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📄 glpini02.c

📁 著名的大规模线性规划求解器源码GLPK.C语言版本,可以修剪.内有详细帮助文档.
💻 C
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/* glpini02.c *//************************************************************************  This code is part of GLPK (GNU Linear Programming Kit).**  Copyright (C) 2000,01,02,03,04,05,06,07,08,2009 Andrew Makhorin,*  Department for Applied Informatics, Moscow Aviation Institute,*  Moscow, Russia. All rights reserved. E-mail: <mao@mai2.rcnet.ru>.**  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 "glpini.h"struct var{     /* structural variable */      int j;      /* ordinal number */      double q;      /* penalty value */};static int 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;}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("Crashing...\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;}/* eof */

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