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+/* cpxbas.c (construct Bixby's initial LP basis) */
+
+/***********************************************************************
+* This code is part of GLPK (GNU Linear Programming Kit).
+*
+* Copyright (C) 2008-2018 Andrew Makhorin, Department for Applied
+* Informatics, Moscow Aviation Institute, Moscow, Russia. All rights
+* reserved. E-mail: <mao@gnu.org>.
+*
+* 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 <http://www.gnu.org/licenses/>.
+***********************************************************************/
+
+#include "env.h"
+#include "prob.h"
+
+struct var
+{ /* structural variable */
+ int j;
+ /* ordinal number */
+ double q;
+ /* penalty value */
+};
+
+static int CDECL fcmp(const void *ptr1, const void *ptr2)
+{ /* this routine is passed to the qsort() function */
+ struct var *col1 = (void *)ptr1, *col2 = (void *)ptr2;
+ if (col1->q < col2->q) return -1;
+ if (col1->q > col2->q) return +1;
+ return 0;
+}
+
+static int get_column(glp_prob *lp, int j, int ind[], double val[])
+{ /* Bixby's algorithm assumes that the constraint matrix is scaled
+ such that the maximum absolute value in every non-zero row and
+ column is 1 */
+ int k, len;
+ double big;
+ len = glp_get_mat_col(lp, j, ind, val);
+ big = 0.0;
+ for (k = 1; k <= len; k++)
+ if (big < fabs(val[k])) big = fabs(val[k]);
+ if (big == 0.0) big = 1.0;
+ for (k = 1; k <= len; k++) val[k] /= big;
+ return len;
+}
+
+static void cpx_basis(glp_prob *lp)
+{ /* main routine */
+ struct var *C, *C2, *C3, *C4;
+ int m, n, i, j, jk, k, l, ll, t, n2, n3, n4, type, len, *I, *r,
+ *ind;
+ double alpha, gamma, cmax, temp, *v, *val;
+ xprintf("Constructing initial basis...\n");
+ /* determine the number of rows and columns */
+ m = glp_get_num_rows(lp);
+ n = glp_get_num_cols(lp);
+ /* allocate working arrays */
+ C = xcalloc(1+n, sizeof(struct var));
+ I = xcalloc(1+m, sizeof(int));
+ r = xcalloc(1+m, sizeof(int));
+ v = xcalloc(1+m, sizeof(double));
+ ind = xcalloc(1+m, sizeof(int));
+ val = xcalloc(1+m, sizeof(double));
+ /* make all auxiliary variables non-basic */
+ for (i = 1; i <= m; i++)
+ { if (glp_get_row_type(lp, i) != GLP_DB)
+ glp_set_row_stat(lp, i, GLP_NS);
+ else if (fabs(glp_get_row_lb(lp, i)) <=
+ fabs(glp_get_row_ub(lp, i)))
+ glp_set_row_stat(lp, i, GLP_NL);
+ else
+ glp_set_row_stat(lp, i, GLP_NU);
+ }
+ /* make all structural variables non-basic */
+ for (j = 1; j <= n; j++)
+ { if (glp_get_col_type(lp, j) != GLP_DB)
+ glp_set_col_stat(lp, j, GLP_NS);
+ else if (fabs(glp_get_col_lb(lp, j)) <=
+ fabs(glp_get_col_ub(lp, j)))
+ glp_set_col_stat(lp, j, GLP_NL);
+ else
+ glp_set_col_stat(lp, j, GLP_NU);
+ }
+ /* C2 is a set of free structural variables */
+ n2 = 0, C2 = C + 0;
+ for (j = 1; j <= n; j++)
+ { type = glp_get_col_type(lp, j);
+ if (type == GLP_FR)
+ { n2++;
+ C2[n2].j = j;
+ C2[n2].q = 0.0;
+ }
+ }
+ /* C3 is a set of structural variables having excatly one (lower
+ or upper) bound */
+ n3 = 0, C3 = C2 + n2;
+ for (j = 1; j <= n; j++)
+ { type = glp_get_col_type(lp, j);
+ if (type == GLP_LO)
+ { n3++;
+ C3[n3].j = j;
+ C3[n3].q = + glp_get_col_lb(lp, j);
+ }
+ else if (type == GLP_UP)
+ { n3++;
+ C3[n3].j = j;
+ C3[n3].q = - glp_get_col_ub(lp, j);
+ }
+ }
+ /* C4 is a set of structural variables having both (lower and
+ upper) bounds */
+ n4 = 0, C4 = C3 + n3;
+ for (j = 1; j <= n; j++)
+ { type = glp_get_col_type(lp, j);
+ if (type == GLP_DB)
+ { n4++;
+ C4[n4].j = j;
+ C4[n4].q = glp_get_col_lb(lp, j) - glp_get_col_ub(lp, j);
+ }
+ }
+ /* compute gamma = max{|c[j]|: 1 <= j <= n} */
+ gamma = 0.0;
+ for (j = 1; j <= n; j++)
+ { temp = fabs(glp_get_obj_coef(lp, j));
+ if (gamma < temp) gamma = temp;
+ }
+ /* compute cmax */
+ cmax = (gamma == 0.0 ? 1.0 : 1000.0 * gamma);
+ /* compute final penalty for all structural variables within sets
+ C2, C3, and C4 */
+ switch (glp_get_obj_dir(lp))
+ { case GLP_MIN: temp = +1.0; break;
+ case GLP_MAX: temp = -1.0; break;
+ default: xassert(lp != lp);
+ }
+ for (k = 1; k <= n2+n3+n4; k++)
+ { j = C[k].j;
+ C[k].q += (temp * glp_get_obj_coef(lp, j)) / cmax;
+ }
+ /* sort structural variables within C2, C3, and C4 in ascending
+ order of penalty value */
+ qsort(C2+1, n2, sizeof(struct var), fcmp);
+ for (k = 1; k < n2; k++) xassert(C2[k].q <= C2[k+1].q);
+ qsort(C3+1, n3, sizeof(struct var), fcmp);
+ for (k = 1; k < n3; k++) xassert(C3[k].q <= C3[k+1].q);
+ qsort(C4+1, n4, sizeof(struct var), fcmp);
+ for (k = 1; k < n4; k++) xassert(C4[k].q <= C4[k+1].q);
+ /*** STEP 1 ***/
+ for (i = 1; i <= m; i++)
+ { type = glp_get_row_type(lp, i);
+ if (type != GLP_FX)
+ { /* row i is either free or inequality constraint */
+ glp_set_row_stat(lp, i, GLP_BS);
+ I[i] = 1;
+ r[i] = 1;
+ }
+ else
+ { /* row i is equality constraint */
+ I[i] = 0;
+ r[i] = 0;
+ }
+ v[i] = +DBL_MAX;
+ }
+ /*** STEP 2 ***/
+ for (k = 1; k <= n2+n3+n4; k++)
+ { jk = C[k].j;
+ len = get_column(lp, jk, ind, val);
+ /* let alpha = max{|A[l,jk]|: r[l] = 0} and let l' be such
+ that alpha = |A[l',jk]| */
+ alpha = 0.0, ll = 0;
+ for (t = 1; t <= len; t++)
+ { l = ind[t];
+ if (r[l] == 0 && alpha < fabs(val[t]))
+ alpha = fabs(val[t]), ll = l;
+ }
+ if (alpha >= 0.99)
+ { /* B := B union {jk} */
+ glp_set_col_stat(lp, jk, GLP_BS);
+ I[ll] = 1;
+ v[ll] = alpha;
+ /* r[l] := r[l] + 1 for all l such that |A[l,jk]| != 0 */
+ for (t = 1; t <= len; t++)
+ { l = ind[t];
+ if (val[t] != 0.0) r[l]++;
+ }
+ /* continue to the next k */
+ continue;
+ }
+ /* if |A[l,jk]| > 0.01 * v[l] for some l, continue to the
+ next k */
+ for (t = 1; t <= len; t++)
+ { l = ind[t];
+ if (fabs(val[t]) > 0.01 * v[l]) break;
+ }
+ if (t <= len) continue;
+ /* otherwise, let alpha = max{|A[l,jk]|: I[l] = 0} and let l'
+ be such that alpha = |A[l',jk]| */
+ alpha = 0.0, ll = 0;
+ for (t = 1; t <= len; t++)
+ { l = ind[t];
+ if (I[l] == 0 && alpha < fabs(val[t]))
+ alpha = fabs(val[t]), ll = l;
+ }
+ /* if alpha = 0, continue to the next k */
+ if (alpha == 0.0) continue;
+ /* B := B union {jk} */
+ glp_set_col_stat(lp, jk, GLP_BS);
+ I[ll] = 1;
+ v[ll] = alpha;
+ /* r[l] := r[l] + 1 for all l such that |A[l,jk]| != 0 */
+ for (t = 1; t <= len; t++)
+ { l = ind[t];
+ if (val[t] != 0.0) r[l]++;
+ }
+ }
+ /*** STEP 3 ***/
+ /* add an artificial variable (auxiliary variable for equality
+ constraint) to cover each remaining uncovered row */
+ for (i = 1; i <= m; i++)
+ if (I[i] == 0) glp_set_row_stat(lp, i, GLP_BS);
+ /* free working arrays */
+ xfree(C);
+ xfree(I);
+ xfree(r);
+ xfree(v);
+ xfree(ind);
+ xfree(val);
+ return;
+}
+
+/***********************************************************************
+* NAME
+*
+* glp_cpx_basis - construct Bixby's initial LP basis
+*
+* SYNOPSIS
+*
+* void glp_cpx_basis(glp_prob *lp);
+*
+* DESCRIPTION
+*
+* The routine glp_cpx_basis constructs an advanced initial basis for
+* the specified problem object.
+*
+* The routine is based on Bixby's algorithm described in the paper:
+*
+* Robert E. Bixby. Implementing the Simplex Method: The Initial Basis.
+* ORSA Journal on Computing, Vol. 4, No. 3, 1992, pp. 267-84. */
+
+void glp_cpx_basis(glp_prob *lp)
+{ if (lp->m == 0 || lp->n == 0)
+ glp_std_basis(lp);
+ else
+ cpx_basis(lp);
+ return;
+}
+
+/* eof */