/* glpapi08.c (interior-point method routines) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * Copyright (C) 2000-2013 Free Software Foundation, Inc. * Written by Andrew Makhorin . * * GLPK is free software: you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * GLPK is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public * License for more details. * * You should have received a copy of the GNU General Public License * along with GLPK. If not, see . ***********************************************************************/ #include "env.h" #include "glpipm.h" #include "npp.h" /*********************************************************************** * NAME * * glp_interior - solve LP problem with the interior-point method * * SYNOPSIS * * int glp_interior(glp_prob *P, const glp_iptcp *parm); * * The routine glp_interior is a driver to the LP solver based on the * interior-point method. * * The interior-point solver has a set of control parameters. Values of * the control parameters can be passed in a structure glp_iptcp, which * the parameter parm points to. * * Currently this routine implements an easy variant of the primal-dual * interior-point method based on Mehrotra's technique. * * This routine transforms the original LP problem to an equivalent LP * problem in the standard formulation (all constraints are equalities, * all variables are non-negative), calls the routine ipm_main to solve * the transformed problem, and then transforms an obtained solution to * the solution of the original problem. * * RETURNS * * 0 The LP problem instance has been successfully solved. This code * does not necessarily mean that the solver has found optimal * solution. It only means that the solution process was successful. * * GLP_EFAIL * The problem has no rows/columns. * * GLP_ENOCVG * Very slow convergence or divergence. * * GLP_EITLIM * Iteration limit exceeded. * * GLP_EINSTAB * Numerical instability on solving Newtonian system. */ static void transform(NPP *npp) { /* transform LP to the standard formulation */ NPPROW *row, *prev_row; NPPCOL *col, *prev_col; for (row = npp->r_tail; row != NULL; row = prev_row) { prev_row = row->prev; if (row->lb == -DBL_MAX && row->ub == +DBL_MAX) npp_free_row(npp, row); else if (row->lb == -DBL_MAX) npp_leq_row(npp, row); else if (row->ub == +DBL_MAX) npp_geq_row(npp, row); else if (row->lb != row->ub) { if (fabs(row->lb) < fabs(row->ub)) npp_geq_row(npp, row); else npp_leq_row(npp, row); } } for (col = npp->c_tail; col != NULL; col = prev_col) { prev_col = col->prev; if (col->lb == -DBL_MAX && col->ub == +DBL_MAX) npp_free_col(npp, col); else if (col->lb == -DBL_MAX) npp_ubnd_col(npp, col); else if (col->ub == +DBL_MAX) { if (col->lb != 0.0) npp_lbnd_col(npp, col); } else if (col->lb != col->ub) { if (fabs(col->lb) < fabs(col->ub)) { if (col->lb != 0.0) npp_lbnd_col(npp, col); } else npp_ubnd_col(npp, col); npp_dbnd_col(npp, col); } else npp_fixed_col(npp, col); } for (row = npp->r_head; row != NULL; row = row->next) xassert(row->lb == row->ub); for (col = npp->c_head; col != NULL; col = col->next) xassert(col->lb == 0.0 && col->ub == +DBL_MAX); return; } int glp_interior(glp_prob *P, const glp_iptcp *parm) { glp_iptcp _parm; GLPROW *row; GLPCOL *col; NPP *npp = NULL; glp_prob *prob = NULL; int i, j, ret; /* check control parameters */ if (parm == NULL) glp_init_iptcp(&_parm), parm = &_parm; if (!(parm->msg_lev == GLP_MSG_OFF || parm->msg_lev == GLP_MSG_ERR || parm->msg_lev == GLP_MSG_ON || parm->msg_lev == GLP_MSG_ALL)) xerror("glp_interior: msg_lev = %d; invalid parameter\n", parm->msg_lev); if (!(parm->ord_alg == GLP_ORD_NONE || parm->ord_alg == GLP_ORD_QMD || parm->ord_alg == GLP_ORD_AMD || parm->ord_alg == GLP_ORD_SYMAMD)) xerror("glp_interior: ord_alg = %d; invalid parameter\n", parm->ord_alg); /* interior-point solution is currently undefined */ P->ipt_stat = GLP_UNDEF; P->ipt_obj = 0.0; /* check bounds of double-bounded variables */ for (i = 1; i <= P->m; i++) { row = P->row[i]; if (row->type == GLP_DB && row->lb >= row->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: row %d: lb = %g, ub = %g; incorre" "ct bounds\n", i, row->lb, row->ub); ret = GLP_EBOUND; goto done; } } for (j = 1; j <= P->n; j++) { col = P->col[j]; if (col->type == GLP_DB && col->lb >= col->ub) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: column %d: lb = %g, ub = %g; inco" "rrect bounds\n", j, col->lb, col->ub); ret = GLP_EBOUND; goto done; } } /* transform LP to the standard formulation */ if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Original LP has %d row(s), %d column(s), and %d non-z" "ero(s)\n", P->m, P->n, P->nnz); npp = npp_create_wksp(); npp_load_prob(npp, P, GLP_OFF, GLP_IPT, GLP_ON); transform(npp); prob = glp_create_prob(); npp_build_prob(npp, prob); if (parm->msg_lev >= GLP_MSG_ALL) xprintf("Working LP has %d row(s), %d column(s), and %d non-ze" "ro(s)\n", prob->m, prob->n, prob->nnz); #if 1 /* currently empty problem cannot be solved */ if (!(prob->m > 0 && prob->n > 0)) { if (parm->msg_lev >= GLP_MSG_ERR) xprintf("glp_interior: unable to solve empty problem\n"); ret = GLP_EFAIL; goto done; } #endif /* scale the resultant LP */ { ENV *env = get_env_ptr(); int term_out = env->term_out; env->term_out = GLP_OFF; glp_scale_prob(prob, GLP_SF_EQ); env->term_out = term_out; } /* warn about dense columns */ if (parm->msg_lev >= GLP_MSG_ON && prob->m >= 200) { int len, cnt = 0; for (j = 1; j <= prob->n; j++) { len = glp_get_mat_col(prob, j, NULL, NULL); if ((double)len >= 0.20 * (double)prob->m) cnt++; } if (cnt == 1) xprintf("WARNING: PROBLEM HAS ONE DENSE COLUMN\n"); else if (cnt > 0) xprintf("WARNING: PROBLEM HAS %d DENSE COLUMNS\n", cnt); } /* solve the transformed LP */ ret = ipm_solve(prob, parm); /* postprocess solution from the transformed LP */ npp_postprocess(npp, prob); /* and store solution to the original LP */ npp_unload_sol(npp, P); done: /* free working program objects */ if (npp != NULL) npp_delete_wksp(npp); if (prob != NULL) glp_delete_prob(prob); /* return to the application program */ return ret; } /*********************************************************************** * NAME * * glp_init_iptcp - initialize interior-point solver control parameters * * SYNOPSIS * * void glp_init_iptcp(glp_iptcp *parm); * * DESCRIPTION * * The routine glp_init_iptcp initializes control parameters, which are * used by the interior-point solver, with default values. * * Default values of the control parameters are stored in the glp_iptcp * structure, which the parameter parm points to. */ void glp_init_iptcp(glp_iptcp *parm) { parm->msg_lev = GLP_MSG_ALL; parm->ord_alg = GLP_ORD_AMD; return; } /*********************************************************************** * NAME * * glp_ipt_status - retrieve status of interior-point solution * * SYNOPSIS * * int glp_ipt_status(glp_prob *lp); * * RETURNS * * The routine glp_ipt_status reports the status of solution found by * the interior-point solver as follows: * * GLP_UNDEF - interior-point solution is undefined; * GLP_OPT - interior-point solution is optimal; * GLP_INFEAS - interior-point solution is infeasible; * GLP_NOFEAS - no feasible solution exists. */ int glp_ipt_status(glp_prob *lp) { int ipt_stat = lp->ipt_stat; return ipt_stat; } /*********************************************************************** * NAME * * glp_ipt_obj_val - retrieve objective value (interior point) * * SYNOPSIS * * double glp_ipt_obj_val(glp_prob *lp); * * RETURNS * * The routine glp_ipt_obj_val returns value of the objective function * for interior-point solution. */ double glp_ipt_obj_val(glp_prob *lp) { /*struct LPXCPS *cps = lp->cps;*/ double z; z = lp->ipt_obj; /*if (cps->round && fabs(z) < 1e-9) z = 0.0;*/ return z; } /*********************************************************************** * NAME * * glp_ipt_row_prim - retrieve row primal value (interior point) * * SYNOPSIS * * double glp_ipt_row_prim(glp_prob *lp, int i); * * RETURNS * * The routine glp_ipt_row_prim returns primal value of the auxiliary * variable associated with i-th row. */ double glp_ipt_row_prim(glp_prob *lp, int i) { /*struct LPXCPS *cps = lp->cps;*/ double pval; if (!(1 <= i && i <= lp->m)) xerror("glp_ipt_row_prim: i = %d; row number out of range\n", i); pval = lp->row[i]->pval; /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ return pval; } /*********************************************************************** * NAME * * glp_ipt_row_dual - retrieve row dual value (interior point) * * SYNOPSIS * * double glp_ipt_row_dual(glp_prob *lp, int i); * * RETURNS * * The routine glp_ipt_row_dual returns dual value (i.e. reduced cost) * of the auxiliary variable associated with i-th row. */ double glp_ipt_row_dual(glp_prob *lp, int i) { /*struct LPXCPS *cps = lp->cps;*/ double dval; if (!(1 <= i && i <= lp->m)) xerror("glp_ipt_row_dual: i = %d; row number out of range\n", i); dval = lp->row[i]->dval; /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ return dval; } /*********************************************************************** * NAME * * glp_ipt_col_prim - retrieve column primal value (interior point) * * SYNOPSIS * * double glp_ipt_col_prim(glp_prob *lp, int j); * * RETURNS * * The routine glp_ipt_col_prim returns primal value of the structural * variable associated with j-th column. */ double glp_ipt_col_prim(glp_prob *lp, int j) { /*struct LPXCPS *cps = lp->cps;*/ double pval; if (!(1 <= j && j <= lp->n)) xerror("glp_ipt_col_prim: j = %d; column number out of range\n" , j); pval = lp->col[j]->pval; /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ return pval; } /*********************************************************************** * NAME * * glp_ipt_col_dual - retrieve column dual value (interior point) * * SYNOPSIS * * double glp_ipt_col_dual(glp_prob *lp, int j); * * RETURNS * * The routine glp_ipt_col_dual returns dual value (i.e. reduced cost) * of the structural variable associated with j-th column. */ double glp_ipt_col_dual(glp_prob *lp, int j) { /*struct LPXCPS *cps = lp->cps;*/ double dval; if (!(1 <= j && j <= lp->n)) xerror("glp_ipt_col_dual: j = %d; column number out of range\n" , j); dval = lp->col[j]->dval; /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ return dval; } /* eof */