lemon-project-template-glpk

annotate deps/glpk/src/glpscl.c @ 11:4fc6ad2fb8a6

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Test GLPK in src/main.cc
author Alpar Juttner <alpar@cs.elte.hu>
date Sun, 06 Nov 2011 21:43:29 +0100
parents
children
rev   line source
alpar@9 1 /* glpscl.c (problem scaling routines) */
alpar@9 2
alpar@9 3 /***********************************************************************
alpar@9 4 * This code is part of GLPK (GNU Linear Programming Kit).
alpar@9 5 *
alpar@9 6 * Copyright (C) 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
alpar@9 7 * 2009, 2010, 2011 Andrew Makhorin, Department for Applied Informatics,
alpar@9 8 * Moscow Aviation Institute, Moscow, Russia. All rights reserved.
alpar@9 9 * E-mail: <mao@gnu.org>.
alpar@9 10 *
alpar@9 11 * GLPK is free software: you can redistribute it and/or modify it
alpar@9 12 * under the terms of the GNU General Public License as published by
alpar@9 13 * the Free Software Foundation, either version 3 of the License, or
alpar@9 14 * (at your option) any later version.
alpar@9 15 *
alpar@9 16 * GLPK is distributed in the hope that it will be useful, but WITHOUT
alpar@9 17 * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
alpar@9 18 * or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public
alpar@9 19 * License for more details.
alpar@9 20 *
alpar@9 21 * You should have received a copy of the GNU General Public License
alpar@9 22 * along with GLPK. If not, see <http://www.gnu.org/licenses/>.
alpar@9 23 ***********************************************************************/
alpar@9 24
alpar@9 25 #include "glpapi.h"
alpar@9 26
alpar@9 27 /***********************************************************************
alpar@9 28 * min_row_aij - determine minimal |a[i,j]| in i-th row
alpar@9 29 *
alpar@9 30 * This routine returns minimal magnitude of (non-zero) constraint
alpar@9 31 * coefficients in i-th row of the constraint matrix.
alpar@9 32 *
alpar@9 33 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 34 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 35 *
alpar@9 36 * If i-th row of the matrix is empty, the routine returns 1. */
alpar@9 37
alpar@9 38 static double min_row_aij(glp_prob *lp, int i, int scaled)
alpar@9 39 { GLPAIJ *aij;
alpar@9 40 double min_aij, temp;
alpar@9 41 xassert(1 <= i && i <= lp->m);
alpar@9 42 min_aij = 1.0;
alpar@9 43 for (aij = lp->row[i]->ptr; aij != NULL; aij = aij->r_next)
alpar@9 44 { temp = fabs(aij->val);
alpar@9 45 if (scaled) temp *= (aij->row->rii * aij->col->sjj);
alpar@9 46 if (aij->r_prev == NULL || min_aij > temp)
alpar@9 47 min_aij = temp;
alpar@9 48 }
alpar@9 49 return min_aij;
alpar@9 50 }
alpar@9 51
alpar@9 52 /***********************************************************************
alpar@9 53 * max_row_aij - determine maximal |a[i,j]| in i-th row
alpar@9 54 *
alpar@9 55 * This routine returns maximal magnitude of (non-zero) constraint
alpar@9 56 * coefficients in i-th row of the constraint matrix.
alpar@9 57 *
alpar@9 58 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 59 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 60 *
alpar@9 61 * If i-th row of the matrix is empty, the routine returns 1. */
alpar@9 62
alpar@9 63 static double max_row_aij(glp_prob *lp, int i, int scaled)
alpar@9 64 { GLPAIJ *aij;
alpar@9 65 double max_aij, temp;
alpar@9 66 xassert(1 <= i && i <= lp->m);
alpar@9 67 max_aij = 1.0;
alpar@9 68 for (aij = lp->row[i]->ptr; aij != NULL; aij = aij->r_next)
alpar@9 69 { temp = fabs(aij->val);
alpar@9 70 if (scaled) temp *= (aij->row->rii * aij->col->sjj);
alpar@9 71 if (aij->r_prev == NULL || max_aij < temp)
alpar@9 72 max_aij = temp;
alpar@9 73 }
alpar@9 74 return max_aij;
alpar@9 75 }
alpar@9 76
alpar@9 77 /***********************************************************************
alpar@9 78 * min_col_aij - determine minimal |a[i,j]| in j-th column
alpar@9 79 *
alpar@9 80 * This routine returns minimal magnitude of (non-zero) constraint
alpar@9 81 * coefficients in j-th column of the constraint matrix.
alpar@9 82 *
alpar@9 83 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 84 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 85 *
alpar@9 86 * If j-th column of the matrix is empty, the routine returns 1. */
alpar@9 87
alpar@9 88 static double min_col_aij(glp_prob *lp, int j, int scaled)
alpar@9 89 { GLPAIJ *aij;
alpar@9 90 double min_aij, temp;
alpar@9 91 xassert(1 <= j && j <= lp->n);
alpar@9 92 min_aij = 1.0;
alpar@9 93 for (aij = lp->col[j]->ptr; aij != NULL; aij = aij->c_next)
alpar@9 94 { temp = fabs(aij->val);
alpar@9 95 if (scaled) temp *= (aij->row->rii * aij->col->sjj);
alpar@9 96 if (aij->c_prev == NULL || min_aij > temp)
alpar@9 97 min_aij = temp;
alpar@9 98 }
alpar@9 99 return min_aij;
alpar@9 100 }
alpar@9 101
alpar@9 102 /***********************************************************************
alpar@9 103 * max_col_aij - determine maximal |a[i,j]| in j-th column
alpar@9 104 *
alpar@9 105 * This routine returns maximal magnitude of (non-zero) constraint
alpar@9 106 * coefficients in j-th column of the constraint matrix.
alpar@9 107 *
alpar@9 108 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 109 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 110 *
alpar@9 111 * If j-th column of the matrix is empty, the routine returns 1. */
alpar@9 112
alpar@9 113 static double max_col_aij(glp_prob *lp, int j, int scaled)
alpar@9 114 { GLPAIJ *aij;
alpar@9 115 double max_aij, temp;
alpar@9 116 xassert(1 <= j && j <= lp->n);
alpar@9 117 max_aij = 1.0;
alpar@9 118 for (aij = lp->col[j]->ptr; aij != NULL; aij = aij->c_next)
alpar@9 119 { temp = fabs(aij->val);
alpar@9 120 if (scaled) temp *= (aij->row->rii * aij->col->sjj);
alpar@9 121 if (aij->c_prev == NULL || max_aij < temp)
alpar@9 122 max_aij = temp;
alpar@9 123 }
alpar@9 124 return max_aij;
alpar@9 125 }
alpar@9 126
alpar@9 127 /***********************************************************************
alpar@9 128 * min_mat_aij - determine minimal |a[i,j]| in constraint matrix
alpar@9 129 *
alpar@9 130 * This routine returns minimal magnitude of (non-zero) constraint
alpar@9 131 * coefficients in the constraint matrix.
alpar@9 132 *
alpar@9 133 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 134 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 135 *
alpar@9 136 * If the matrix is empty, the routine returns 1. */
alpar@9 137
alpar@9 138 static double min_mat_aij(glp_prob *lp, int scaled)
alpar@9 139 { int i;
alpar@9 140 double min_aij, temp;
alpar@9 141 min_aij = 1.0;
alpar@9 142 for (i = 1; i <= lp->m; i++)
alpar@9 143 { temp = min_row_aij(lp, i, scaled);
alpar@9 144 if (i == 1 || min_aij > temp)
alpar@9 145 min_aij = temp;
alpar@9 146 }
alpar@9 147 return min_aij;
alpar@9 148 }
alpar@9 149
alpar@9 150 /***********************************************************************
alpar@9 151 * max_mat_aij - determine maximal |a[i,j]| in constraint matrix
alpar@9 152 *
alpar@9 153 * This routine returns maximal magnitude of (non-zero) constraint
alpar@9 154 * coefficients in the constraint matrix.
alpar@9 155 *
alpar@9 156 * If the parameter scaled is zero, the original constraint matrix A is
alpar@9 157 * assumed. Otherwise, the scaled constraint matrix R*A*S is assumed.
alpar@9 158 *
alpar@9 159 * If the matrix is empty, the routine returns 1. */
alpar@9 160
alpar@9 161 static double max_mat_aij(glp_prob *lp, int scaled)
alpar@9 162 { int i;
alpar@9 163 double max_aij, temp;
alpar@9 164 max_aij = 1.0;
alpar@9 165 for (i = 1; i <= lp->m; i++)
alpar@9 166 { temp = max_row_aij(lp, i, scaled);
alpar@9 167 if (i == 1 || max_aij < temp)
alpar@9 168 max_aij = temp;
alpar@9 169 }
alpar@9 170 return max_aij;
alpar@9 171 }
alpar@9 172
alpar@9 173 /***********************************************************************
alpar@9 174 * eq_scaling - perform equilibration scaling
alpar@9 175 *
alpar@9 176 * This routine performs equilibration scaling of rows and columns of
alpar@9 177 * the constraint matrix.
alpar@9 178 *
alpar@9 179 * If the parameter flag is zero, the routine scales rows at first and
alpar@9 180 * then columns. Otherwise, the routine scales columns and then rows.
alpar@9 181 *
alpar@9 182 * Rows are scaled as follows:
alpar@9 183 *
alpar@9 184 * n
alpar@9 185 * a'[i,j] = a[i,j] / max |a[i,j]|, i = 1,...,m.
alpar@9 186 * j=1
alpar@9 187 *
alpar@9 188 * This makes the infinity (maximum) norm of each row of the matrix
alpar@9 189 * equal to 1.
alpar@9 190 *
alpar@9 191 * Columns are scaled as follows:
alpar@9 192 *
alpar@9 193 * m
alpar@9 194 * a'[i,j] = a[i,j] / max |a[i,j]|, j = 1,...,n.
alpar@9 195 * i=1
alpar@9 196 *
alpar@9 197 * This makes the infinity (maximum) norm of each column of the matrix
alpar@9 198 * equal to 1. */
alpar@9 199
alpar@9 200 static void eq_scaling(glp_prob *lp, int flag)
alpar@9 201 { int i, j, pass;
alpar@9 202 double temp;
alpar@9 203 xassert(flag == 0 || flag == 1);
alpar@9 204 for (pass = 0; pass <= 1; pass++)
alpar@9 205 { if (pass == flag)
alpar@9 206 { /* scale rows */
alpar@9 207 for (i = 1; i <= lp->m; i++)
alpar@9 208 { temp = max_row_aij(lp, i, 1);
alpar@9 209 glp_set_rii(lp, i, glp_get_rii(lp, i) / temp);
alpar@9 210 }
alpar@9 211 }
alpar@9 212 else
alpar@9 213 { /* scale columns */
alpar@9 214 for (j = 1; j <= lp->n; j++)
alpar@9 215 { temp = max_col_aij(lp, j, 1);
alpar@9 216 glp_set_sjj(lp, j, glp_get_sjj(lp, j) / temp);
alpar@9 217 }
alpar@9 218 }
alpar@9 219 }
alpar@9 220 return;
alpar@9 221 }
alpar@9 222
alpar@9 223 /***********************************************************************
alpar@9 224 * gm_scaling - perform geometric mean scaling
alpar@9 225 *
alpar@9 226 * This routine performs geometric mean scaling of rows and columns of
alpar@9 227 * the constraint matrix.
alpar@9 228 *
alpar@9 229 * If the parameter flag is zero, the routine scales rows at first and
alpar@9 230 * then columns. Otherwise, the routine scales columns and then rows.
alpar@9 231 *
alpar@9 232 * Rows are scaled as follows:
alpar@9 233 *
alpar@9 234 * a'[i,j] = a[i,j] / sqrt(alfa[i] * beta[i]), i = 1,...,m,
alpar@9 235 *
alpar@9 236 * where:
alpar@9 237 * n n
alpar@9 238 * alfa[i] = min |a[i,j]|, beta[i] = max |a[i,j]|.
alpar@9 239 * j=1 j=1
alpar@9 240 *
alpar@9 241 * This allows decreasing the ratio beta[i] / alfa[i] for each row of
alpar@9 242 * the matrix.
alpar@9 243 *
alpar@9 244 * Columns are scaled as follows:
alpar@9 245 *
alpar@9 246 * a'[i,j] = a[i,j] / sqrt(alfa[j] * beta[j]), j = 1,...,n,
alpar@9 247 *
alpar@9 248 * where:
alpar@9 249 * m m
alpar@9 250 * alfa[j] = min |a[i,j]|, beta[j] = max |a[i,j]|.
alpar@9 251 * i=1 i=1
alpar@9 252 *
alpar@9 253 * This allows decreasing the ratio beta[j] / alfa[j] for each column
alpar@9 254 * of the matrix. */
alpar@9 255
alpar@9 256 static void gm_scaling(glp_prob *lp, int flag)
alpar@9 257 { int i, j, pass;
alpar@9 258 double temp;
alpar@9 259 xassert(flag == 0 || flag == 1);
alpar@9 260 for (pass = 0; pass <= 1; pass++)
alpar@9 261 { if (pass == flag)
alpar@9 262 { /* scale rows */
alpar@9 263 for (i = 1; i <= lp->m; i++)
alpar@9 264 { temp = min_row_aij(lp, i, 1) * max_row_aij(lp, i, 1);
alpar@9 265 glp_set_rii(lp, i, glp_get_rii(lp, i) / sqrt(temp));
alpar@9 266 }
alpar@9 267 }
alpar@9 268 else
alpar@9 269 { /* scale columns */
alpar@9 270 for (j = 1; j <= lp->n; j++)
alpar@9 271 { temp = min_col_aij(lp, j, 1) * max_col_aij(lp, j, 1);
alpar@9 272 glp_set_sjj(lp, j, glp_get_sjj(lp, j) / sqrt(temp));
alpar@9 273 }
alpar@9 274 }
alpar@9 275 }
alpar@9 276 return;
alpar@9 277 }
alpar@9 278
alpar@9 279 /***********************************************************************
alpar@9 280 * max_row_ratio - determine worst scaling "quality" for rows
alpar@9 281 *
alpar@9 282 * This routine returns the worst scaling "quality" for rows of the
alpar@9 283 * currently scaled constraint matrix:
alpar@9 284 *
alpar@9 285 * m
alpar@9 286 * ratio = max ratio[i],
alpar@9 287 * i=1
alpar@9 288 * where:
alpar@9 289 * n n
alpar@9 290 * ratio[i] = max |a[i,j]| / min |a[i,j]|, 1 <= i <= m,
alpar@9 291 * j=1 j=1
alpar@9 292 *
alpar@9 293 * is the scaling "quality" of i-th row. */
alpar@9 294
alpar@9 295 static double max_row_ratio(glp_prob *lp)
alpar@9 296 { int i;
alpar@9 297 double ratio, temp;
alpar@9 298 ratio = 1.0;
alpar@9 299 for (i = 1; i <= lp->m; i++)
alpar@9 300 { temp = max_row_aij(lp, i, 1) / min_row_aij(lp, i, 1);
alpar@9 301 if (i == 1 || ratio < temp) ratio = temp;
alpar@9 302 }
alpar@9 303 return ratio;
alpar@9 304 }
alpar@9 305
alpar@9 306 /***********************************************************************
alpar@9 307 * max_col_ratio - determine worst scaling "quality" for columns
alpar@9 308 *
alpar@9 309 * This routine returns the worst scaling "quality" for columns of the
alpar@9 310 * currently scaled constraint matrix:
alpar@9 311 *
alpar@9 312 * n
alpar@9 313 * ratio = max ratio[j],
alpar@9 314 * j=1
alpar@9 315 * where:
alpar@9 316 * m m
alpar@9 317 * ratio[j] = max |a[i,j]| / min |a[i,j]|, 1 <= j <= n,
alpar@9 318 * i=1 i=1
alpar@9 319 *
alpar@9 320 * is the scaling "quality" of j-th column. */
alpar@9 321
alpar@9 322 static double max_col_ratio(glp_prob *lp)
alpar@9 323 { int j;
alpar@9 324 double ratio, temp;
alpar@9 325 ratio = 1.0;
alpar@9 326 for (j = 1; j <= lp->n; j++)
alpar@9 327 { temp = max_col_aij(lp, j, 1) / min_col_aij(lp, j, 1);
alpar@9 328 if (j == 1 || ratio < temp) ratio = temp;
alpar@9 329 }
alpar@9 330 return ratio;
alpar@9 331 }
alpar@9 332
alpar@9 333 /***********************************************************************
alpar@9 334 * gm_iterate - perform iterative geometric mean scaling
alpar@9 335 *
alpar@9 336 * This routine performs iterative geometric mean scaling of rows and
alpar@9 337 * columns of the constraint matrix.
alpar@9 338 *
alpar@9 339 * The parameter it_max specifies the maximal number of iterations.
alpar@9 340 * Recommended value of it_max is 15.
alpar@9 341 *
alpar@9 342 * The parameter tau specifies a minimal improvement of the scaling
alpar@9 343 * "quality" on each iteration, 0 < tau < 1. It means than the scaling
alpar@9 344 * process continues while the following condition is satisfied:
alpar@9 345 *
alpar@9 346 * ratio[k] <= tau * ratio[k-1],
alpar@9 347 *
alpar@9 348 * where ratio = max |a[i,j]| / min |a[i,j]| is the scaling "quality"
alpar@9 349 * to be minimized, k is the iteration number. Recommended value of tau
alpar@9 350 * is 0.90. */
alpar@9 351
alpar@9 352 static void gm_iterate(glp_prob *lp, int it_max, double tau)
alpar@9 353 { int k, flag;
alpar@9 354 double ratio = 0.0, r_old;
alpar@9 355 /* if the scaling "quality" for rows is better than for columns,
alpar@9 356 the rows are scaled first; otherwise, the columns are scaled
alpar@9 357 first */
alpar@9 358 flag = (max_row_ratio(lp) > max_col_ratio(lp));
alpar@9 359 for (k = 1; k <= it_max; k++)
alpar@9 360 { /* save the scaling "quality" from previous iteration */
alpar@9 361 r_old = ratio;
alpar@9 362 /* determine the current scaling "quality" */
alpar@9 363 ratio = max_mat_aij(lp, 1) / min_mat_aij(lp, 1);
alpar@9 364 #if 0
alpar@9 365 xprintf("k = %d; ratio = %g\n", k, ratio);
alpar@9 366 #endif
alpar@9 367 /* if improvement is not enough, terminate scaling */
alpar@9 368 if (k > 1 && ratio > tau * r_old) break;
alpar@9 369 /* otherwise, perform another iteration */
alpar@9 370 gm_scaling(lp, flag);
alpar@9 371 }
alpar@9 372 return;
alpar@9 373 }
alpar@9 374
alpar@9 375 /***********************************************************************
alpar@9 376 * NAME
alpar@9 377 *
alpar@9 378 * scale_prob - scale problem data
alpar@9 379 *
alpar@9 380 * SYNOPSIS
alpar@9 381 *
alpar@9 382 * #include "glpscl.h"
alpar@9 383 * void scale_prob(glp_prob *lp, int flags);
alpar@9 384 *
alpar@9 385 * DESCRIPTION
alpar@9 386 *
alpar@9 387 * The routine scale_prob performs automatic scaling of problem data
alpar@9 388 * for the specified problem object. */
alpar@9 389
alpar@9 390 static void scale_prob(glp_prob *lp, int flags)
alpar@9 391 { static const char *fmt =
alpar@9 392 "%s: min|aij| = %10.3e max|aij| = %10.3e ratio = %10.3e\n";
alpar@9 393 double min_aij, max_aij, ratio;
alpar@9 394 xprintf("Scaling...\n");
alpar@9 395 /* cancel the current scaling effect */
alpar@9 396 glp_unscale_prob(lp);
alpar@9 397 /* report original scaling "quality" */
alpar@9 398 min_aij = min_mat_aij(lp, 1);
alpar@9 399 max_aij = max_mat_aij(lp, 1);
alpar@9 400 ratio = max_aij / min_aij;
alpar@9 401 xprintf(fmt, " A", min_aij, max_aij, ratio);
alpar@9 402 /* check if the problem is well scaled */
alpar@9 403 if (min_aij >= 0.10 && max_aij <= 10.0)
alpar@9 404 { xprintf("Problem data seem to be well scaled\n");
alpar@9 405 /* skip scaling, if required */
alpar@9 406 if (flags & GLP_SF_SKIP) goto done;
alpar@9 407 }
alpar@9 408 /* perform iterative geometric mean scaling, if required */
alpar@9 409 if (flags & GLP_SF_GM)
alpar@9 410 { gm_iterate(lp, 15, 0.90);
alpar@9 411 min_aij = min_mat_aij(lp, 1);
alpar@9 412 max_aij = max_mat_aij(lp, 1);
alpar@9 413 ratio = max_aij / min_aij;
alpar@9 414 xprintf(fmt, "GM", min_aij, max_aij, ratio);
alpar@9 415 }
alpar@9 416 /* perform equilibration scaling, if required */
alpar@9 417 if (flags & GLP_SF_EQ)
alpar@9 418 { eq_scaling(lp, max_row_ratio(lp) > max_col_ratio(lp));
alpar@9 419 min_aij = min_mat_aij(lp, 1);
alpar@9 420 max_aij = max_mat_aij(lp, 1);
alpar@9 421 ratio = max_aij / min_aij;
alpar@9 422 xprintf(fmt, "EQ", min_aij, max_aij, ratio);
alpar@9 423 }
alpar@9 424 /* round scale factors to nearest power of two, if required */
alpar@9 425 if (flags & GLP_SF_2N)
alpar@9 426 { int i, j;
alpar@9 427 for (i = 1; i <= lp->m; i++)
alpar@9 428 glp_set_rii(lp, i, round2n(glp_get_rii(lp, i)));
alpar@9 429 for (j = 1; j <= lp->n; j++)
alpar@9 430 glp_set_sjj(lp, j, round2n(glp_get_sjj(lp, j)));
alpar@9 431 min_aij = min_mat_aij(lp, 1);
alpar@9 432 max_aij = max_mat_aij(lp, 1);
alpar@9 433 ratio = max_aij / min_aij;
alpar@9 434 xprintf(fmt, "2N", min_aij, max_aij, ratio);
alpar@9 435 }
alpar@9 436 done: return;
alpar@9 437 }
alpar@9 438
alpar@9 439 /***********************************************************************
alpar@9 440 * NAME
alpar@9 441 *
alpar@9 442 * glp_scale_prob - scale problem data
alpar@9 443 *
alpar@9 444 * SYNOPSIS
alpar@9 445 *
alpar@9 446 * void glp_scale_prob(glp_prob *lp, int flags);
alpar@9 447 *
alpar@9 448 * DESCRIPTION
alpar@9 449 *
alpar@9 450 * The routine glp_scale_prob performs automatic scaling of problem
alpar@9 451 * data for the specified problem object.
alpar@9 452 *
alpar@9 453 * The parameter flags specifies scaling options used by the routine.
alpar@9 454 * Options can be combined with the bitwise OR operator and may be the
alpar@9 455 * following:
alpar@9 456 *
alpar@9 457 * GLP_SF_GM perform geometric mean scaling;
alpar@9 458 * GLP_SF_EQ perform equilibration scaling;
alpar@9 459 * GLP_SF_2N round scale factors to nearest power of two;
alpar@9 460 * GLP_SF_SKIP skip scaling, if the problem is well scaled.
alpar@9 461 *
alpar@9 462 * The parameter flags may be specified as GLP_SF_AUTO, in which case
alpar@9 463 * the routine chooses scaling options automatically. */
alpar@9 464
alpar@9 465 void glp_scale_prob(glp_prob *lp, int flags)
alpar@9 466 { if (flags & ~(GLP_SF_GM | GLP_SF_EQ | GLP_SF_2N | GLP_SF_SKIP |
alpar@9 467 GLP_SF_AUTO))
alpar@9 468 xerror("glp_scale_prob: flags = 0x%02X; invalid scaling option"
alpar@9 469 "s\n", flags);
alpar@9 470 if (flags & GLP_SF_AUTO)
alpar@9 471 flags = (GLP_SF_GM | GLP_SF_EQ | GLP_SF_SKIP);
alpar@9 472 scale_prob(lp, flags);
alpar@9 473 return;
alpar@9 474 }
alpar@9 475
alpar@9 476 /* eof */