1 | /* glpapi08.c (interior-point method routines) */ |
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2 | |
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3 | /*********************************************************************** |
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4 | * This code is part of GLPK (GNU Linear Programming Kit). |
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5 | * |
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6 | * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, |
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7 | * 2009, 2010 Andrew Makhorin, Department for Applied Informatics, |
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8 | * Moscow Aviation Institute, Moscow, Russia. All rights reserved. |
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9 | * E-mail: <mao@gnu.org>. |
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10 | * |
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11 | * GLPK is free software: you can redistribute it and/or modify it |
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12 | * under the terms of the GNU General Public License as published by |
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13 | * the Free Software Foundation, either version 3 of the License, or |
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14 | * (at your option) any later version. |
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15 | * |
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16 | * GLPK is distributed in the hope that it will be useful, but WITHOUT |
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17 | * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY |
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18 | * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public |
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19 | * License for more details. |
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20 | * |
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21 | * You should have received a copy of the GNU General Public License |
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22 | * along with GLPK. If not, see <http://www.gnu.org/licenses/>. |
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23 | ***********************************************************************/ |
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24 | |
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25 | #include "glpapi.h" |
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26 | #include "glpipm.h" |
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27 | #include "glpnpp.h" |
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28 | |
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29 | /*********************************************************************** |
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30 | * NAME |
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31 | * |
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32 | * glp_interior - solve LP problem with the interior-point method |
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33 | * |
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34 | * SYNOPSIS |
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35 | * |
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36 | * int glp_interior(glp_prob *P, const glp_iptcp *parm); |
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37 | * |
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38 | * The routine glp_interior is a driver to the LP solver based on the |
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39 | * interior-point method. |
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40 | * |
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41 | * The interior-point solver has a set of control parameters. Values of |
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42 | * the control parameters can be passed in a structure glp_iptcp, which |
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43 | * the parameter parm points to. |
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44 | * |
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45 | * Currently this routine implements an easy variant of the primal-dual |
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46 | * interior-point method based on Mehrotra's technique. |
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47 | * |
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48 | * This routine transforms the original LP problem to an equivalent LP |
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49 | * problem in the standard formulation (all constraints are equalities, |
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50 | * all variables are non-negative), calls the routine ipm_main to solve |
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51 | * the transformed problem, and then transforms an obtained solution to |
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52 | * the solution of the original problem. |
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53 | * |
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54 | * RETURNS |
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55 | * |
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56 | * 0 The LP problem instance has been successfully solved. This code |
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57 | * does not necessarily mean that the solver has found optimal |
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58 | * solution. It only means that the solution process was successful. |
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59 | * |
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60 | * GLP_EFAIL |
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61 | * The problem has no rows/columns. |
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62 | * |
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63 | * GLP_ENOCVG |
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64 | * Very slow convergence or divergence. |
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65 | * |
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66 | * GLP_EITLIM |
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67 | * Iteration limit exceeded. |
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68 | * |
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69 | * GLP_EINSTAB |
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70 | * Numerical instability on solving Newtonian system. */ |
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71 | |
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72 | static void transform(NPP *npp) |
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73 | { /* transform LP to the standard formulation */ |
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74 | NPPROW *row, *prev_row; |
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75 | NPPCOL *col, *prev_col; |
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76 | for (row = npp->r_tail; row != NULL; row = prev_row) |
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77 | { prev_row = row->prev; |
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78 | if (row->lb == -DBL_MAX && row->ub == +DBL_MAX) |
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79 | npp_free_row(npp, row); |
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80 | else if (row->lb == -DBL_MAX) |
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81 | npp_leq_row(npp, row); |
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82 | else if (row->ub == +DBL_MAX) |
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83 | npp_geq_row(npp, row); |
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84 | else if (row->lb != row->ub) |
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85 | { if (fabs(row->lb) < fabs(row->ub)) |
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86 | npp_geq_row(npp, row); |
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87 | else |
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88 | npp_leq_row(npp, row); |
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89 | } |
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90 | } |
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91 | for (col = npp->c_tail; col != NULL; col = prev_col) |
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92 | { prev_col = col->prev; |
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93 | if (col->lb == -DBL_MAX && col->ub == +DBL_MAX) |
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94 | npp_free_col(npp, col); |
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95 | else if (col->lb == -DBL_MAX) |
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96 | npp_ubnd_col(npp, col); |
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97 | else if (col->ub == +DBL_MAX) |
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98 | { if (col->lb != 0.0) |
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99 | npp_lbnd_col(npp, col); |
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100 | } |
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101 | else if (col->lb != col->ub) |
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102 | { if (fabs(col->lb) < fabs(col->ub)) |
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103 | { if (col->lb != 0.0) |
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104 | npp_lbnd_col(npp, col); |
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105 | } |
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106 | else |
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107 | npp_ubnd_col(npp, col); |
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108 | npp_dbnd_col(npp, col); |
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109 | } |
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110 | else |
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111 | npp_fixed_col(npp, col); |
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112 | } |
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113 | for (row = npp->r_head; row != NULL; row = row->next) |
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114 | xassert(row->lb == row->ub); |
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115 | for (col = npp->c_head; col != NULL; col = col->next) |
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116 | xassert(col->lb == 0.0 && col->ub == +DBL_MAX); |
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117 | return; |
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118 | } |
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119 | |
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120 | int glp_interior(glp_prob *P, const glp_iptcp *parm) |
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121 | { glp_iptcp _parm; |
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122 | GLPROW *row; |
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123 | GLPCOL *col; |
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124 | NPP *npp = NULL; |
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125 | glp_prob *prob = NULL; |
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126 | int i, j, ret; |
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127 | /* check control parameters */ |
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128 | if (parm == NULL) |
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129 | glp_init_iptcp(&_parm), parm = &_parm; |
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130 | if (!(parm->msg_lev == GLP_MSG_OFF || |
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131 | parm->msg_lev == GLP_MSG_ERR || |
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132 | parm->msg_lev == GLP_MSG_ON || |
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133 | parm->msg_lev == GLP_MSG_ALL)) |
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134 | xerror("glp_interior: msg_lev = %d; invalid parameter\n", |
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135 | parm->msg_lev); |
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136 | if (!(parm->ord_alg == GLP_ORD_NONE || |
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137 | parm->ord_alg == GLP_ORD_QMD || |
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138 | parm->ord_alg == GLP_ORD_AMD || |
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139 | parm->ord_alg == GLP_ORD_SYMAMD)) |
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140 | xerror("glp_interior: ord_alg = %d; invalid parameter\n", |
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141 | parm->ord_alg); |
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142 | /* interior-point solution is currently undefined */ |
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143 | P->ipt_stat = GLP_UNDEF; |
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144 | P->ipt_obj = 0.0; |
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145 | /* check bounds of double-bounded variables */ |
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146 | for (i = 1; i <= P->m; i++) |
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147 | { row = P->row[i]; |
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148 | if (row->type == GLP_DB && row->lb >= row->ub) |
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149 | { if (parm->msg_lev >= GLP_MSG_ERR) |
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150 | xprintf("glp_interior: row %d: lb = %g, ub = %g; incorre" |
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151 | "ct bounds\n", i, row->lb, row->ub); |
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152 | ret = GLP_EBOUND; |
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153 | goto done; |
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154 | } |
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155 | } |
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156 | for (j = 1; j <= P->n; j++) |
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157 | { col = P->col[j]; |
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158 | if (col->type == GLP_DB && col->lb >= col->ub) |
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159 | { if (parm->msg_lev >= GLP_MSG_ERR) |
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160 | xprintf("glp_interior: column %d: lb = %g, ub = %g; inco" |
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161 | "rrect bounds\n", j, col->lb, col->ub); |
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162 | ret = GLP_EBOUND; |
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163 | goto done; |
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164 | } |
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165 | } |
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166 | /* transform LP to the standard formulation */ |
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167 | if (parm->msg_lev >= GLP_MSG_ALL) |
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168 | xprintf("Original LP has %d row(s), %d column(s), and %d non-z" |
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169 | "ero(s)\n", P->m, P->n, P->nnz); |
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170 | npp = npp_create_wksp(); |
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171 | npp_load_prob(npp, P, GLP_OFF, GLP_IPT, GLP_ON); |
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172 | transform(npp); |
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173 | prob = glp_create_prob(); |
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174 | npp_build_prob(npp, prob); |
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175 | if (parm->msg_lev >= GLP_MSG_ALL) |
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176 | xprintf("Working LP has %d row(s), %d column(s), and %d non-ze" |
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177 | "ro(s)\n", prob->m, prob->n, prob->nnz); |
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178 | #if 1 |
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179 | /* currently empty problem cannot be solved */ |
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180 | if (!(prob->m > 0 && prob->n > 0)) |
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181 | { if (parm->msg_lev >= GLP_MSG_ERR) |
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182 | xprintf("glp_interior: unable to solve empty problem\n"); |
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183 | ret = GLP_EFAIL; |
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184 | goto done; |
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185 | } |
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186 | #endif |
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187 | /* scale the resultant LP */ |
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188 | { ENV *env = get_env_ptr(); |
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189 | int term_out = env->term_out; |
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190 | env->term_out = GLP_OFF; |
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191 | glp_scale_prob(prob, GLP_SF_EQ); |
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192 | env->term_out = term_out; |
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193 | } |
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194 | /* warn about dense columns */ |
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195 | if (parm->msg_lev >= GLP_MSG_ON && prob->m >= 200) |
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196 | { int len, cnt = 0; |
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197 | for (j = 1; j <= prob->n; j++) |
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198 | { len = glp_get_mat_col(prob, j, NULL, NULL); |
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199 | if ((double)len >= 0.20 * (double)prob->m) cnt++; |
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200 | } |
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201 | if (cnt == 1) |
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202 | xprintf("WARNING: PROBLEM HAS ONE DENSE COLUMN\n"); |
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203 | else if (cnt > 0) |
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204 | xprintf("WARNING: PROBLEM HAS %d DENSE COLUMNS\n", cnt); |
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205 | } |
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206 | /* solve the transformed LP */ |
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207 | ret = ipm_solve(prob, parm); |
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208 | /* postprocess solution from the transformed LP */ |
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209 | npp_postprocess(npp, prob); |
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210 | /* and store solution to the original LP */ |
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211 | npp_unload_sol(npp, P); |
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212 | done: /* free working program objects */ |
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213 | if (npp != NULL) npp_delete_wksp(npp); |
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214 | if (prob != NULL) glp_delete_prob(prob); |
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215 | /* return to the application program */ |
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216 | return ret; |
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217 | } |
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218 | |
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219 | /*********************************************************************** |
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220 | * NAME |
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221 | * |
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222 | * glp_init_iptcp - initialize interior-point solver control parameters |
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223 | * |
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224 | * SYNOPSIS |
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225 | * |
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226 | * void glp_init_iptcp(glp_iptcp *parm); |
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227 | * |
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228 | * DESCRIPTION |
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229 | * |
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230 | * The routine glp_init_iptcp initializes control parameters, which are |
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231 | * used by the interior-point solver, with default values. |
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232 | * |
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233 | * Default values of the control parameters are stored in the glp_iptcp |
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234 | * structure, which the parameter parm points to. */ |
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235 | |
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236 | void glp_init_iptcp(glp_iptcp *parm) |
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237 | { parm->msg_lev = GLP_MSG_ALL; |
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238 | parm->ord_alg = GLP_ORD_AMD; |
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239 | return; |
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240 | } |
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241 | |
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242 | /*********************************************************************** |
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243 | * NAME |
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244 | * |
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245 | * glp_ipt_status - retrieve status of interior-point solution |
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246 | * |
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247 | * SYNOPSIS |
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248 | * |
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249 | * int glp_ipt_status(glp_prob *lp); |
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250 | * |
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251 | * RETURNS |
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252 | * |
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253 | * The routine glp_ipt_status reports the status of solution found by |
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254 | * the interior-point solver as follows: |
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255 | * |
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256 | * GLP_UNDEF - interior-point solution is undefined; |
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257 | * GLP_OPT - interior-point solution is optimal; |
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258 | * GLP_INFEAS - interior-point solution is infeasible; |
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259 | * GLP_NOFEAS - no feasible solution exists. */ |
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260 | |
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261 | int glp_ipt_status(glp_prob *lp) |
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262 | { int ipt_stat = lp->ipt_stat; |
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263 | return ipt_stat; |
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264 | } |
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265 | |
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266 | /*********************************************************************** |
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267 | * NAME |
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268 | * |
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269 | * glp_ipt_obj_val - retrieve objective value (interior point) |
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270 | * |
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271 | * SYNOPSIS |
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272 | * |
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273 | * double glp_ipt_obj_val(glp_prob *lp); |
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274 | * |
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275 | * RETURNS |
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276 | * |
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277 | * The routine glp_ipt_obj_val returns value of the objective function |
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278 | * for interior-point solution. */ |
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279 | |
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280 | double glp_ipt_obj_val(glp_prob *lp) |
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281 | { /*struct LPXCPS *cps = lp->cps;*/ |
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282 | double z; |
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283 | z = lp->ipt_obj; |
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284 | /*if (cps->round && fabs(z) < 1e-9) z = 0.0;*/ |
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285 | return z; |
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286 | } |
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287 | |
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288 | /*********************************************************************** |
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289 | * NAME |
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290 | * |
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291 | * glp_ipt_row_prim - retrieve row primal value (interior point) |
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292 | * |
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293 | * SYNOPSIS |
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294 | * |
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295 | * double glp_ipt_row_prim(glp_prob *lp, int i); |
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296 | * |
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297 | * RETURNS |
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298 | * |
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299 | * The routine glp_ipt_row_prim returns primal value of the auxiliary |
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300 | * variable associated with i-th row. */ |
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301 | |
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302 | double glp_ipt_row_prim(glp_prob *lp, int i) |
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303 | { /*struct LPXCPS *cps = lp->cps;*/ |
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304 | double pval; |
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305 | if (!(1 <= i && i <= lp->m)) |
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306 | xerror("glp_ipt_row_prim: i = %d; row number out of range\n", |
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307 | i); |
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308 | pval = lp->row[i]->pval; |
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309 | /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ |
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310 | return pval; |
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311 | } |
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312 | |
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313 | /*********************************************************************** |
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314 | * NAME |
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315 | * |
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316 | * glp_ipt_row_dual - retrieve row dual value (interior point) |
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317 | * |
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318 | * SYNOPSIS |
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319 | * |
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320 | * double glp_ipt_row_dual(glp_prob *lp, int i); |
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321 | * |
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322 | * RETURNS |
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323 | * |
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324 | * The routine glp_ipt_row_dual returns dual value (i.e. reduced cost) |
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325 | * of the auxiliary variable associated with i-th row. */ |
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326 | |
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327 | double glp_ipt_row_dual(glp_prob *lp, int i) |
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328 | { /*struct LPXCPS *cps = lp->cps;*/ |
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329 | double dval; |
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330 | if (!(1 <= i && i <= lp->m)) |
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331 | xerror("glp_ipt_row_dual: i = %d; row number out of range\n", |
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332 | i); |
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333 | dval = lp->row[i]->dval; |
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334 | /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ |
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335 | return dval; |
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336 | } |
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337 | |
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338 | /*********************************************************************** |
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339 | * NAME |
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340 | * |
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341 | * glp_ipt_col_prim - retrieve column primal value (interior point) |
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342 | * |
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343 | * SYNOPSIS |
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344 | * |
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345 | * double glp_ipt_col_prim(glp_prob *lp, int j); |
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346 | * |
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347 | * RETURNS |
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348 | * |
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349 | * The routine glp_ipt_col_prim returns primal value of the structural |
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350 | * variable associated with j-th column. */ |
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351 | |
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352 | double glp_ipt_col_prim(glp_prob *lp, int j) |
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353 | { /*struct LPXCPS *cps = lp->cps;*/ |
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354 | double pval; |
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355 | if (!(1 <= j && j <= lp->n)) |
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356 | xerror("glp_ipt_col_prim: j = %d; column number out of range\n" |
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357 | , j); |
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358 | pval = lp->col[j]->pval; |
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359 | /*if (cps->round && fabs(pval) < 1e-9) pval = 0.0;*/ |
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360 | return pval; |
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361 | } |
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362 | |
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363 | /*********************************************************************** |
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364 | * NAME |
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365 | * |
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366 | * glp_ipt_col_dual - retrieve column dual value (interior point) |
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367 | * |
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368 | * SYNOPSIS |
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369 | * |
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370 | * #include "glplpx.h" |
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371 | * double glp_ipt_col_dual(glp_prob *lp, int j); |
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372 | * |
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373 | * RETURNS |
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374 | * |
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375 | * The routine glp_ipt_col_dual returns dual value (i.e. reduced cost) |
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376 | * of the structural variable associated with j-th column. */ |
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377 | |
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378 | double glp_ipt_col_dual(glp_prob *lp, int j) |
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379 | { /*struct LPXCPS *cps = lp->cps;*/ |
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380 | double dval; |
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381 | if (!(1 <= j && j <= lp->n)) |
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382 | xerror("glp_ipt_col_dual: j = %d; column number out of range\n" |
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383 | , j); |
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384 | dval = lp->col[j]->dval; |
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385 | /*if (cps->round && fabs(dval) < 1e-9) dval = 0.0;*/ |
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386 | return dval; |
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387 | } |
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388 | |
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389 | /* eof */ |
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