
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;
}
int glpk (int sense, int n, int m, double *c, int nz, int *rn, int *cn,
double *a, double *b, char *ctype, int *freeLB, double *lb,
int *freeUB, double *ub, int *vartype, int isMIP, int lpsolver,
int save_pb, char *save_filename, char *filetype,
double *xmin, double *fmin, double *status,
double *lambda, double *redcosts, double *time, double *mem)
{
int typx = 0;
int method;
clock_t t_start = clock();
//Redirect standard output
if (glpIntParam[0] > 1) glp_term_hook (glpk_print_hook, NULL);
else glp_term_hook (NULL, NULL);
//-- Create an empty LP/MILP object
LPX *lp = lpx_create_prob ();
//-- Set the sense of optimization
if (sense == 1)
glp_set_obj_dir (lp, GLP_MIN);
else
glp_set_obj_dir (lp, GLP_MAX);
//-- Define the number of unknowns and their domains.
glp_add_cols (lp, n);
for (int i = 0; i < n; i++)
{
//-- Define type of the structural variables
if (! freeLB[i] && ! freeUB[i]) {
if ( lb[i] == ub[i] )
glp_set_col_bnds (lp, i+1, GLP_FX, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_DB, lb[i], ub[i]);
}
else
{
if (! freeLB[i] && freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_LO, lb[i], ub[i]);
else
{
if (freeLB[i] && ! freeUB[i])
glp_set_col_bnds (lp, i+1, GLP_UP, lb[i], ub[i]);
else
glp_set_col_bnds (lp, i+1, GLP_FR, lb[i], ub[i]);
}
}
// -- Set the objective coefficient of the corresponding
// -- structural variable. No constant term is assumed.
glp_set_obj_coef(lp,i+1,c[i]);
if (isMIP)
glp_set_col_kind (lp, i+1, vartype[i]);
}
glp_add_rows (lp, m);
for (int i = 0; i < m; i++)
{
/* If the i-th row has no lower bound (types F,U), the
corrispondent parameter will be ignored.
If the i-th row has no upper bound (types F,L), the corrispondent
parameter will be ignored.
If the i-th row is of S type, the i-th LB is used, but
the i-th UB is ignored.
*/
switch (ctype[i])
{
case 'F': typx = GLP_FR; break;
// upper bound
case 'U': typx = GLP_UP; break;
// lower bound
case 'L': typx = GLP_LO; break;
// fixed constraint
case 'S': typx = GLP_FX; break;
// double-bounded variable
case 'D': typx = GLP_DB; break;
}
if ( typx == GLP_DB && -b[i] < b[i]) {
glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
}
else if(typx == GLP_DB && -b[i] == b[i]) {
glp_set_row_bnds (lp, i+1, GLP_FX, b[i], b[i]);
}
else {
// this should be glp_set_row_bnds (lp, i+1, typx, -b[i], b[i]);
glp_set_row_bnds (lp, i+1, typx, b[i], b[i]);
}
}
// Load constraint matrix A
glp_load_matrix (lp, nz, rn, cn, a);
// Save problem
if (save_pb) {
if (!strcmp(filetype,"cplex")){
if (glp_write_lp (lp, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"fixedmps")){
if (glp_write_mps (lp, GLP_MPS_DECK, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{
if (!strcmp(filetype,"freemps")){
if (glp_write_mps (lp, GLP_MPS_FILE, NULL, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}else{// plain text
if (lpx_print_prob (lp, save_filename) != 0) {
mexErrMsgTxt("glpk: unable to write the problem");
longjmp (mark, -1);
}
}
}
}
}
//-- scale the problem data (if required)
if (! glpIntParam[16] || lpsolver != 1) {
switch ( glpIntParam[1] ) {
case ( 0 ): glp_scale_prob( lp, GLP_SF_SKIP ); break;
case ( 1 ): glp_scale_prob( lp, GLP_SF_GM ); break;
case ( 2 ): glp_scale_prob( lp, GLP_SF_EQ ); break;
case ( 3 ): glp_scale_prob( lp, GLP_SF_AUTO ); break;
case ( 4 ): glp_scale_prob( lp, GLP_SF_2N ); break;
default :
mexErrMsgTxt("glpk: unrecognized scaling option");
longjmp (mark, -1);
}
}
else {
/* do nothing? or unscale?
glp_unscale_prob( lp );
*/
}
//-- build advanced initial basis (if required)
if (lpsolver == 1 && ! glpIntParam[16])
glp_adv_basis (lp, 0);
glp_smcp sParam;
glp_init_smcp(&sParam);
//-- set control parameters for simplex/exact method
if (lpsolver == 1 || lpsolver == 3){
//remap of control parameters for simplex method
sParam.msg_lev=glpIntParam[0]; // message level
// simplex method: primal/dual
switch ( glpIntParam[2] ) {
case 0: sParam.meth=GLP_PRIMAL; break;
case 1: sParam.meth=GLP_DUAL; break;
case 2: sParam.meth=GLP_DUALP; break;
default:
mexErrMsgTxt("glpk: unrecognized primal/dual method");
longjmp (mark, -1);
}
// pricing technique
if (glpIntParam[3]==0) sParam.pricing=GLP_PT_STD;
else sParam.pricing=GLP_PT_PSE;
// ratio test
if (glpIntParam[20]==0) sParam.r_test = GLP_RT_STD;
else sParam.r_test=GLP_RT_HAR;
//tollerances
sParam.tol_bnd=glpRealParam[1]; // primal feasible tollerance
sParam.tol_dj=glpRealParam[2]; // dual feasible tollerance
sParam.tol_piv=glpRealParam[3]; // pivot tollerance
sParam.obj_ll=glpRealParam[4]; // lower limit
sParam.obj_ul=glpRealParam[5]; // upper limit
// iteration limit
if (glpIntParam[5]==-1) sParam.it_lim=INT_MAX;
else sParam.it_lim=glpIntParam[5];
// time limit
if (glpRealParam[6]==-1) sParam.tm_lim=INT_MAX;
else sParam.tm_lim=(int) glpRealParam[6];
sParam.out_frq=glpIntParam[7]; // output frequency
sParam.out_dly=(int) glpRealParam[7]; // output delay
// presolver
if (glpIntParam[16]) sParam.presolve=GLP_ON;
else sParam.presolve=GLP_OFF;
}else{
for(int i = 0; i < NIntP; i++) {
// skip assinging ratio test or
if ( i == 18 || i == 20) continue;
lpx_set_int_parm (lp, IParam[i], glpIntParam[i]);
}
for (int i = 0; i < NRealP; i++) {
lpx_set_real_parm (lp, RParam[i], glpRealParam[i]);
}
}
//set MIP params if MIP....
glp_iocp iParam;
glp_init_iocp(&iParam);
if ( isMIP ){
method = 'I';
switch (glpIntParam[0]) { //message level
case 0: iParam.msg_lev = GLP_MSG_OFF; break;
case 1: iParam.msg_lev = GLP_MSG_ERR; break;
case 2: iParam.msg_lev = GLP_MSG_ON; break;
case 3: iParam.msg_lev = GLP_MSG_ALL; break;
default: mexErrMsgTxt("glpk: msg_lev bad param");
}
switch (glpIntParam[14]) { //branching param
case 0: iParam.br_tech = GLP_BR_FFV; break;
case 1: iParam.br_tech = GLP_BR_LFV; break;
case 2: iParam.br_tech = GLP_BR_MFV; break;
case 3: iParam.br_tech = GLP_BR_DTH; break;
default: mexErrMsgTxt("glpk: branch bad param");
}
switch (glpIntParam[15]) { //backtracking heuristic
case 0: iParam.bt_tech = GLP_BT_DFS; break;
case 1: iParam.bt_tech = GLP_BT_BFS; break;
case 2: iParam.bt_tech = GLP_BT_BLB; break;
case 3: iParam.bt_tech = GLP_BT_BPH; break;
default: mexErrMsgTxt("glpk: backtrack bad param");
}
if ( glpRealParam[8] > 0.0 && glpRealParam[8] < 1.0 )
iParam.tol_int = glpRealParam[8]; // absolute tolorence
else
mexErrMsgTxt("glpk: tolint must be between 0 and 1");
iParam.tol_obj = glpRealParam[9]; // relative tolarence
iParam.mip_gap = glpRealParam[10]; // realative gap tolerance
// set time limit for mip
if ( glpRealParam[6] < 0.0 || glpRealParam[6] > 1e6 )
iParam.tm_lim = INT_MAX;
else
iParam.tm_lim = (int)(1000.0 * glpRealParam[6] );
// Choose Cutsets for mip
// shut all cuts off, then start over....
iParam.gmi_cuts = GLP_OFF;
iParam.mir_cuts = GLP_OFF;
iParam.cov_cuts = GLP_OFF;
iParam.clq_cuts = GLP_OFF;
switch( glpIntParam[17] ) {
case 0: break;
case 1: iParam.gmi_cuts = GLP_ON; break;
case 2: iParam.mir_cuts = GLP_ON; break;
case 3: iParam.cov_cuts = GLP_ON; break;
case 4: iParam.clq_cuts = GLP_ON; break;
case 5: iParam.clq_cuts = GLP_ON;
iParam.gmi_cuts = GLP_ON;
iParam.mir_cuts = GLP_ON;
iParam.cov_cuts = GLP_ON;
iParam.clq_cuts = GLP_ON; break;
default: mexErrMsgTxt("glpk: cutset bad param");
}
switch( glpIntParam[18] ) { // pre-processing for mip
case 0: iParam.pp_tech = GLP_PP_NONE; break;
case 1: iParam.pp_tech = GLP_PP_ROOT; break;
case 2: iParam.pp_tech = GLP_PP_ALL; break;
default: mexErrMsgTxt("glpk: pprocess bad param");
}
if (glpIntParam[16]) iParam.presolve=GLP_ON;
else iParam.presolve=GLP_OFF;
if (glpIntParam[19]) iParam.binarize = GLP_ON;
else iParam.binarize = GLP_OFF;
}
else {
/* Choose simplex method ('S')
or interior point method ('T')
or Exact method ('E')
to solve the problem */
switch (lpsolver) {
case 1: method = 'S'; break;
case 2: method = 'T'; break;
case 3: method = 'E'; break;
default:
mexErrMsgTxt("glpk: lpsolver != lpsolver");
longjmp (mark, -1);
}
}
// now run the problem...
int errnum = 0;
switch (method) {
case 'I':
errnum = glp_intopt( lp, &iParam );
errnum += 200; //this is to avoid ambiguity in the return codes.
break;
case 'S':
errnum = glp_simplex(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
case 'T':
errnum = glp_interior(lp, NULL );
errnum += 300; //this is to avoid ambiguity in the return codes.
break;
case 'E':
errnum = glp_exact(lp, &sParam);
errnum += 100; //this is to avoid ambiguity in the return codes.
break;
default: /*xassert (method != method); */
mexErrMsgTxt("glpk: method != method");
longjmp (mark, -1);
}
if (errnum==100 || errnum==200 || errnum==300 || errnum==106 || errnum==107 || errnum==108 || errnum==109 || errnum==209 || errnum==214 || errnum==308) {
// Get status and object value
if (isMIP) {
*status = glp_mip_status (lp);
*fmin = glp_mip_obj_val (lp);
}
else {
if (lpsolver == 1 || lpsolver == 3) {
*status = glp_get_status (lp);
*fmin = glp_get_obj_val (lp);
}
else {
*status = glp_ipt_status (lp);
*fmin = glp_ipt_obj_val (lp);
}
}
// Get optimal solution (if exists)
if (isMIP) {
for (int i = 0; i < n; i++)
xmin[i] = glp_mip_col_val (lp, i+1);
}
else {
/* Primal values */
for (int i = 0; i < n; i++) {
if (lpsolver == 1 || lpsolver == 3)
xmin[i] = glp_get_col_prim (lp, i+1);
else
xmin[i] = glp_ipt_col_prim (lp, i+1);
}
/* Dual values */
for (int i = 0; i < m; i++) {
if (lpsolver == 1 || lpsolver == 3)
lambda[i] = glp_get_row_dual (lp, i+1);
else
lambda[i] = glp_ipt_row_dual (lp, i+1);
}
/* Reduced costs */
for (int i = 0; i < glp_get_num_cols (lp); i++) {
if (lpsolver == 1 || lpsolver == 3)
redcosts[i] = glp_get_col_dual (lp, i+1);
else
redcosts[i] = glp_ipt_col_dual (lp, i+1);
}
}
*time = (clock () - t_start) / CLOCKS_PER_SEC;
size_t tpeak;
glp_mem_usage(NULL, NULL, NULL, &tpeak);
*mem=((double) tpeak) / (1024);
lpx_delete_prob(lp);
return 0;
}
else {
// printf("errnum is %d\n", errnum);
}
lpx_delete_prob(lp);
/* this shouldn't be nessiary with glp_deleted_prob, but try it
if we have weird behavior again... */
glp_free_env();
*status = errnum;
return errnum;
}
static 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;
}
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;
}
}
