/* glpios09.c (branching heuristics) */ /*********************************************************************** * This code is part of GLPK (GNU Linear Programming Kit). * * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, * 2009, 2010, 2011, 2013 Andrew Makhorin, Department for Applied * Informatics, Moscow Aviation Institute, Moscow, Russia. All rights * reserved. E-mail: . * * 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 "glpenv.h" #include "glpios.h" /*********************************************************************** * NAME * * ios_choose_var - select variable to branch on * * SYNOPSIS * * #include "glpios.h" * int ios_choose_var(glp_tree *T, int *next); * * The routine ios_choose_var chooses a variable from the candidate * list to branch on. Additionally the routine provides a flag stored * in the location next to suggests which of the child subproblems * should be solved next. * * RETURNS * * The routine ios_choose_var returns the ordinal number of the column * choosen. */ static int branch_first(glp_tree *T, int *next); static int branch_last(glp_tree *T, int *next); static int branch_mostf(glp_tree *T, int *next); static int branch_drtom(glp_tree *T, int *next); int ios_choose_var(glp_tree *T, int *next) { int j; if (T->parm->br_tech == GLP_BR_FFV) { /* branch on first fractional variable */ j = branch_first(T, next); } else if (T->parm->br_tech == GLP_BR_LFV) { /* branch on last fractional variable */ j = branch_last(T, next); } else if (T->parm->br_tech == GLP_BR_MFV) { /* branch on most fractional variable */ j = branch_mostf(T, next); } else if (T->parm->br_tech == GLP_BR_DTH) { /* branch using the heuristic by Dreebeck and Tomlin */ j = branch_drtom(T, next); } else if (T->parm->br_tech == GLP_BR_PCH) { /* hybrid pseudocost heuristic */ j = ios_pcost_branch(T, next); } else xassert(T != T); return j; } /*********************************************************************** * branch_first - choose first branching variable * * This routine looks up the list of structural variables and chooses * the first one, which is of integer kind and has fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. */ static int branch_first(glp_tree *T, int *_next) { int j, next; double beta; /* choose the column to branch on */ for (j = 1; j <= T->n; j++) if (T->non_int[j]) break; xassert(1 <= j && j <= T->n); /* select the branch to be solved next */ beta = glp_get_col_prim(T->mip, j); if (beta - floor(beta) < ceil(beta) - beta) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; *_next = next; return j; } /*********************************************************************** * branch_last - choose last branching variable * * This routine looks up the list of structural variables and chooses * the last one, which is of integer kind and has fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. */ static int branch_last(glp_tree *T, int *_next) { int j, next; double beta; /* choose the column to branch on */ for (j = T->n; j >= 1; j--) if (T->non_int[j]) break; xassert(1 <= j && j <= T->n); /* select the branch to be solved next */ beta = glp_get_col_prim(T->mip, j); if (beta - floor(beta) < ceil(beta) - beta) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; *_next = next; return j; } /*********************************************************************** * branch_mostf - choose most fractional branching variable * * This routine looks up the list of structural variables and chooses * that one, which is of integer kind and has most fractional value in * optimal solution to the current LP relaxation. * * This routine also selects the branch to be solved next where integer * infeasibility of the chosen variable is less than in other one. * * (Alexander Martin notices that "...most infeasible is as good as * random...".) */ static int branch_mostf(glp_tree *T, int *_next) { int j, jj, next; double beta, most, temp; /* choose the column to branch on */ jj = 0, most = DBL_MAX; for (j = 1; j <= T->n; j++) { if (T->non_int[j]) { beta = glp_get_col_prim(T->mip, j); temp = floor(beta) + 0.5; if (most > fabs(beta - temp)) { jj = j, most = fabs(beta - temp); if (beta < temp) next = GLP_DN_BRNCH; else next = GLP_UP_BRNCH; } } } *_next = next; return jj; } /*********************************************************************** * branch_drtom - choose branching var using Driebeck-Tomlin heuristic * * This routine chooses a structural variable, which is required to be * integral and has fractional value in optimal solution of the current * LP relaxation, using a heuristic proposed by Driebeck and Tomlin. * * The routine also selects the branch to be solved next, again due to * Driebeck and Tomlin. * * This routine is based on the heuristic proposed in: * * Driebeck N.J. An algorithm for the solution of mixed-integer * programming problems, Management Science, 12: 576-87 (1966); * * and improved in: * * Tomlin J.A. Branch and bound methods for integer and non-convex * programming, in J.Abadie (ed.), Integer and Nonlinear Programming, * North-Holland, Amsterdam, pp. 437-50 (1970). * * Must note that this heuristic is time-expensive, because computing * one-step degradation (see the routine below) requires one BTRAN for * each fractional-valued structural variable. */ static int branch_drtom(glp_tree *T, int *_next) { glp_prob *mip = T->mip; int m = mip->m; int n = mip->n; unsigned 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", glp_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", glp_get_obj_val(mip) + dd_up); } done: *_next = next; return jj; } /**********************************************************************/ struct csa { /* common storage area */ int *dn_cnt; /* int dn_cnt[1+n]; */ /* dn_cnt[j] is the number of subproblems, whose LP relaxations have been solved and which are down-branches for variable x[j]; dn_cnt[j] = 0 means the down pseudocost is uninitialized */ double *dn_sum; /* double dn_sum[1+n]; */ /* dn_sum[j] is the sum of per unit degradations of the objective over all dn_cnt[j] subproblems */ int *up_cnt; /* int up_cnt[1+n]; */ /* up_cnt[j] is the number of subproblems, whose LP relaxations have been solved and which are up-branches for variable x[j]; up_cnt[j] = 0 means the up pseudocost is uninitialized */ double *up_sum; /* double up_sum[1+n]; */ /* up_sum[j] is the sum of per unit degradations of the objective over all up_cnt[j] subproblems */ }; void *ios_pcost_init(glp_tree *tree) { /* initialize working data used on pseudocost branching */ struct csa *csa; int n = tree->n, j; csa = xmalloc(sizeof(struct csa)); csa->dn_cnt = xcalloc(1+n, sizeof(int)); csa->dn_sum = xcalloc(1+n, sizeof(double)); csa->up_cnt = xcalloc(1+n, sizeof(int)); csa->up_sum = xcalloc(1+n, sizeof(double)); for (j = 1; j <= n; j++) { csa->dn_cnt[j] = csa->up_cnt[j] = 0; csa->dn_sum[j] = csa->up_sum[j] = 0.0; } return csa; } 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; } void ios_pcost_update(glp_tree *tree) { /* update history information for pseudocost branching */ /* this routine is called every time when LP relaxation of the current subproblem has been solved to optimality with all lazy and cutting plane constraints included */ int j; double dx, dz, psi; struct csa *csa = tree->pcost; xassert(csa != NULL); xassert(tree->curr != NULL); /* if the current subproblem is the root, skip updating */ if (tree->curr->up == NULL) goto skip; /* determine branching variable x[j], which was used in the parent subproblem to create the current subproblem */ j = tree->curr->up->br_var; xassert(1 <= j && j <= tree->n); /* determine the change dx[j] = new x[j] - old x[j], where new x[j] is a value of x[j] in optimal solution to LP relaxation of the current subproblem, old x[j] is a value of x[j] in optimal solution to LP relaxation of the parent subproblem */ dx = tree->mip->col[j]->prim - tree->curr->up->br_val; xassert(dx != 0.0); /* determine corresponding change dz = new dz - old dz in the objective function value */ dz = tree->mip->obj_val - tree->curr->up->lp_obj; /* determine per unit degradation of the objective function */ psi = fabs(dz / dx); /* update history information */ if (dx < 0.0) { /* the current subproblem is down-branch */ csa->dn_cnt[j]++; csa->dn_sum[j] += psi; } else /* dx > 0.0 */ { /* the current subproblem is up-branch */ csa->up_cnt[j]++; csa->up_sum[j] += psi; } skip: return; } void ios_pcost_free(glp_tree *tree) { /* free working area used on pseudocost branching */ struct csa *csa = tree->pcost; xassert(csa != NULL); xfree(csa->dn_cnt); xfree(csa->dn_sum); xfree(csa->up_cnt); xfree(csa->up_sum); xfree(csa); tree->pcost = NULL; return; } static double eval_psi(glp_tree *T, int j, int brnch) { /* compute estimation of pseudocost of variable x[j] for down- or up-branch */ struct csa *csa = T->pcost; double beta, degrad, psi; xassert(csa != NULL); xassert(1 <= j && j <= T->n); if (brnch == GLP_DN_BRNCH) { /* down-branch */ if (csa->dn_cnt[j] == 0) { /* initialize down pseudocost */ beta = T->mip->col[j]->prim; degrad = eval_degrad(T->mip, j, floor(beta)); if (degrad == DBL_MAX) { psi = DBL_MAX; goto done; } csa->dn_cnt[j] = 1; csa->dn_sum[j] = degrad / (beta - floor(beta)); } psi = csa->dn_sum[j] / (double)csa->dn_cnt[j]; } else if (brnch == GLP_UP_BRNCH) { /* up-branch */ if (csa->up_cnt[j] == 0) { /* initialize up pseudocost */ beta = T->mip->col[j]->prim; degrad = eval_degrad(T->mip, j, ceil(beta)); if (degrad == DBL_MAX) { psi = DBL_MAX; goto done; } csa->up_cnt[j] = 1; csa->up_sum[j] = degrad / (ceil(beta) - beta); } psi = csa->up_sum[j] / (double)csa->up_cnt[j]; } else xassert(brnch != brnch); done: return psi; } static void progress(glp_tree *T) { /* display progress of pseudocost initialization */ struct csa *csa = T->pcost; int j, nv = 0, ni = 0; for (j = 1; j <= T->n; j++) { if (glp_ios_can_branch(T, j)) { nv++; if (csa->dn_cnt[j] > 0 && csa->up_cnt[j] > 0) ni++; } } xprintf("Pseudocosts initialized for %d of %d variables\n", ni, nv); return; } int ios_pcost_branch(glp_tree *T, int *_next) { /* choose branching variable with pseudocost branching */ #if 0 /* 10/VI-2013 */ glp_long t = xtime(); #else double t = xtime(); #endif int j, jjj, sel; double beta, psi, d1, d2, d, dmax; /* initialize the working arrays */ if (T->pcost == NULL) T->pcost = ios_pcost_init(T); /* nothing has been chosen so far */ jjj = 0, dmax = -1.0; /* go through the list of branching candidates */ for (j = 1; j <= T->n; j++) { if (!glp_ios_can_branch(T, j)) continue; /* determine primal value of x[j] in optimal solution to LP relaxation of the current subproblem */ beta = T->mip->col[j]->prim; /* estimate pseudocost of x[j] for down-branch */ psi = eval_psi(T, j, GLP_DN_BRNCH); if (psi == DBL_MAX) { /* down-branch has no primal feasible solution */ jjj = j, sel = GLP_DN_BRNCH; goto done; } /* estimate degradation of the objective for down-branch */ d1 = psi * (beta - floor(beta)); /* estimate pseudocost of x[j] for up-branch */ psi = eval_psi(T, j, GLP_UP_BRNCH); if (psi == DBL_MAX) { /* up-branch has no primal feasible solution */ jjj = j, sel = GLP_UP_BRNCH; goto done; } /* estimate degradation of the objective for up-branch */ d2 = psi * (ceil(beta) - beta); /* determine d = max(d1, d2) */ d = (d1 > d2 ? d1 : d2); /* choose x[j] which provides maximal estimated degradation of the objective either in down- or up-branch */ if (dmax < d) { dmax = d; jjj = j; /* continue the search from a subproblem, where degradation is less than in other one */ sel = (d1 <= d2 ? GLP_DN_BRNCH : GLP_UP_BRNCH); } /* display progress of pseudocost initialization */ if (T->parm->msg_lev >= GLP_ON) { if (xdifftime(xtime(), t) >= 10.0) { progress(T); t = xtime(); } } } if (dmax == 0.0) { /* no degradation is indicated; choose a variable having most fractional value */ jjj = branch_mostf(T, &sel); } done: *_next = sel; return jjj; } /* eof */