Scippy

SCIP

Solving Constraint Integer Programs

heur_subnlp.c
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1/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
2/* */
3/* This file is part of the program and library */
4/* SCIP --- Solving Constraint Integer Programs */
5/* */
6/* Copyright (c) 2002-2024 Zuse Institute Berlin (ZIB) */
7/* */
8/* Licensed under the Apache License, Version 2.0 (the "License"); */
9/* you may not use this file except in compliance with the License. */
10/* You may obtain a copy of the License at */
11/* */
12/* http://www.apache.org/licenses/LICENSE-2.0 */
13/* */
14/* Unless required by applicable law or agreed to in writing, software */
15/* distributed under the License is distributed on an "AS IS" BASIS, */
16/* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. */
17/* See the License for the specific language governing permissions and */
18/* limitations under the License. */
19/* */
20/* You should have received a copy of the Apache-2.0 license */
21/* along with SCIP; see the file LICENSE. If not visit scipopt.org. */
22/* */
23/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */
24
25/**@file heur_subnlp.c
26 * @ingroup DEFPLUGINS_HEUR
27 * @brief NLP local search primal heuristic using sub-SCIPs
28 * @author Stefan Vigerske
29 *
30 * @todo reconstruct sub-SCIP if problem has changed
31 */
32
33/*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
34
35#include "blockmemshell/memory.h"
36#include "scip/nlpi_ipopt.h"
37#include "scip/cons_bounddisjunction.h"
38#include "scip/cons_setppc.h"
39#include "scip/heur_subnlp.h"
40#include "scip/pub_event.h"
41#include "scip/pub_heur.h"
42#include "scip/pub_message.h"
43#include "scip/pub_misc.h"
44#include "scip/pub_sol.h"
45#include "scip/pub_var.h"
46#include "scip/scip_branch.h"
47#include "scip/scip_cons.h"
48#include "scip/scip_copy.h"
49#include "scip/scip_event.h"
50#include "scip/scip_general.h"
51#include "scip/scip_heur.h"
52#include "scip/scip_lp.h"
53#include "scip/scip_mem.h"
54#include "scip/scip_message.h"
55#include "scip/scip_nlp.h"
56#include "scip/scip_nlpi.h"
57#include "scip/scip_numerics.h"
58#include "scip/scip_param.h"
59#include "scip/scip_presol.h"
60#include "scip/scip_pricer.h"
61#include "scip/scip_prob.h"
62#include "scip/scip_sol.h"
63#include "scip/scip_solve.h"
64#include "scip/scip_solvingstats.h"
65#include "scip/scip_timing.h"
66#include "scip/scip_var.h"
67#include <string.h>
68
69#define HEUR_NAME "subnlp"
70#define HEUR_DESC "primal heuristic that performs a local search in an NLP after fixing integer variables and presolving"
71#define HEUR_DISPCHAR SCIP_HEURDISPCHAR_LNS
72#define HEUR_PRIORITY -2000010
73#define HEUR_FREQ 1
74#define HEUR_FREQOFS 0
75#define HEUR_MAXDEPTH -1
76#define HEUR_TIMING SCIP_HEURTIMING_AFTERNODE
77#define HEUR_USESSUBSCIP FALSE /**< does the heuristic use a secondary SCIP instance? we set this to FALSE because we want this heuristic to also run within other heuristics */
78
79/*
80 * Data structures
81 */
82
83/** primal heuristic data */
84struct SCIP_HeurData
85{
86 SCIP* subscip; /**< copy of CIP where presolving and NLP solving is done */
87 SCIP_Bool triedsetupsubscip; /**< whether we have tried to setup a sub-SCIP */
88 SCIP_Bool subscipisvalid; /**< whether all constraints have been copied */
89 SCIP_Bool continuous; /**< whether problem was continuous when sub-SCIP was created */
90 int nseriousnlpierror; /**< number of consecutive serious NLP solver failures (memout, ...) */
91 SCIP_EVENTHDLR* eventhdlr; /**< event handler for global bound change events */
92
93 int nvars; /**< number of active transformed variables in SCIP */
94 int nsubvars; /**< number of original variables in sub-SCIP */
95 SCIP_VAR** var_subscip2scip; /**< mapping variables in sub-SCIP to SCIP variables */
96 SCIP_VAR** var_scip2subscip; /**< mapping variables in SCIP to sub-SCIP variables */
97
98 SCIP_SOL* startcand; /**< candidate for start point for heuristic */
99 SCIP_Real startcandviol; /**< violation of start point candidate w.r.t. constraint that reported this candidate */
100 SCIP_SOL* lastsol; /**< pointer to last found solution (or NULL if none), not captured, thus may be dangling */
101
102 int nlpverblevel; /**< verbosity level of NLP solver */
103 SCIP_Real opttol; /**< optimality tolerance to use for NLP solves */
104 SCIP_Real feastolfactor; /**< factor on SCIP feasibility tolerance for NLP solves if resolving when NLP solution not feasible in CIP */
105 SCIP_Real feastol; /**< feasibility tolerance for NLP solves */
106 SCIP_Bool tighterfeastolfailed;/**< whether we tried to use a tighter feasibility tolerance but the NLP solution was still not accepted */
107 int maxpresolverounds; /**< limit on number of presolve rounds in sub-SCIP */
108 int presolveemphasis; /**< presolve emphasis in sub-SCIP */
109 SCIP_Bool setcutoff; /**< whether to set cutoff in sub-SCIP to current primal bound */
110 SCIP_Bool forbidfixings; /**< whether to add constraints that forbid specific fixations that turned out to be infeasible */
111 SCIP_Bool keepcopy; /**< whether to keep SCIP copy or to create new copy each time heuristic is applied */
112 SCIP_Real expectinfeas; /**< when to tell NLP solver that an infeasible NLP is not unexpected */
113
114 SCIP_Longint iterused; /**< number of iterations used so far (+ number of heuristic runs + number of presolve runs in subscip) */
115 SCIP_Longint iterusedokay; /**< number of iterations used so far when NLP stopped with status okay */
116 SCIP_Longint iterusediterlim; /**< maximal number of iterations used when NLP stopped due to iteration limit */
117 int nnlpsolves; /**< number of NLP solves */
118 int nnlpsolvesokay; /**< number of NLP solves with status okay */
119 int nnlpsolvesiterlim; /**< number of NLP solves that hit an iteration limit */
120 int nnlpsolvesinfeas; /**< number of NLP solves with status okay and infeasible */
121 int nodesoffset; /**< number of nodes added to the actual number of nodes when computing itercontingent */
122 SCIP_Real nodesfactor; /**< factor to apply to number of nodes in SCIP to compute initial itercontingent */
123 SCIP_Real successrateexp; /**< exponent for power of success rate to be multiplied with itercontingent */
124 int iterinit; /**< number of iterations used for initial NLP solves */
125 int ninitsolves; /**< number of successful NLP solves until switching to iterlimit guess and using success rate */
126 int itermin; /**< minimal number of iterations for NLP solves */
127};
128
129
130/*
131 * Local methods
132 */
133
134/** indicates whether the heuristic should be running, i.e., whether we expect something nonlinear after fixing all discrete variables */
135static
136SCIP_RETCODE runHeuristic(
137 SCIP* scip, /**< SCIP data structure */
138 SCIP_Bool* runheur /**< buffer to store whether to run heuristic */
139 )
140{
141 assert(scip != NULL);
142 assert(runheur != NULL);
143
144 /* do not run heuristic if no NLP solver is available */
145 if( SCIPgetNNlpis(scip) <= 0 )
146 {
147 *runheur = FALSE;
148 return SCIP_OKAY;
149 }
150
151 /* do not run heuristic if no NLP */
152 if( !SCIPisNLPConstructed(scip) )
153 {
154 *runheur = FALSE;
155 return SCIP_OKAY;
156 }
157
158 /* do not run heuristic if no continuous nonlinear variables in NLP */
159 SCIP_CALL( SCIPhasNLPContinuousNonlinearity(scip, runheur) );
160
161 return SCIP_OKAY;
162}
163
164/** free sub-SCIP data structure */
165static
166SCIP_RETCODE freeSubSCIP(
167 SCIP* scip, /**< SCIP data structure */
168 SCIP_HEURDATA* heurdata /**< heuristic data structure */
169 )
170{
171 SCIP_VAR** subvars;
172 int nsubvars;
173 int i;
174 SCIP_VAR* var;
175 SCIP_VAR* subvar;
176
177 assert(scip != NULL);
178 assert(heurdata != NULL);
179
180 assert(heurdata->subscip != NULL);
181
182 SCIP_CALL( SCIPgetOrigVarsData(heurdata->subscip, &subvars, &nsubvars, NULL, NULL, NULL, NULL) );
183 assert(nsubvars == heurdata->nsubvars);
184
185 /* drop global bound change events
186 * release variables in SCIP and sub-SCIP
187 */
188 for( i = 0; i < heurdata->nsubvars; ++i )
189 {
190 subvar = subvars[i];
191 assert(subvar != NULL);
192 assert(SCIPvarGetProbindex(subvar) == i);
193
194 var = heurdata->var_subscip2scip[SCIPvarGetProbindex(subvar)];
195 assert(var != NULL);
196 assert(SCIPvarGetProbindex(var) <= heurdata->nvars);
197 assert(!SCIPvarIsActive(var) || heurdata->var_scip2subscip[SCIPvarGetProbindex(var)] == subvar);
198
199 SCIP_CALL( SCIPdropVarEvent(scip, var, SCIP_EVENTTYPE_GBDCHANGED, heurdata->eventhdlr, (SCIP_EVENTDATA*)heurdata, -1) );
200
201 SCIP_CALL( SCIPreleaseVar(heurdata->subscip, &subvar) );
202 SCIP_CALL( SCIPreleaseVar(scip, &var) );
203 }
204
205 /* free variable mappings subscip -> scip and scip -> subscip */
206 SCIPfreeBlockMemoryArray(scip, &heurdata->var_subscip2scip, heurdata->nsubvars);
207 SCIPfreeBlockMemoryArray(scip, &heurdata->var_scip2subscip, heurdata->nvars);
208 heurdata->nsubvars = 0;
209 heurdata->nvars = 0;
210
211 /* free sub-SCIP */
212 SCIP_CALL( SCIPfree(&heurdata->subscip) );
213
214 return SCIP_OKAY;
215}
216
217/** creates copy of CIP from problem in SCIP */
218static
219SCIP_RETCODE createSubSCIP(
220 SCIP* scip, /**< SCIP data structure */
221 SCIP_HEURDATA* heurdata /**< heuristic data structure */
222 )
223{
224 int nvars;
225 SCIP_VAR** vars;
226 SCIP_VAR** subvars;
227 SCIP_VAR* var;
228 SCIP_VAR* subvar;
229 SCIP_Bool success;
230 char probname[SCIP_MAXSTRLEN];
231 int i;
232 SCIP_HASHMAP* varsmap;
233 SCIP_HASHMAP* conssmap;
234
235 assert(heurdata != NULL);
236 assert(heurdata->subscip == NULL);
237
238 heurdata->triedsetupsubscip = TRUE;
239
240 /* initializing the subproblem */
241 SCIP_CALL( SCIPcreate(&heurdata->subscip) );
242
243 /* create sub-SCIP copy of CIP */
244
245 /* copy interesting plugins */
246 success = TRUE;
247 SCIP_CALL( SCIPcopyPlugins(scip, heurdata->subscip,
248 FALSE, /* readers */
249 FALSE, /* pricers */
250 TRUE, /* conshdlrs */
251 FALSE, /* conflicthdlrs */
252 TRUE, /* presolvers */
253 FALSE, /* relaxators */
254 FALSE, /* separators */
255 FALSE, /* cutselectors */
256 TRUE, /* propagators */
257 FALSE, /* heuristics */
258 TRUE, /* eventhandler */
259 TRUE, /* nodeselectors (SCIP gives an error if there is none) */
260 FALSE, /* branchrules */
261 TRUE, /* displays */
262 FALSE, /* tables */
263 FALSE, /* dialogs */
264 TRUE, /* expression handlers */
265 TRUE, /* nlpis */
266 TRUE, /* message handler */
267 &success) );
268 if( !success )
269 {
270 SCIPdebugMsg(scip, "failed to copy some plugins to sub-SCIP, continue anyway\n");
271 }
272
273 /* check if we still have NLPI's in subscip */
274 if( SCIPgetNNlpis(heurdata->subscip) <= 0 )
275 {
276 SCIPdebugMsg(scip, "none of the NLPIs from main SCIP copied into sub-SCIP, give up heuristic.\n");
277 SCIP_CALL( SCIPfree(&heurdata->subscip) );
278
279 return SCIP_OKAY;
280 }
281
282 /* copy parameter settings */
283 SCIP_CALL( SCIPcopyParamSettings(scip, heurdata->subscip) );
284
285 /* create problem in sub-SCIP */
286 /* get name of the original problem and add "subnlp" */
287 (void) SCIPsnprintf(probname, SCIP_MAXSTRLEN, "%s_subnlp", SCIPgetProbName(scip));
288 SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
289 SCIP_CALL( SCIPhashmapCreate(&varsmap, SCIPblkmem(scip), nvars) );
290 SCIP_CALL( SCIPhashmapCreate(&conssmap, SCIPblkmem(scip), SCIPgetNConss(scip)) );
291 SCIP_CALL( SCIPcopyProb(scip, heurdata->subscip, varsmap, conssmap, TRUE, probname) );
292
293 /* copy all variables */
294 SCIP_CALL( SCIPcopyVars(scip, heurdata->subscip, varsmap, conssmap, NULL, NULL, 0, TRUE) );
295
296 /* copy as many constraints as possible */
297 SCIP_CALL( SCIPcopyConss(scip, heurdata->subscip, varsmap, conssmap, TRUE, FALSE, &heurdata->subscipisvalid) );
298 SCIPhashmapFree(&conssmap);
299 if( !heurdata->subscipisvalid )
300 {
301 SCIPdebugMsg(scip, "failed to copy some constraints to sub-SCIP, continue anyway\n");
302 }
303
304 /* create arrays translating scip transformed vars to subscip original vars, and vice versa
305 * capture variables in SCIP and sub-SCIP
306 * catch global bound change events
307 */
308
309 SCIP_CALL( SCIPgetVarsData(heurdata->subscip, &subvars, &heurdata->nsubvars, NULL, NULL, NULL, NULL) );
310
311 SCIP_CALL( SCIPallocClearBlockMemoryArray(scip, &heurdata->var_subscip2scip, heurdata->nsubvars) );
312
313 heurdata->nvars = nvars;
314 SCIP_CALL( SCIPallocClearBlockMemoryArray(scip, &heurdata->var_scip2subscip, heurdata->nvars) );
315
316 /* we need to get all subscip variables, also those which are copies of fixed variables from the main scip
317 * therefore we iterate over the hashmap
318 */
319 for( i = 0; i < SCIPhashmapGetNEntries(varsmap); ++i )
320 {
321 SCIP_HASHMAPENTRY* entry;
322 entry = SCIPhashmapGetEntry(varsmap, i);
323 if( entry != NULL )
324 {
325 var = (SCIP_VAR*) SCIPhashmapEntryGetOrigin(entry);
326 subvar = (SCIP_VAR*) SCIPhashmapEntryGetImage(entry);
327 assert(subvar != NULL);
328 assert(SCIPvarGetProbindex(subvar) >= 0);
329 assert(SCIPvarGetProbindex(subvar) <= heurdata->nsubvars);
330
331 if( SCIPvarIsActive(var) )
332 {
333 assert(SCIPvarGetProbindex(var) <= heurdata->nvars);
334 assert(heurdata->var_scip2subscip[SCIPvarGetProbindex(var)] == NULL); /* assert that we have no mapping for this var yet */
335 heurdata->var_scip2subscip[SCIPvarGetProbindex(var)] = subvar;
336 }
337
338 assert(heurdata->var_subscip2scip[SCIPvarGetProbindex(subvar)] == NULL); /* assert that we have no mapping for this subvar yet */
339 heurdata->var_subscip2scip[SCIPvarGetProbindex(subvar)] = var;
340 }
341 }
342
343 for( i = 0; i < heurdata->nsubvars; ++i )
344 {
345 subvar = SCIPgetVars(heurdata->subscip)[i];
346 assert(SCIPvarGetProbindex(subvar) == i);
347 var = heurdata->var_subscip2scip[i];
348
349 SCIP_CALL( SCIPcaptureVar(scip, var) );
350 SCIP_CALL( SCIPcaptureVar(heurdata->subscip, subvar) );
351
352 assert(SCIPisFeasEQ(scip, SCIPvarGetLbGlobal(var), SCIPvarGetLbGlobal(subvar)));
353 assert(SCIPisFeasEQ(scip, SCIPvarGetUbGlobal(var), SCIPvarGetUbGlobal(subvar)));
354
355 SCIP_CALL( SCIPcatchVarEvent(scip, var, SCIP_EVENTTYPE_GBDCHANGED, heurdata->eventhdlr, (SCIP_EVENTDATA*)heurdata, NULL) );
356 }
357
358#ifndef NDEBUG
359 for( i = 0; i < heurdata->nvars; ++i )
360 {
361 assert(heurdata->var_scip2subscip[i] == NULL || (SCIP_VAR*)SCIPhashmapGetImage(varsmap, (void*)vars[i]) == heurdata->var_scip2subscip[i]);
362 }
363 for( i = 0; i < heurdata->nsubvars; ++i )
364 {
365 assert(heurdata->var_subscip2scip[i] != NULL);
366 assert((SCIP_VAR*)SCIPhashmapGetImage(varsmap, (void*)heurdata->var_subscip2scip[i]) == subvars[i]);
367 }
368#endif
369
370 /* do not need hashmap anymore */
371 SCIPhashmapFree(&varsmap);
372
373 /* do not abort subproblem on CTRL-C */
374 SCIP_CALL( SCIPsetBoolParam(heurdata->subscip, "misc/catchctrlc", FALSE) );
375
376 /* disable keeping solutions from one subscip solve for next solve (with usually different fixings) */
377 SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "limits/maxorigsol", 0) );
378
379#ifdef SCIP_DEBUG
380 /* for debugging, enable SCIP output */
381 SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "display/verblevel", 5) );
382#else
383 /* disable output to console */
384 SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "display/verblevel", 0) );
385#endif
386
387 /* reset some limits to default values, in case users changed them in main scip (SCIPcopy copies parameter values :-() */
388 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/absgap") );
389 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/bestsol") );
390 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/gap") );
391 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/restarts") );
392 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/solutions") );
393 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/time") );
394 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/totalnodes") );
395 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/primal") );
396 SCIP_CALL( SCIPresetParam(heurdata->subscip, "limits/dual") );
397
398 /* we remember here which way (continuous or not) we went, in case all binary and integer vars get fixed in root */
399 heurdata->continuous = SCIPgetNBinVars(heurdata->subscip) == 0 && SCIPgetNIntVars(heurdata->subscip) == 0;
400 if( !heurdata->continuous )
401 {
402 /* set presolve maxrounds and emphasis; always disable components presolver
403 * heuristics and separators were not copied into subscip, so should not need to switch off
404 */
405 if( !SCIPisParamFixed(heurdata->subscip, "presolving/maxrounds") )
406 {
407 SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "presolving/maxrounds", heurdata->maxpresolverounds) );
408 }
409 SCIP_CALL( SCIPsetPresolving(heurdata->subscip, (SCIP_PARAMSETTING)heurdata->presolveemphasis, TRUE) );
410 if( !SCIPisParamFixed(heurdata->subscip, "constraints/components/maxprerounds") )
411 {
412 SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "constraints/components/maxprerounds", 0) );
413 }
414 }
415 else
416 {
417 /* for continuous problems, disable presolve and move subscip into a stage where it has a NLP
418 * the only reason why we don't solve the NLP in the main SCIP is that we want global variable bounds for the NLP
419 */
420 SCIP_RETCODE retcode;
421
422 SCIP_CALL( SCIPtransformProb(heurdata->subscip) );
423
424 SCIP_CALL( SCIPsetPresolving(heurdata->subscip, SCIP_PARAMSETTING_OFF, TRUE) );
425 SCIP_CALL( SCIPpresolve(heurdata->subscip) );
426
427 if( SCIPgetStage(heurdata->subscip) != SCIP_STAGE_PRESOLVED || SCIPgetNVars(heurdata->subscip) == 0 )
428 {
429 /* presolve found problem infeasible, solved it, or stopped due to some limit
430 * all a bit strange, since problem should be the same as original, presolve was disabled, and we didn't set any limits
431 * we will give up and not run the heuristic
432 */
433 SCIP_CALL( freeSubSCIP(scip, heurdata) );
434 return SCIP_OKAY;
435 }
436
437 /* do initial solve, i.e., "solve" root node with node limit 0 (should do scip.c::initSolve and then stop immediately in solve.c::SCIPsolveCIP) */
438 SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 0LL) );
439 retcode = SCIPsolve(heurdata->subscip);
440
441 /* errors in solving the subproblem should not kill the overall solving process
442 * hence, the return code is caught and a warning is printed
443 */
444 if( retcode != SCIP_OKAY )
445 {
446 SCIPwarningMessage(scip, "Error while initializing subproblem in subnlp heuristic; sub-SCIP terminated with code <%d>\n", retcode);
447 SCIP_CALL( freeSubSCIP(scip, heurdata) );
448 return SCIP_OKAY;
449 }
450
451 /* If we are in stage "solved" (strange) or have no NLP (also strange), then do not run heuristic, too */
452 if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED || !SCIPisNLPConstructed(heurdata->subscip) )
453 {
454 SCIP_CALL( freeSubSCIP(scip, heurdata) );
455 return SCIP_OKAY;
456 }
457
458 assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVING);
459 assert(SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_NODELIMIT);
460 assert(SCIPisNLPConstructed(heurdata->subscip));
461 }
462
463 return SCIP_OKAY;
464}
465
466/** process variable global bound change event */
467static
468SCIP_DECL_EVENTEXEC(processVarEvent)
469{
470 SCIP_HEURDATA* heurdata;
471 SCIP_VAR* var;
472 SCIP_VAR* subvar;
473 int idx;
474
475 assert(scip != NULL);
476 assert(event != NULL);
477 assert(eventdata != NULL);
478 assert(eventhdlr != NULL);
479
480 heurdata = (SCIP_HEURDATA*)eventdata;
481 assert(heurdata != NULL);
482
483 var = SCIPeventGetVar(event);
484 assert(var != NULL);
485
486 idx = SCIPvarGetProbindex(var);
487 /* if event corresponds to an active variable, we can easily look up the corresponding subvar
488 * if it is an inactive variable that has been copied to the subproblem,
489 * then we need to check the subscip2scip mapping
490 * @todo we could do this faster if we keep the variables mapping from SCIPcopy around
491 */
492 if( idx >= 0 )
493 {
494 assert(idx < heurdata->nvars);
495
496 subvar = heurdata->var_scip2subscip[idx];
497 }
498 else
499 {
500 for( idx = 0; idx < heurdata->nsubvars; ++idx )
501 {
502 if( heurdata->var_subscip2scip[idx] == var )
503 break;
504 }
505 assert(idx < heurdata->nsubvars);
506 subvar = SCIPgetVars(heurdata->subscip)[idx];
507 }
508 assert(subvar != NULL);
509
510 if( SCIPeventGetType(event) & SCIP_EVENTTYPE_GLBCHANGED )
511 {
512 SCIP_CALL( SCIPchgVarLbGlobal(heurdata->subscip, subvar, SCIPeventGetNewbound(event)) );
513 }
514
515 if( SCIPeventGetType(event) & SCIP_EVENTTYPE_GUBCHANGED )
516 {
517 SCIP_CALL( SCIPchgVarUbGlobal(heurdata->subscip, subvar, SCIPeventGetNewbound(event)) );
518 }
519
520 return SCIP_OKAY;
521}
522
523/* creates a SCIP_SOL in our SCIP space out of the solution from NLP solver in sub-SCIP */
524static
525SCIP_RETCODE createSolFromNLP(
526 SCIP* scip, /**< SCIP data structure */
527 SCIP_HEUR* heur, /**< heuristic data structure */
528 SCIP_SOL** sol, /**< buffer to store solution value; if pointing to NULL, then a new solution is created, otherwise values in the given one are overwritten */
529 SCIP_HEUR* authorheur /**< the heuristic which should be registered as author of the solution */
530 )
531{
532 SCIP_HEURDATA* heurdata;
533 SCIP_VAR** vars;
534 int nvars;
535 SCIP_VAR* var;
536 SCIP_VAR* subvar;
537 SCIP_Real solval;
538 int i;
539
540 assert(scip != NULL);
541 assert(heur != NULL);
542 assert(sol != NULL);
543
544 heurdata = SCIPheurGetData(heur);
545 assert(heurdata != NULL);
546 assert(SCIPhasNLPSolution(heurdata->subscip));
547
548 if( *sol == NULL )
549 {
550 SCIP_CALL( SCIPcreateSol(scip, sol, authorheur) );
551 }
552 else
553 {
554 SCIPsolSetHeur(*sol, authorheur);
555 }
556
557 SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
558
559 assert(nvars >= heurdata->nvars);
560 for( i = 0; i < heurdata->nvars; ++i )
561 {
562 var = vars[i];
563 assert(var != NULL);
564 assert(SCIPvarIsActive(var)); /* SCIPgetVarsData should have given us only active vars */
565
566 subvar = heurdata->var_scip2subscip[i];
567 if( subvar == NULL )
568 solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
569 else
570 solval = SCIPvarGetNLPSol(subvar);
571
572 assert(solval != SCIP_INVALID); /*lint !e777*/
573 SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
574 }
575
576 for( ; i < nvars; ++i )
577 {
578 var = vars[i];
579 assert(var != NULL);
580 assert(SCIPvarIsActive(var)); /* SCIPgetVarsData should have given us only active vars */
581
582 solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
583 SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
584 }
585
586 return SCIP_OKAY;
587}
588
589/** creates SCIP solution from NLP and tries adding to SCIP or only checks feasibility */
590static
591SCIP_RETCODE processNLPSol(
592 SCIP* scip, /**< original SCIP data structure */
593 SCIP_HEUR* heur, /**< heuristic data structure */
594 SCIP_HEUR* authorheur, /**< the heuristic that should be the author of solution, if any */
595 SCIP_RESULT* result, /**< buffer to store result FOUNDSOL if a solution has been found and accepted */
596 SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
597 )
598{
599 SCIP_HEURDATA* heurdata;
600
601 assert(scip != NULL);
602 assert(heur != NULL);
603 assert(result != NULL);
604
605 heurdata = SCIPheurGetData(heur);
606 assert(heurdata != NULL);
607
608 assert(SCIPhasNLPSolution(heurdata->subscip));
609
610 if( resultsol == NULL )
611 {
612 /* resultsol NULL means we should try adding the sol to SCIP */
613 if( SCIPisLE(scip, SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip)) )
614 {
615 /* solution is feasible and should improve upper bound, so try adding it to SCIP */
616 SCIP_SOL* sol;
617 SCIP_Bool stored;
618
619 sol = NULL;
620 SCIP_CALL( createSolFromNLP(scip, heur, &sol, authorheur) );
621
622 heurdata->lastsol = sol; /* remember just the pointer so we might recognize if this solution comes back as startingpoint */
623#ifdef SCIP_DEBUG
624 /* print the infeasibilities to stdout */
625 SCIP_CALL( SCIPtrySolFree(scip, &sol, TRUE, TRUE, TRUE, FALSE, TRUE, &stored) );
626#else
627 SCIP_CALL( SCIPtrySolFree(scip, &sol, FALSE, FALSE, TRUE, FALSE, TRUE, &stored) );
628#endif
629
630 if( stored )
631 {
632 /* SCIP stored solution (yippi!), so we are done */
633 if( heurdata->nlpverblevel >= 1 )
634 {
635 SCIPinfoMessage(scip, NULL, "SCIP stored solution from NLP solve\n");
636 }
637 else
638 {
639 SCIPdebugMsg(scip, "SCIP stored solution from NLP solve\n");
640 }
641
642 *result = SCIP_FOUNDSOL;
643 }
644 else
645 {
646 if( heurdata->nlpverblevel >= 1 )
647 {
648 SCIPinfoMessage(scip, NULL, "solution reported by NLP solver not stored by SCIP\n");
649 }
650 else
651 {
652 SCIPdebugMsg(scip, "solution reported by NLP solver not stored by SCIP\n");
653 }
654 }
655 }
656 else if( heurdata->nlpverblevel >= 1 )
657 {
658 SCIPinfoMessage(scip, NULL, "subnlp solution objval %e is above the primal bound %e\n",
659 SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip));
660 }
661 }
662 else
663 {
664 /* only create a solution and pass it back in resultsol, do not add to SCIP */
665 SCIP_Bool feasible;
666
667 SCIP_CALL( createSolFromNLP(scip, heur, &resultsol, authorheur) );
668
669 heurdata->lastsol = resultsol;
670#ifdef SCIP_DEBUG
671 /* print the infeasibilities to stdout */
672 SCIP_CALL( SCIPcheckSol(scip, resultsol, TRUE, TRUE, TRUE, FALSE, TRUE, &feasible) );
673#else
674 SCIP_CALL( SCIPcheckSol(scip, resultsol, FALSE, FALSE, TRUE, FALSE, TRUE, &feasible) );
675#endif
676 if( feasible )
677 {
678 /* SCIP find solution feasible, so we are done */
679 if( heurdata->nlpverblevel >= 1 )
680 {
681 SCIPinfoMessage(scip, NULL, "solution reported by NLP solver feasible for SCIP\n");
682 }
683 else
684 {
685 SCIPdebugMsg(scip, "solution reported by NLP solver feasible for SCIP\n");
686 }
687 *result = SCIP_FOUNDSOL;
688 }
689 else
690 {
691 if( heurdata->nlpverblevel >= 1 )
692 {
693 SCIPinfoMessage(scip, NULL, "solution reported by NLP solver not feasible for SCIP\n");
694 }
695 else
696 {
697 SCIPdebugMsg(scip, "solution reported by NLP solver not feasible for SCIP\n");
698 }
699 }
700 }
701
702 return SCIP_OKAY;
703}
704
705/* creates a SCIP_SOL in our SCIP space out of the SCIP_SOL from a sub-SCIP */
706static
707SCIP_RETCODE createSolFromSubScipSol(
708 SCIP* scip, /**< SCIP data structure */
709 SCIP_HEUR* heur, /**< heuristic data structure */
710 SCIP_SOL** sol, /**< buffer to store solution value; if pointing to NULL, then a new solution is created, otherwise values in the given one are overwritten */
711 SCIP_SOL* subsol, /**< solution of sub-SCIP */
712 SCIP_HEUR* authorheur /**< the heuristic which should be registered as author of the solution */
713 )
714{
715 SCIP_HEURDATA* heurdata;
716 SCIP_VAR** vars;
717 int nvars;
718 SCIP_VAR* var;
719 SCIP_VAR* subvar;
720 SCIP_Real solval;
721 int i;
722
723 assert(scip != NULL);
724 assert(heur != NULL);
725 assert(sol != NULL);
726 assert(subsol != NULL);
727
728 heurdata = SCIPheurGetData(heur);
729 assert(heurdata != NULL);
730
731 if( *sol == NULL )
732 {
733 SCIP_CALL( SCIPcreateSol(scip, sol, authorheur) );
734 }
735 else
736 {
737 SCIPsolSetHeur(*sol, authorheur);
738 }
739
740 SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
741
742 assert(nvars >= heurdata->nvars);
743 for( i = 0; i < heurdata->nvars; ++i )
744 {
745 var = vars[i];
746 assert(var != NULL);
747 assert(SCIPvarIsActive(var));
748
749 subvar = heurdata->var_scip2subscip[i];
750 if( subvar == NULL )
751 solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
752 else
753 solval = SCIPgetSolVal(heurdata->subscip, subsol, subvar);
754
755 assert(solval != SCIP_INVALID); /*lint !e777*/
756 SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
757 }
758
759 for( ; i < nvars; ++i )
760 {
761 var = vars[i];
762 assert(var != NULL);
763 assert(SCIPvarIsActive(var));
764
765 solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
766 SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
767 }
768
769 return SCIP_OKAY;
770}
771
772/** finds an iteration limit */ /*lint --e{715}*/
773static
774int calcIterLimit(
775 SCIP* scip, /**< original SCIP data structure */
776 SCIP_HEURDATA* heurdata /**< heuristic data */
777 )
778{
779 /* if we hit more often an iterlimit than we were successful (termstatus=okay), then allow for more iterations:
780 * take twice the maximal iterusage on solves that hit the iterlimit
781 */
782 if( heurdata->nnlpsolvesiterlim > heurdata->nnlpsolvesokay )
783 return MAX(heurdata->itermin, 2 * heurdata->iterusediterlim); /*lint !e712*/
784
785 /* if we had sufficiently many successful solves, then take twice the average of previous iterusages on successful solves */
786 if( heurdata->nnlpsolvesokay >= heurdata->ninitsolves )
787 return MAX(heurdata->itermin, 2 * heurdata->iterusedokay / heurdata->nnlpsolvesokay); /*lint !e712*/
788
789 /* if we had too few successful solves, then still ensure that we allow for at least iterinit iterations */
790 if( heurdata->nnlpsolvesokay > 0 )
791 return MAX3(heurdata->itermin, heurdata->iterinit, 2 * heurdata->iterusedokay / heurdata->nnlpsolvesokay); /*lint !e712*/
792
793 /* if we had no successful solve so far and none that hit an iterlimit, e.g., we are at the first NLP solve, then use iterinit */
794 return MAX(heurdata->itermin, heurdata->iterinit);
795}
796
797/** solves the subNLP specified in subscip */
798static
799SCIP_RETCODE solveSubNLP(
800 SCIP* scip, /**< original SCIP data structure */
801 SCIP_HEUR* heur, /**< heuristic data structure */
802 SCIP_RESULT* result, /**< buffer to store result, DIDNOTFIND, FOUNDSOL, or CUTOFF */
803 SCIP_SOL* refpoint, /**< point to take fixation of discrete variables from, and startpoint for NLP solver; if NULL, then LP solution is used */
804 SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
805 )
806{
807 SCIP_HEURDATA* heurdata = SCIPheurGetData(heur);
808 SCIP_RETCODE retcode;
809 SCIP_Real* startpoint;
810 SCIP_VAR* var;
811 SCIP_VAR* subvar;
812 int i;
813 SCIP_HEUR* authorheur; /* the heuristic which will be the author of a solution, if found */
814 SCIP_Real timelimit;
815 SCIP_Bool expectinfeas;
816 SCIP_NLPSTATISTICS nlpstatistics;
817
818 assert(scip != NULL);
819 assert(heur != NULL);
820 assert(heurdata != NULL);
821 assert(result != NULL);
822 assert(SCIPisTransformed(heurdata->subscip));
823
824 /* get remaining SCIP solve time; if no time left, then stop */
825 SCIP_CALL( SCIPgetRealParam(scip, "limits/time", &timelimit) );
826 if( !SCIPisInfinity(scip, timelimit) )
827 {
828 timelimit -= SCIPgetSolvingTime(scip);
829 if( timelimit <= 0.0 )
830 return SCIP_OKAY;
831 }
832 /* set timelimit for NLP solve and in case presolve is unexpectedly expensive */
833 SCIP_CALL( SCIPsetRealParam(heurdata->subscip, "limits/time", timelimit) );
834
835 /* if the refpoint comes from a heuristic, then make it the author of a found solution,
836 * otherwise let the subNLP heuristic claim authorship
837 * TODO: I doubt that this has much effect; for the statistics, the number of solutions found by a heuristic
838 * seems to be computed as the increase in number of solutions before and after a heuristic is run
839 * check this and maybe change
840 */
841 if( refpoint == NULL || SCIPsolGetHeur(refpoint) == NULL )
842 authorheur = heur;
843 else
844 authorheur = SCIPsolGetHeur(refpoint);
845
846 if( !heurdata->continuous )
847 {
848 /* presolve sub-SCIP
849 * set node limit to 1 so that presolve can go
850 */
851 SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 1LL) );
852 SCIP_CALL( SCIPpresolve(heurdata->subscip) );
853
854 /* count one presolve round as on NLP iteration for now
855 * plus one extra for all the setup cost
856 * this is mainly to avoid that the primal heuristics runs all the time on instances that are solved in the subscip-presolve
857 */
858 heurdata->iterused += 1 + SCIPgetNPresolRounds(scip); /*lint !e776*/
859
860 if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED )
861 {
862 /* presolve probably found the subproblem infeasible */
863 SCIPdebugMsg(scip, "SCIP returned from presolve in stage solved with status %d and %d sols\n", SCIPgetStatus(heurdata->subscip), SCIPgetNSols(heurdata->subscip));
864 /* if presolve found subproblem infeasible, report this to caller by setting *result to cutoff */
865 if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
866 *result = SCIP_CUTOFF;
867 }
868 else if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVING )
869 {
870 /* presolve was stopped because some still existing limit was hit (e.g., memory) */
871 SCIPdebugMsg(scip, "SCIP returned from presolve in stage presolving with status %d and %d sols\n", SCIPgetStatus(heurdata->subscip), SCIPgetNSols(heurdata->subscip));
872 /* if presolve found subproblem infeasible, report this to caller by setting *result to cutoff */
873 if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
874 *result = SCIP_CUTOFF;
875 }
876 else
877 {
878 assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVED);
879
880 if( SCIPgetNVars(heurdata->subscip) > 0 )
881 {
882 /* do initial solve, i.e., "solve" root node with node limit 0 (should do scip.c::initSolve and then stop immediately in solve.c::SCIPsolveCIP) */
883 SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 0LL) );
884 retcode = SCIPsolve(heurdata->subscip);
885
886 /* If no NLP was constructed, then there were no nonlinearities after presolve.
887 * So we increase the nodelimit to 1 and hope that SCIP will find some solution to this probably linear subproblem.
888 */
889 if( retcode == SCIP_OKAY && SCIPgetStage(heurdata->subscip) != SCIP_STAGE_SOLVED && !SCIPisNLPConstructed(heurdata->subscip) )
890 {
891 SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 1LL) );
892 retcode = SCIPsolve(heurdata->subscip);
893 }
894 }
895 else
896 {
897 /* If all variables were removed by presolve, but presolve did not end with status SOLVED,
898 * then we run solve, still with nodelimit=1, and hope to find some (maybe trivial) solution.
899 */
900 retcode = SCIPsolve(heurdata->subscip);
901 }
902
903 /* errors in solving the subproblem should not kill the overall solving process
904 * hence, the return code is caught and a warning is printed
905 */
906 if( retcode != SCIP_OKAY )
907 {
908 SCIPwarningMessage(scip, "Error while solving subproblem in subnlp heuristic; sub-SCIP terminated with code <%d>\n", retcode);
909 return SCIP_OKAY;
910 }
911 }
912
913 /* we should either have variables, or the problem was trivial, in which case it should have been presolved or solved */
914 assert(SCIPgetNVars(heurdata->subscip) > 0 || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVING || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED);
915
916 SCIPdebug( SCIP_CALL( SCIPprintStatistics(heurdata->subscip, NULL) ); )
917
918 /* if sub-SCIP found solutions already, then pass them to main scip */
919 for( i = 0; i < SCIPgetNSols(heurdata->subscip); ++i )
920 {
921 if( resultsol == NULL )
922 {
923 SCIP_Bool stored;
924 SCIP_SOL* sol;
925
926 sol = NULL;
927 SCIP_CALL( createSolFromSubScipSol(scip, heur, &sol, SCIPgetSols(heurdata->subscip)[i], authorheur) );
928
929 heurdata->lastsol = sol; /* remember just the pointer so we might recognize if this solution comes back as startingpoint */
930 SCIP_CALL( SCIPtrySolFree(scip, &sol, FALSE, FALSE, TRUE, FALSE, TRUE, &stored) );
931 if( stored )
932 {
933 if( heurdata->nlpverblevel >= 1 )
934 {
935 SCIPinfoMessage(scip, NULL, "SCIP stored solution from sub-SCIP root node\n");
936 }
937 else
938 {
939 SCIPdebugMsg(scip, "SCIP stored solution from sub-SCIP root node\n");
940 }
941 *result = SCIP_FOUNDSOL;
942 break;
943 }
944 else
945 {
946 if( heurdata->nlpverblevel >= 1 )
947 {
948 SCIPinfoMessage(scip, NULL, "SCIP did not store sub-SCIP optimal solution\n");
949 }
950 else
951 {
952 SCIPdebugMsg(scip, "SCIP did not store sub-SCIP optimal solution\n");
953 }
954 }
955 }
956 else
957 {
958 SCIP_Bool feasible;
959
960 SCIP_CALL( createSolFromSubScipSol(scip, heur, &resultsol, SCIPgetSols(heurdata->subscip)[i], authorheur) );
961
962 heurdata->lastsol = resultsol;
963 SCIP_CALL( SCIPcheckSol(scip, resultsol, FALSE, FALSE, TRUE, FALSE, TRUE, &feasible) );
964 if( feasible )
965 {
966 if( heurdata->nlpverblevel >= 1 )
967 {
968 SCIPinfoMessage(scip, NULL, "SCIP solution from sub-SCIP root node is feasible\n");
969 }
970 else
971 {
972 SCIPdebugMsg(scip, "SCIP solution from sub-SCIP root node is feasible\n");
973 }
974 *result = SCIP_FOUNDSOL;
975 break;
976 }
977 else
978 {
979 if( heurdata->nlpverblevel >= 1 )
980 {
981 SCIPinfoMessage(scip, NULL, "SCIP solution from sub-SCIP root node is not feasible\n");
982 }
983 else
984 {
985 SCIPdebugMsg(scip, "SCIP solution from sub-SCIP root node is not feasible\n");
986 }
987 }
988 }
989 }
990
991 /* if subscip is infeasible here, we signal this to the caller */
992 if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
993 {
994 if( heurdata->nlpverblevel >= 1 )
995 {
996 SCIPinfoMessage(scip, NULL, "sub-SCIP detected infeasibility\n");
997 }
998 else
999 {
1000 SCIPdebugMsg(scip, "sub-SCIP detected infeasibility\n");
1001 }
1002
1003 assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED);
1004 *result = SCIP_CUTOFF;
1005 return SCIP_OKAY;
1006 }
1007
1008 /* if we stopped for some other reason, or there is no NLP, we also stop */
1009 if( SCIPgetStage(heurdata->subscip) <= SCIP_STAGE_PRESOLVED || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED || !SCIPisNLPConstructed(heurdata->subscip) )
1010 return SCIP_OKAY;
1011
1012 /* in most cases, the status should be nodelimit
1013 * in some cases, if the sub-SCIP is very easy, it may report optimal, so we do not need invoke an NLP solver
1014 * if the presolve found the problem infeasible, then there is no use in solving an NLP
1015 * if the user interrupted or a timelimit was reached, then we should also stop here
1016 * unbounded is very unlikely to happen, in most cases, it should have been concluded in the main scip already
1017 */
1018 switch( SCIPgetStatus(heurdata->subscip) )
1019 {
1020 case SCIP_STATUS_NODELIMIT:
1021 break; /* this is the status that is most likely happening */
1022 case SCIP_STATUS_TOTALNODELIMIT:
1023 case SCIP_STATUS_STALLNODELIMIT:
1024 case SCIP_STATUS_GAPLIMIT:
1025 case SCIP_STATUS_SOLLIMIT:
1026 case SCIP_STATUS_BESTSOLLIMIT:
1027 /* these should not happen, but if one does, it's safe to return */
1028 SCIPABORT(); /*lint -fallthrough*/
1029 case SCIP_STATUS_OPTIMAL:
1030 case SCIP_STATUS_INFEASIBLE:
1031 case SCIP_STATUS_USERINTERRUPT:
1032 case SCIP_STATUS_TIMELIMIT:
1033 case SCIP_STATUS_MEMLIMIT:
1034 case SCIP_STATUS_UNBOUNDED:
1035 case SCIP_STATUS_INFORUNBD:
1036 return SCIP_OKAY;
1037 default:
1038 SCIPerrorMessage("unexpected status of sub-SCIP: <%d>\n", SCIPgetStatus(heurdata->subscip));
1039 return SCIP_ERROR;
1040 } /*lint !e788*/
1041 }
1042 else
1043 {
1044 /* for continuous problem, createSubSCIP() should have put us into a state where we can invoke the NLP solver */
1045 assert(SCIPisNLPConstructed(heurdata->subscip));
1046 assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVING);
1047 assert(SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_NODELIMIT);
1048 }
1049
1050 /* set starting values (=refpoint, if not NULL; otherwise LP solution (or pseudo solution)) */
1051 SCIP_CALL( SCIPallocBufferArray(scip, &startpoint, SCIPgetNNLPVars(heurdata->subscip)) );
1052
1053 if( heurdata->nlpverblevel >= 3 )
1054 {
1055 SCIPinfoMessage(scip, NULL, "set NLP starting point\n");
1056 }
1057
1058 for( i = 0; i < SCIPgetNNLPVars(heurdata->subscip); ++i )
1059 {
1060 SCIP_Real scalar;
1061 SCIP_Real constant;
1062
1063 subvar = SCIPgetNLPVars(heurdata->subscip)[i];
1064
1065 /* gets corresponding original variable */
1066 scalar = 1.0;
1067 constant = 0.0;
1068 SCIP_CALL( SCIPvarGetOrigvarSum(&subvar, &scalar, &constant) );
1069 if( subvar == NULL )
1070 {
1071 startpoint[i] = constant;
1072
1073 if( heurdata->nlpverblevel >= 3 && !SCIPisZero(heurdata->subscip, startpoint[i]) )
1074 {
1075 SCIPinfoMessage(scip, NULL, "%s = %e\n", SCIPvarGetName(SCIPgetNLPVars(heurdata->subscip)[i]), startpoint[i]);
1076 }
1077
1078 continue;
1079 }
1080
1081 assert(SCIPvarGetProbindex(subvar) >= 0);
1082 assert(SCIPvarGetProbindex(subvar) < heurdata->nsubvars);
1083 var = heurdata->var_subscip2scip[SCIPvarGetProbindex(subvar)];
1084 if( var == NULL || REALABS(SCIPgetSolVal(scip, refpoint, var)) > 1.0e+12 )
1085 startpoint[i] = MIN(MAX(0.0, SCIPvarGetLbGlobal(subvar)), SCIPvarGetUbGlobal(subvar)); /*lint !e666*/
1086 else
1087 /* scalar*subvar+constant corresponds to nlpvar[i], so nlpvar[i] gets value scalar*varval+constant */
1088 startpoint[i] = scalar * SCIPgetSolVal(scip, refpoint, var) + constant;
1089
1090 if( heurdata->nlpverblevel >= 3 && !SCIPisZero(heurdata->subscip, startpoint[i]) )
1091 {
1092 SCIPinfoMessage(scip, NULL, "%s = %e\n", SCIPvarGetName(SCIPgetNLPVars(heurdata->subscip)[i]), startpoint[i]);
1093 }
1094 }
1095 SCIP_CALL( SCIPsetNLPInitialGuess(heurdata->subscip, startpoint) );
1096
1097 SCIPfreeBufferArray(scip, &startpoint);
1098
1099 *result = SCIP_DIDNOTFIND;
1100
1101 /* if we had many (fraction > expectinfeas) infeasible NLPs, then tell NLP solver to expect an infeasible problem */
1102 expectinfeas = FALSE;
1103 if( heurdata->expectinfeas == 0.0 ) /* to keep original behavior on default settings */
1104 expectinfeas = TRUE;
1105 else if( heurdata->nnlpsolvesokay > heurdata->ninitsolves && heurdata->nnlpsolvesinfeas > heurdata->expectinfeas * heurdata->nnlpsolvesokay )
1106 expectinfeas = TRUE;
1107
1108 /* let the NLP solver do its magic */
1109 SCIPdebugMsg(scip, "start NLP solve with iteration limit %d\n", calcIterLimit(scip, heurdata));
1110 SCIP_CALL( SCIPsolveNLP(heurdata->subscip,
1111 .iterlimit = calcIterLimit(scip, heurdata),
1112 .opttol = heurdata->opttol,
1113 .feastol = heurdata->feastol,
1114 .verblevel = (unsigned short)heurdata->nlpverblevel,
1115 .expectinfeas = expectinfeas
1116 ) ); /*lint !e666*/
1117
1118 SCIPdebugMsg(scip, "NLP solver returned with termination status %d and solution status %d, objective value is %g\n",
1119 SCIPgetNLPTermstat(heurdata->subscip), SCIPgetNLPSolstat(heurdata->subscip), SCIPgetNLPObjval(heurdata->subscip));
1120
1121 /* add NLP solve statistics from subscip to main SCIP, so they show up in final statistics
1122 * for continuous problems, we also ask to reset statistics, since we do not retransform subSCIP in the next run (which would reset all stats)
1123 * (merging statistics once in exitsol is too late, since they may be printed before)
1124 */
1125 SCIPmergeNLPIStatistics(heurdata->subscip, scip, heurdata->continuous);
1126
1127 if( SCIPgetNLPTermstat(heurdata->subscip) >= SCIP_NLPTERMSTAT_OUTOFMEMORY )
1128 {
1129 /* oops, something did not go well at all */
1130 if( heurdata->nlpverblevel >= 1 )
1131 {
1132 SCIPinfoMessage(scip, NULL, "NLP solver in subNLP heuristic for problem <%s> returned with bad termination status %d.\n",
1133 SCIPgetProbName(scip), SCIPgetNLPTermstat(heurdata->subscip));
1134 }
1135
1136 ++(heurdata->nseriousnlpierror);
1137 SCIPverbMessage(scip, SCIP_VERBLEVEL_MINIMAL, NULL,
1138 "NLP solver in subNLP heuristic for problem <%s> returned with bad termination status %d. This was the %d%s successive time.\n",
1139 SCIPgetProbName(scip), SCIPgetNLPTermstat(heurdata->subscip), heurdata->nseriousnlpierror,
1140 heurdata->nseriousnlpierror == 1 ? "st" : heurdata->nseriousnlpierror == 2 ? "nd" : heurdata->nseriousnlpierror == 3 ? "rd" : "th");
1141 if( heurdata->nseriousnlpierror >= 5 )
1142 {
1143 SCIPverbMessage(scip, SCIP_VERBLEVEL_MINIMAL, NULL, "Will not run subNLP heuristic again for this run.\n");
1144 SCIP_CALL( freeSubSCIP(scip, heurdata) );
1145 }
1146 return SCIP_OKAY;
1147 }
1148 heurdata->nseriousnlpierror = 0;
1149
1150 SCIP_CALL( SCIPgetNLPStatistics(heurdata->subscip, &nlpstatistics) );
1151
1152 SCIPdebugMsg(scip, "NLP solver used %d iterations and %g seconds; violation cons %g, bounds %g\n",
1153 nlpstatistics.niterations, nlpstatistics.totaltime, nlpstatistics.consviol, nlpstatistics.boundviol);
1154
1155 heurdata->iterused += nlpstatistics.niterations;
1156 ++heurdata->nnlpsolves;
1157 if( SCIPgetNLPTermstat(heurdata->subscip) == SCIP_NLPTERMSTAT_OKAY )
1158 {
1159 ++heurdata->nnlpsolvesokay;
1160 heurdata->iterusedokay += nlpstatistics.niterations;
1161
1162 if( (SCIPgetNLPSolstat(heurdata->subscip) == SCIP_NLPSOLSTAT_GLOBINFEASIBLE) || (SCIPgetNLPSolstat(heurdata->subscip) == SCIP_NLPSOLSTAT_LOCINFEASIBLE) )
1163 ++heurdata->nnlpsolvesinfeas;
1164 }
1165 else if( SCIPgetNLPTermstat(heurdata->subscip) == SCIP_NLPTERMSTAT_ITERLIMIT )
1166 {
1167 ++heurdata->nnlpsolvesiterlim;
1168 heurdata->iterusediterlim = MAX(heurdata->iterusediterlim, nlpstatistics.niterations);
1169 }
1170
1171 if( SCIPgetNLPSolstat(heurdata->subscip) > SCIP_NLPSOLSTAT_FEASIBLE )
1172 return SCIP_OKAY;
1173
1174 /* create SCIP solution, check whether feasible, and try adding to SCIP (if resultsol==NULL) */
1175 SCIP_CALL( processNLPSol(scip, heur, authorheur, result, resultsol) );
1176
1177 if( *result == SCIP_FOUNDSOL || !SCIPisLE(scip, SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip)) )
1178 return SCIP_OKAY;
1179
1180 /* if solution was not added to SCIP, then either
1181 * - the objective function value was not good enough,
1182 * - the NLP was missing some constraints of the original CIP, or
1183 * - the solution is feasible for the presolved CIP, but slightly infeasible for the unpresolved problem
1184 *
1185 * The first case we can check easily (see if() above).
1186 * For the last case, we try whether tightening the feasibility tolerance for the NLP solve may help.
1187 * If that doesn't help, we guess that we are in the second case and will not try a tighter feastol anymore.
1188 */
1189
1190 /* if we tried with a tighter feastol before, but solution was still not accepted, then don't try again */
1191 if( heurdata->tighterfeastolfailed )
1192 return SCIP_OKAY;
1193
1194 /* if resolve with tighter feastol is disabled, then don't do anything */
1195 if( heurdata->feastolfactor == 1.0 )
1196 return SCIP_OKAY;
1197
1198 /* if we have already used a tighter feastol, then give up */
1199 if( heurdata->feastol < SCIPfeastol(scip) )
1200 return SCIP_OKAY;
1201
1202 /* if original CIP is continuous, then we have not done any presolve, so it shouldn't have caused problems */
1203 if( heurdata->continuous )
1204 return SCIP_OKAY;
1205
1206 /* if solution is NLP-feasible for a tightened tolerance already, then there is no use in resolving with that tighter feastol */
1207 if( MAX(nlpstatistics.consviol, nlpstatistics.boundviol) <= heurdata->feastolfactor * heurdata->feastol )
1208 return SCIP_OKAY;
1209
1210 /* let the NLP solver redo its magic
1211 * as iterlimit, we use the number of iterations it took for the first solve, or itermin
1212 */
1213 SCIPdebugMsg(scip, "start NLP solve with iteration limit %d\n", calcIterLimit(scip, heurdata));
1214 SCIP_CALL( SCIPsolveNLP(heurdata->subscip,
1215 .iterlimit = MAX(heurdata->itermin, nlpstatistics.niterations),
1216 .opttol = heurdata->opttol,
1217 .feastol = heurdata->feastolfactor * heurdata->feastol,
1218 .verblevel = (unsigned short)heurdata->nlpverblevel,
1219 .warmstart = TRUE
1220 ) ); /*lint !e666*/
1221
1222 SCIPdebugMsg(scip, "NLP solver returned with termination status %d and solution status %d, objective value is %g\n",
1223 SCIPgetNLPTermstat(heurdata->subscip), SCIPgetNLPSolstat(heurdata->subscip), SCIPgetNLPObjval(heurdata->subscip));
1224
1225 /* add NLP solve statistics from subscip to main SCIP again, so they show up in final statistics */
1226 SCIPmergeNLPIStatistics(heurdata->subscip, scip, heurdata->continuous);
1227
1228 /* some serious problem: just pretend it didn't happen */
1229 if( SCIPgetNLPTermstat(heurdata->subscip) >= SCIP_NLPTERMSTAT_OUTOFMEMORY )
1230 return SCIP_OKAY;
1231
1232 SCIP_CALL( SCIPgetNLPStatistics(heurdata->subscip, &nlpstatistics) );
1233 SCIPdebugMsg(scip, "NLP solver used %d iterations and %g seconds; violation cons %g, bounds %g\n",
1234 nlpstatistics.niterations, nlpstatistics.totaltime, nlpstatistics.consviol, nlpstatistics.boundviol);
1235
1236 /* we account only the extra iterations for this unusual NLP solve, but don't add anything else to our statistics (nnlpsolved, etc) */
1237 heurdata->iterused += nlpstatistics.niterations;
1238
1239 /* if failed to get a feasible NLP solution now, then nothing to do */
1240 if( SCIPgetNLPSolstat(heurdata->subscip) > SCIP_NLPSOLSTAT_FEASIBLE )
1241 return SCIP_OKAY;
1242
1243 SCIP_CALL( processNLPSol(scip, heur, authorheur, result, resultsol) );
1244
1245 /* if successful, then use tighter feastol for all NLP solves from now on
1246 * if still not accepted, then don't try this again
1247 * (maybe the NLP is incomplete; we could give up on running this heur completely, but for now let the successrate factor in heurExec take care of running it less often)
1248 */
1249 if( *result == SCIP_FOUNDSOL )
1250 heurdata->feastol *= heurdata->feastolfactor;
1251 else
1252 heurdata->tighterfeastolfailed = TRUE;
1253
1254 return SCIP_OKAY;
1255}
1256
1257
1258/** adds a set covering or bound disjunction constraint to the original problem */
1259static
1260SCIP_RETCODE forbidFixation(
1261 SCIP* scip, /**< SCIP data structure */
1262 SCIP_HEURDATA* heurdata /**< heuristic data */
1263 )
1264{
1265 SCIP_VAR** subvars;
1266 int nsubvars;
1267 int nsubbinvars;
1268 int nsubintvars;
1269 SCIP_VAR* var;
1270 SCIP_VAR* subvar;
1271 SCIP_CONS* cons;
1272 SCIP_VAR** consvars;
1273 int nconsvars;
1274 char name[SCIP_MAXSTRLEN];
1275 int i;
1276 SCIP_Real fixval;
1277
1278 assert(scip != NULL);
1279
1280 SCIP_CALL( SCIPgetOrigVarsData(heurdata->subscip, &subvars, &nsubvars, &nsubbinvars, &nsubintvars, NULL, NULL) );
1281 assert(nsubvars == heurdata->nsubvars);
1282
1283 if( nsubbinvars == 0 && nsubintvars == 0 )
1284 {
1285 /* If we did not fix any discrete variables but found the "sub"CIP infeasible, then also the CIP is infeasible. */
1286 SCIPdebugMsg(scip, "heur_subnlp found subCIP infeasible after fixing no variables, something is strange here...\n");
1287 return SCIP_OKAY;
1288 }
1289
1290 /* initialize */
1291 cons = NULL;
1292 consvars = NULL;
1293
1294 /* create constraint name */
1295 (void) SCIPsnprintf(name, SCIP_MAXSTRLEN, "subnlp_cutoff");
1296
1297 /* if all discrete variables in the CIP are binary, then we create a set covering constraint
1298 * sum_{x_i fixed at 0} x_i + sum_{x_i fixed at 1} ~x_i >= 1
1299 */
1300 if( nsubintvars == 0 )
1301 {
1302 /* allocate memory for constraint variables */
1303 SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nsubbinvars) );
1304
1305 /* get fixations of discrete variables
1306 * to be sure, we take the values that were put into the subCIP before
1307 */
1308 nconsvars = 0;
1309 for( i = nsubbinvars - 1; i >= 0; --i )
1310 {
1311 subvar = subvars[i];
1312 assert(SCIPvarGetProbindex(subvar) == i);
1313
1314 var = heurdata->var_subscip2scip[i];
1315 assert(var != NULL || SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetUbGlobal(subvar))); /* otherwise we should have exited in the variable fixation loop */
1316 if( var == NULL )
1317 continue;
1318
1319 fixval = SCIPvarGetLbGlobal(subvar);
1320 assert(fixval == SCIPvarGetUbGlobal(subvar)); /* variable should be fixed in sub-SCIP */ /*lint !e777*/
1321 assert(fixval == 0.0 || fixval == 1.0); /* we have rounded values before fixing */
1322
1323 if( fixval == 0.0 )
1324 {
1325 /* variable fixed at lower bound */
1326 consvars[nconsvars] = var;
1327 }
1328 else
1329 {
1330 SCIP_CALL( SCIPgetNegatedVar(scip, var, &consvars[nconsvars]) );
1331 }
1332
1333 ++nconsvars;
1334 }
1335
1336 /* create conflict constraint
1337 * In undercover, ConsLogicor is used, since then the inequality is not added to the LP.
1338 * However, I may want to use Setcover to avoid that the same fixing is computed by some LP based heuristic again.
1339 */
1340 SCIP_CALL( SCIPcreateConsSetcover(scip, &cons, name, nconsvars, consvars,
1341 FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
1342 }
1343 else
1344 {
1345 /* if there are also integer variable, then create a bound disjunction constraint
1346 * x_1 >= fixval_1 + 1 || x_1 <= fixval_1 - 1 || x_2 >= fixval_2 + 1 || x_2 <= fixval_2 - 1 || ...
1347 */
1348 SCIP_BOUNDTYPE* boundtypes;
1349 SCIP_Real* bounds;
1350
1351 /* allocate memory for constraint variables, boundtypes, and bounds
1352 * (there should be at most two literals for each integer variable)
1353 */
1354 SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nsubbinvars + 2*nsubintvars) );
1355 SCIP_CALL( SCIPallocBufferArray(scip, &boundtypes, nsubbinvars + 2*nsubintvars) );
1356 SCIP_CALL( SCIPallocBufferArray(scip, &bounds, nsubbinvars + 2*nsubintvars) );
1357
1358 /* get fixations of discrete variables
1359 * to be sure, we take the values that were put into the subCIP before
1360 */
1361 nconsvars = 0;
1362 for( i = nsubbinvars + nsubintvars - 1; i >= 0; --i )
1363 {
1364 subvar = subvars[i];
1365 assert(SCIPvarGetProbindex(subvar) == i);
1366
1367 var = heurdata->var_subscip2scip[i];
1368 assert(var != NULL || SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetUbGlobal(subvar))); /* otherwise we should have exited in the variable fixation loop */
1369
1370 if( var == NULL )
1371 continue;
1372
1373 fixval = SCIPvarGetLbGlobal(subvar);
1374 assert(fixval == SCIPvarGetUbGlobal(subvar)); /* variable should be fixed in sub-SCIP */ /*lint !e777*/
1375 assert(SCIPceil(scip, fixval - 0.5) == fixval); /* we have rounded values before fixing */ /*lint !e777*/
1376 assert(SCIPvarGetType(var) != SCIP_VARTYPE_BINARY || SCIPvarGetLbGlobal(var) == fixval || SCIPvarGetUbGlobal(var) == fixval); /* for binaries, the fixval should be either 0.0 or 1.0 */ /*lint !e777*/
1377
1378 if( SCIPvarGetLbGlobal(var) < fixval )
1379 {
1380 assert(nconsvars < nsubbinvars + 2*nsubintvars);
1381
1382 /* literal x_i <= fixval-1 */
1383 boundtypes[nconsvars] = SCIP_BOUNDTYPE_UPPER;
1384 bounds[nconsvars] = fixval - 1.0;
1385 consvars[nconsvars] = var;
1386 ++nconsvars;
1387 }
1388
1389 if( SCIPvarGetUbGlobal(var) > fixval )
1390 {
1391 assert(nconsvars < nsubbinvars + 2*nsubintvars);
1392
1393 /* literal x_i >= fixval+1 */
1394 boundtypes[nconsvars] = SCIP_BOUNDTYPE_LOWER;
1395 bounds[nconsvars] = fixval + 1.0;
1396 consvars[nconsvars] = var;
1397 ++nconsvars;
1398 }
1399 }
1400
1401 /* create conflict constraint */
1402 SCIP_CALL( SCIPcreateConsBounddisjunction(scip, &cons, name, nconsvars, consvars, boundtypes, bounds,
1403 FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
1404
1405 SCIPfreeBufferArray(scip, &bounds);
1406 SCIPfreeBufferArray(scip, &boundtypes);
1407 SCIPfreeBufferArray(scip, &consvars);
1408 }
1409
1410 /* add and release constraint if created successfully */
1411 if( cons != NULL )
1412 {
1413 SCIPdebugMsg(scip, "adding constraint to forbid fixation in main problem\n");
1414 /* SCIPdebugPrintCons(scip, cons, NULL); */
1415 SCIP_CALL( SCIPaddCons(scip, cons) );
1416 SCIP_CALL( SCIPreleaseCons(scip, &cons) );
1417 }
1418
1419 /* free memory */
1420 SCIPfreeBufferArrayNull(scip, &consvars);
1421
1422 return SCIP_OKAY;
1423}
1424
1425
1426/*
1427 * Callback methods of primal heuristic
1428 */
1429
1430/** copy method for primal heuristic plugins (called when SCIP copies plugins) */
1431static
1432SCIP_DECL_HEURCOPY(heurCopySubNlp)
1433{ /*lint --e{715}*/
1434 assert(scip != NULL);
1435 assert(heur != NULL);
1436 assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
1437
1438 /* call inclusion method of primal heuristic */
1439 SCIP_CALL( SCIPincludeHeurSubNlp(scip) );
1440
1441 return SCIP_OKAY;
1442}
1443
1444/** destructor of primal heuristic to free user data (called when SCIP is exiting) */
1445static
1446SCIP_DECL_HEURFREE(heurFreeSubNlp)
1447{
1448 SCIP_HEURDATA* heurdata;
1449 assert(scip != NULL);
1450 assert(heur != NULL);
1451
1452 heurdata = SCIPheurGetData(heur);
1453 assert(heurdata != NULL);
1454 assert(heurdata->subscip == NULL);
1455 assert(heurdata->var_subscip2scip == NULL);
1456 assert(heurdata->var_scip2subscip == NULL);
1457 assert(heurdata->startcand == NULL);
1458
1459 SCIPfreeBlockMemory(scip, &heurdata);
1460
1461 return SCIP_OKAY;
1462}
1463
1464/** initialization method of primal heuristic (called after problem was transformed) */
1465static
1466SCIP_DECL_HEURINIT(heurInitSubNlp)
1467{ /*lint --e{715}*/
1468 SCIP_HEURDATA* heurdata;
1469
1470 assert(scip != NULL);
1471 assert(heur != NULL);
1472
1473 heurdata = SCIPheurGetData(heur);
1474 assert(heurdata != NULL);
1475 assert(heurdata->subscip == NULL);
1476
1477 /* reset or initialize some flags and counters */
1478 heurdata->feastol = SCIPfeastol(scip);
1479 heurdata->tighterfeastolfailed = FALSE;
1480 heurdata->triedsetupsubscip = FALSE;
1481 heurdata->nseriousnlpierror = 0;
1482 heurdata->iterused = 0;
1483 heurdata->iterusedokay = 0;
1484 heurdata->iterusediterlim = 0;
1485 heurdata->nnlpsolves = 0;
1486 heurdata->nnlpsolvesokay = 0;
1487 heurdata->nnlpsolvesiterlim = 0;
1488 heurdata->nnlpsolvesinfeas = 0;
1489
1490 return SCIP_OKAY;
1491}
1492
1493/** solving process initialization method of primal heuristic (called when branch and bound process is about to begin) */
1494static
1495SCIP_DECL_HEURINITSOL(heurInitsolSubNlp)
1496{
1497 assert(scip != NULL);
1498 assert(heur != NULL);
1499
1500 /* if the heuristic is called at the root node, we want to be called directly after the initial root LP solve */
1501 if( SCIPheurGetFreqofs(heur) == 0 )
1502 SCIPheurSetTimingmask(heur, SCIP_HEURTIMING_DURINGLPLOOP | HEUR_TIMING);
1503
1504 return SCIP_OKAY;
1505}
1506
1507/** solving process deinitialization method of primal heuristic (called before branch and bound process data is freed) */
1508static
1509SCIP_DECL_HEUREXITSOL(heurExitsolSubNlp)
1510{
1511 SCIP_HEURDATA* heurdata;
1512 assert(scip != NULL);
1513 assert(heur != NULL);
1514
1515 /* get heuristic's data */
1516 heurdata = SCIPheurGetData(heur);
1517 assert(heurdata != NULL);
1518
1519 if( heurdata->subscip != NULL )
1520 {
1521 SCIP_CALL( freeSubSCIP(scip, heurdata) );
1522 heurdata->triedsetupsubscip = FALSE;
1523 }
1524
1525 /* free start candidate */
1526 if( heurdata->startcand != NULL )
1527 {
1528 SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1529 }
1530
1531 SCIPheurSetTimingmask(heur, HEUR_TIMING);
1532
1533 return SCIP_OKAY;
1534}
1535
1536
1537/** execution method of primal heuristic */
1538static
1539SCIP_DECL_HEUREXEC(heurExecSubNlp)
1540{ /*lint --e{666,715}*/
1541 SCIP_HEURDATA* heurdata;
1542 SCIP_Bool runheur;
1543 SCIP_Real itercontingent;
1544
1545 assert(scip != NULL);
1546 assert(heur != NULL);
1547
1548 /* obviously, we did not do anything yet */
1549 *result = SCIP_DIDNOTRUN;
1550
1551 /* get heuristic's data */
1552 heurdata = SCIPheurGetData(heur);
1553 assert(heurdata != NULL);
1554
1555 /* if triedsetupsubscip and keepcopy and subscip == NULL, then we tried to setup a subSCIP before, but failed due to some serious error
1556 * thus, we should do not need to try again
1557 *
1558 * otherwise, we continue and let SCIPapplyHeurSubNlp try to create subscip
1559 */
1560 if( heurdata->subscip == NULL && heurdata->keepcopy && heurdata->triedsetupsubscip )
1561 return SCIP_OKAY;
1562
1563 /* before we run the heuristic for the first time, check whether we want to run the heuristic at all */
1564 if( SCIPheurGetNCalls(heur) == 0 )
1565 {
1566 SCIP_CALL( runHeuristic(scip, &runheur) );
1567 if( !runheur )
1568 return SCIP_OKAY;
1569 }
1570
1571 if( heurdata->startcand == NULL )
1572 {
1573 /* if no start candidate is given, we consider the LP solution of the current node */
1574
1575 /* however, if the node was already detected to be infeasible, then there is no point to look at its LP solution */
1576 if( nodeinfeasible )
1577 return SCIP_OKAY;
1578
1579 /* at least if we are not called the first time, we call the heuristic only if an optimal LP solution is available
1580 * if we are called the first time and the LP is unbounded, then we are quite desperate and still give the NLP a try
1581 */
1582 if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
1583 {
1584 if( SCIPgetNNodes(scip) > 1 || SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_UNBOUNDEDRAY )
1585 {
1586 *result = SCIP_DELAYED;
1587 SCIPdebugMsg(scip, "NLP heuristic delayed because no start candidate given and no LP solution available; LP status = %d\n", SCIPgetLPSolstat(scip));
1588 return SCIP_OKAY;
1589 }
1590 else
1591 {
1592 SCIPdebugMsg(scip, "LP is unbounded in root node, so we are quite desperate; run NLP heuristic and pray\n");
1593 }
1594 }
1595 else if( SCIPgetNLPBranchCands(scip) > 0 )
1596 {
1597 /* only call heuristic, if there are no fractional variables */
1598 *result = SCIP_DELAYED;
1599 SCIPdebugMsg(scip, "NLP heuristic delayed because no start candidate given and current LP solution is fractional\n");
1600 return SCIP_OKAY;
1601 }
1602 else if( !SCIPisInfinity(scip, SCIPgetPrimalbound(scip)) && SCIPisEQ(scip, SCIPgetLocalDualbound(scip), SCIPgetPrimalbound(scip)) )
1603 {
1604 /* only call heuristic, if there is still room for improvement in the current node */
1605 SCIPdebugMsg(scip, "NLP heuristic delayed because lower and upper bound coincide in current node\n");
1606 return SCIP_OKAY;
1607 }
1608 SCIPdebugMsg(scip, "using current LP solution as startcand\n");
1609 }
1610 else
1611 {
1612 SCIPdebugMsg(scip, "have startcand from heur %s\n", SCIPsolGetHeur(heurdata->startcand) ? SCIPheurGetName(SCIPsolGetHeur(heurdata->startcand)) : "NULL");
1613 }
1614
1615 /* check if enough nodes have been processed so that we want to run the heuristic again */
1616
1617 /* compute the contingent on number of iterations that the NLP solver is allowed to use
1618 * we make it depending on the current number of processed nodes
1619 */
1620 itercontingent = heurdata->nodesfactor * (SCIPgetNNodes(scip) + heurdata->nodesoffset);
1621 /* weight by previous success of heuristic if we have been running already
1622 * require at least ninitsolves many runs that didn't run into the NLP iterlimit
1623 * (so if we are still in the phase of finding a good iterlimit, do not consider success rate so far)
1624 */
1625 if( heurdata->successrateexp > 0.0 && SCIPheurGetNCalls(heur) - heurdata->nnlpsolvesiterlim >= heurdata->ninitsolves )
1626 itercontingent *= pow((SCIPheurGetNSolsFound(heur) + 1.0) / (SCIPheurGetNCalls(heur) + 1.0), heurdata->successrateexp);
1627 /* subtract the number of iterations used for all NLP solves so far */
1628 itercontingent -= heurdata->iterused;
1629
1630 /* check whether the itercontingent is sufficient for the iteration limit we would use */
1631 if( itercontingent < calcIterLimit(scip, heurdata) )
1632 {
1633 /* not enough iterations left to start NLP solver */
1634 SCIPdebugMsg(scip, "skip NLP heuristic; contingent=%f; iterlimit=%d; success ratio=%g\n",
1635 itercontingent, calcIterLimit(scip, heurdata), pow((SCIPheurGetNSolsFound(heur) + 1.0) / (SCIPheurGetNCalls(heur) + 1.0), heurdata->successrateexp));
1636 return SCIP_OKAY;
1637 }
1638
1639 /* so far we have not found any solution, but now we are willing to search for one */
1640 *result = SCIP_DIDNOTFIND;
1641
1642 if( heurdata->nlpverblevel >= 1 )
1643 {
1644 SCIPinfoMessage(scip, NULL, "calling subnlp heuristic\n");
1645 }
1646
1647 SCIP_CALL( SCIPapplyHeurSubNlp(scip, heur, result, heurdata->startcand, NULL) );
1648
1649 /* SCIP does not like cutoff as return, so we say didnotfind, since we did not find a solution */
1650 if( *result == SCIP_CUTOFF )
1651 *result = SCIP_DIDNOTFIND;
1652
1653 /* forget startcand */
1654 if( heurdata->startcand != NULL )
1655 {
1656 SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1657 }
1658
1659 /* reset timing, if it was changed temporary (at the root node) */
1660 if( heurtiming != HEUR_TIMING )
1661 SCIPheurSetTimingmask(heur, HEUR_TIMING);
1662
1663 return SCIP_OKAY;
1664}
1665
1666
1667/*
1668 * primal heuristic specific interface methods
1669 */
1670
1671/** creates the NLP local search primal heuristic and includes it in SCIP */
1672SCIP_RETCODE SCIPincludeHeurSubNlp(
1673 SCIP* scip /**< SCIP data structure */
1674 )
1675{
1676 SCIP_HEURDATA* heurdata;
1677 SCIP_HEUR* heur;
1678
1679 /* create Nlp primal heuristic data */
1680 SCIP_CALL( SCIPallocBlockMemory(scip, &heurdata) );
1681 BMSclearMemory(heurdata);
1682
1683 /* include variable event handler */
1684 heurdata->eventhdlr = NULL;
1685 SCIP_CALL( SCIPincludeEventhdlrBasic(scip, &heurdata->eventhdlr, HEUR_NAME, "propagates a global bound change to the sub-SCIP",
1686 processVarEvent, NULL) );
1687 assert(heurdata->eventhdlr != NULL);
1688
1689 /* include primal heuristic */
1690 SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
1691 HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
1692 HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecSubNlp, heurdata) );
1693
1694 assert(heur != NULL);
1695
1696 /* set non-NULL pointers to callback methods */
1697 SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopySubNlp) );
1698 SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeSubNlp) );
1699 SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitSubNlp) );
1700 SCIP_CALL( SCIPsetHeurInitsol(scip, heur, heurInitsolSubNlp) );
1701 SCIP_CALL( SCIPsetHeurExitsol(scip, heur, heurExitsolSubNlp) );
1702
1703 /* add Nlp primal heuristic parameters */
1704 SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/nlpverblevel",
1705 "verbosity level of NLP solver",
1706 &heurdata->nlpverblevel, FALSE, 0, 0, USHRT_MAX, NULL, NULL) );
1707
1708 SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/nodesoffset",
1709 "number of nodes added to the current number of nodes when computing itercontingent (higher value runs heuristic more often in early search)",
1710 &heurdata->nodesoffset, FALSE, 1600, 0, INT_MAX, NULL, NULL) );
1711
1712 SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/nodesfactor",
1713 "factor on number of nodes in SCIP (plus nodesoffset) to compute itercontingent (higher value runs heuristics more frequently)",
1714 &heurdata->nodesfactor, FALSE, 0.3, 0.0, SCIPinfinity(scip), NULL, NULL) );
1715
1716 SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/successrateexp",
1717 "exponent for power of success rate to be multiplied with itercontingent (lower value decreases impact of success rate)",
1718 &heurdata->successrateexp, FALSE, 1.0, 0.0, DBL_MAX, NULL, NULL) );
1719
1720 SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/iterinit",
1721 "number of iterations used for initial NLP solves",
1722 &heurdata->iterinit, FALSE, 300, 0, INT_MAX, NULL, NULL) );
1723
1724 SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/ninitsolves",
1725 "number of successful NLP solves until switching to iterlimit guess and using success rate",
1726 &heurdata->ninitsolves, FALSE, 2, 0, INT_MAX, NULL, NULL) );
1727
1728 SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/itermin",
1729 "minimal number of iterations for NLP solves",
1730 &heurdata->itermin, FALSE, 20, 0, INT_MAX, NULL, NULL) );
1731
1732 SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/opttol",
1733 "absolute optimality tolerance to use for NLP solves",
1734 &heurdata->opttol, TRUE, SCIPdualfeastol(scip), 0.0, 1.0, NULL, NULL) );
1735
1736 SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/feastolfactor",
1737 "factor on SCIP feasibility tolerance for NLP solves if resolving when NLP solution not feasible in CIP",
1738 &heurdata->feastolfactor, FALSE, 0.1, 0.0, 1.0, NULL, NULL) );
1739
1740 SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/maxpresolverounds",
1741 "limit on number of presolve rounds in sub-SCIP (-1 for unlimited, 0 for no presolve)",
1742 &heurdata->maxpresolverounds, FALSE, -1, -1, INT_MAX, NULL, NULL) );
1743
1744 SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/presolveemphasis",
1745 "presolve emphasis in sub-SCIP (0: default, 1: aggressive, 2: fast, 3: off)",
1746 &heurdata->presolveemphasis, FALSE, (int)SCIP_PARAMSETTING_FAST, (int)SCIP_PARAMSETTING_DEFAULT, (int)SCIP_PARAMSETTING_OFF, NULL, NULL) );
1747
1748 SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/setcutoff",
1749 "whether to set cutoff in sub-SCIP to current primal bound",
1750 &heurdata->setcutoff, FALSE, TRUE, NULL, NULL) );
1751
1752 SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/forbidfixings",
1753 "whether to add constraints that forbid specific fixings that turned out to be infeasible",
1754 &heurdata->forbidfixings, FALSE, FALSE, NULL, NULL) );
1755
1756 SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/keepcopy",
1757 "whether to keep SCIP copy or to create new copy each time heuristic is applied",
1758 &heurdata->keepcopy, TRUE, TRUE, NULL, NULL) );
1759
1760 SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/expectinfeas",
1761 "percentage of NLP solves with infeasible status required to tell NLP solver to expect an infeasible NLP",
1762 &heurdata->expectinfeas, FALSE, 0.0, 0.0, 1.0, NULL, NULL) );
1763
1764 return SCIP_OKAY;
1765}
1766
1767/** main procedure of the subNLP heuristic */
1768SCIP_RETCODE SCIPapplyHeurSubNlp(
1769 SCIP* scip, /**< original SCIP data structure */
1770 SCIP_HEUR* heur, /**< heuristic data structure */
1771 SCIP_RESULT* result, /**< pointer to store result of: did not run, solution found, no solution found, or fixing is infeasible (cutoff) */
1772 SCIP_SOL* refpoint, /**< point to take fixation of discrete variables from, and startpoint for NLP solver; if NULL, then LP solution is used */
1773 SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
1774 )
1775{
1776 SCIP_HEURDATA* heurdata;
1777 SCIP_VAR* var;
1778 SCIP_VAR* subvar;
1779 int i;
1780 SCIP_Real cutoff = SCIPinfinity(scip);
1781
1782 assert(scip != NULL);
1783 assert(heur != NULL);
1784
1785 /* get heuristic's data */
1786 heurdata = SCIPheurGetData(heur);
1787 assert(heurdata != NULL);
1788
1789 /* try to setup NLP if not tried before */
1790 if( heurdata->subscip == NULL && !heurdata->triedsetupsubscip )
1791 {
1792 SCIP_CALL( createSubSCIP(scip, heurdata) );
1793 }
1794
1795 *result = SCIP_DIDNOTRUN;
1796
1797 /* if subSCIP could not be created, then do not run */
1798 if( heurdata->subscip == NULL )
1799 return SCIP_OKAY;
1800
1801 assert(heurdata->nsubvars > 0);
1802 assert(heurdata->var_subscip2scip != NULL);
1803
1804 /* fix discrete variables in sub-SCIP */
1805 if( !heurdata->continuous )
1806 {
1807 SCIP_Real fixval;
1808 SCIP_VAR** subvars;
1809 int nsubvars;
1810 int nsubbinvars;
1811 int nsubintvars;
1812 SCIP_Bool infeas;
1813 SCIP_Bool tightened;
1814
1815 /* transform sub-SCIP, so variable fixing are easily undone by free-transform */
1816 assert(!SCIPisTransformed(heurdata->subscip));
1817 SCIP_CALL( SCIPtransformProb(heurdata->subscip) );
1818
1819 SCIP_CALL( SCIPgetOrigVarsData(heurdata->subscip, &subvars, &nsubvars, &nsubbinvars, &nsubintvars, NULL, NULL) );
1820 assert(nsubvars == heurdata->nsubvars);
1821
1822 /* fix discrete variables to values in startpoint */
1823 for( i = nsubbinvars + nsubintvars - 1; i >= 0; --i )
1824 {
1825 subvar = subvars[i];
1826 assert(SCIPvarGetProbindex(subvar) == i);
1827
1828 var = heurdata->var_subscip2scip[i];
1829 assert(var != NULL);
1830
1831 /* at this point, variables in subscip and in our scip should have same bounds */
1832 assert(SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetLbGlobal(var)));
1833 assert(SCIPisEQ(scip, SCIPvarGetUbGlobal(subvar), SCIPvarGetUbGlobal(var)));
1834
1835 fixval = SCIPgetSolVal(scip, refpoint, var);
1836
1837 /* only run heuristic on integer feasible points (unless we are on an unbounded LP) */
1838 if( !SCIPisFeasIntegral(scip, fixval) )
1839 {
1840 if( refpoint != NULL || SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
1841 {
1842 SCIPdebugMsg(scip, "skip NLP heuristic because start candidate not integer feasible: var <%s> has value %g\n", SCIPvarGetName(var), fixval);
1843 goto CLEANUP;
1844 }
1845 }
1846 /* if we do not really have a startpoint, then we should take care that we do not fix variables to very large values
1847 * thus, we set to 0.0 here and project on bounds below
1848 */
1849 if( REALABS(fixval) > 1E+10 && refpoint == NULL && SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
1850 fixval = 0.0;
1851
1852 /* fixing variables to infinity causes problems, we should not have been passed such a solution as refpoint */
1853 assert(!SCIPisInfinity(scip, REALABS(fixval)));
1854
1855 /* round fractional variables to the nearest integer */
1856 fixval = SCIPround(scip, fixval);
1857
1858 /* adjust value to the global bounds of the corresponding SCIP variable */
1859 fixval = MAX(fixval, SCIPvarGetLbGlobal(var)); /*lint !e666*/
1860 fixval = MIN(fixval, SCIPvarGetUbGlobal(var)); /*lint !e666*/
1861
1862 /* SCIPdebugMsg(scip, "fix variable <%s> to %g\n", SCIPvarGetName(var), fixval); */
1863 SCIP_CALL( SCIPtightenVarLb(heurdata->subscip, subvar, fixval, TRUE, &infeas, &tightened) );
1864 if( !infeas )
1865 {
1866 SCIP_CALL( SCIPtightenVarUb(heurdata->subscip, subvar, fixval, TRUE, &infeas, &tightened) );
1867 }
1868 if( infeas )
1869 {
1870 SCIPdebugMsg(scip, "skip NLP heuristic because start candidate not feasible: fixing var <%s> to value %g is infeasible\n", SCIPvarGetName(var), fixval);
1871 goto CLEANUP;
1872 }
1873 }
1874
1875 /* if there is already a solution, possibly add an objective cutoff in sub-SCIP
1876 * we do this here only for problems with discrete variables, since the cutoff may be useful when presolving the subscip
1877 * for the NLP solver, a cutoff is useless at best
1878 */
1879 if( SCIPgetNSols(scip) > 0 && heurdata->setcutoff )
1880 {
1881 cutoff = SCIPgetUpperbound(scip);
1882 assert( !SCIPisInfinity(scip, cutoff) );
1883
1884 SCIP_CALL( SCIPsetObjlimit(heurdata->subscip, cutoff) );
1885 SCIPdebugMsg(scip, "set objective limit %g\n", cutoff);
1886 }
1887 }
1888 else
1889 {
1890 /* for continuous problems, we should already be in the transformed stage */
1891 assert(SCIPisTransformed(heurdata->subscip));
1892 }
1893
1894 /* solve the subNLP and try to add solution to SCIP */
1895 SCIP_CALL( solveSubNLP(scip, heur, result, refpoint, resultsol) );
1896
1897 if( heurdata->subscip == NULL )
1898 {
1899 /* something horrible must have happened that we decided to give up completely on this heuristic */
1900 *result = SCIP_DIDNOTFIND;
1901 return SCIP_OKAY;
1902 }
1903
1904 if( *result == SCIP_CUTOFF )
1905 {
1906 if( heurdata->subscipisvalid && SCIPgetNActivePricers(scip) == 0 )
1907 {
1908 /* if the subNLP is valid and turned out to be globally infeasible (i.e., proven by SCIP), then we forbid this fixation in the main problem */
1909 if( SCIPisInfinity(scip, cutoff) && heurdata->forbidfixings )
1910 {
1911 SCIP_CALL( forbidFixation(scip, heurdata) );
1912 }
1913 }
1914 else
1915 {
1916 /* if the subNLP turned out to be globally infeasible but we are not sure that we have a valid copy, we change to DIDNOTFIND */
1917 *result = SCIP_DIDNOTFIND;
1918 }
1919 }
1920
1921 CLEANUP:
1922 if( !heurdata->continuous )
1923 {
1924 SCIP_CALL( SCIPfreeTransform(heurdata->subscip) );
1925 }
1926
1927 /* if the heuristic was applied before solving has started, then destroy subSCIP, since EXITSOL may not be called
1928 * also if keepcopy is disabled, then destroy subSCIP
1929 */
1930 if( SCIPgetStage(scip) < SCIP_STAGE_SOLVING || !heurdata->keepcopy )
1931 {
1932 SCIP_CALL( freeSubSCIP(scip, heurdata) );
1933 heurdata->triedsetupsubscip = FALSE;
1934 }
1935
1936 return SCIP_OKAY;
1937}
1938
1939/** updates the starting point for the NLP heuristic
1940 *
1941 * Is called by a constraint handler that handles nonlinear constraints when a check on feasibility of a solution fails.
1942 */
1943SCIP_RETCODE SCIPupdateStartpointHeurSubNlp(
1944 SCIP* scip, /**< SCIP data structure */
1945 SCIP_HEUR* heur, /**< NLP heuristic */
1946 SCIP_SOL* solcand, /**< solution candidate */
1947 SCIP_Real violation /**< constraint violation of solution candidate */
1948 )
1949{
1950 SCIP_HEURDATA* heurdata;
1951
1952 assert(scip != NULL);
1953 assert(heur != NULL);
1954 assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
1955 assert(solcand != NULL);
1956 assert(SCIPisPositive(scip, violation));
1957
1958 /* too early or the game is over already: no more interest in starting points */
1959 if( SCIPgetStage(scip) != SCIP_STAGE_SOLVING )
1960 return SCIP_OKAY;
1961
1962 heurdata = SCIPheurGetData(heur);
1963 assert(heurdata != NULL);
1964
1965 if( heurdata->subscip == NULL )
1966 {
1967 /* if we do not have a sub-SCIP, but tried to set one up before or will never create a subSCIP, then do not need a starting point */
1968 SCIP_Bool runheur;
1969 if( heurdata->triedsetupsubscip )
1970 return SCIP_OKAY;
1971 if( SCIPheurGetFreq(heur) < 0 )
1972 return SCIP_OKAY;
1973 SCIP_CALL( runHeuristic(scip, &runheur) );
1974 if( !runheur )
1975 return SCIP_OKAY;
1976 }
1977
1978 /* if the solution is the one we created (last), then it is useless to use it as starting point again
1979 * (we cannot check SCIPsolGetHeur()==heur, as subnlp may not be registered as author of the solution)
1980 */
1981 if( heurdata->lastsol == solcand )
1982 return SCIP_OKAY;
1983
1984 SCIPdebugMsg(scip, "consider solution candidate with violation %g and objective %g from %s\n",
1985 violation, SCIPgetSolTransObj(scip, solcand), SCIPsolGetHeur(solcand) ? SCIPheurGetName(SCIPsolGetHeur(solcand)) : "tree");
1986
1987 /* if we have no point yet, or the new point has a lower constraint violation, or it has a better objective function value, then take the new point */
1988 if( heurdata->startcand == NULL || violation < heurdata->startcandviol ||
1989 SCIPisRelGT(scip, SCIPgetSolTransObj(scip, heurdata->startcand), SCIPgetSolTransObj(scip, solcand)) )
1990 {
1991 if( heurdata->startcand != NULL )
1992 {
1993 SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1994 }
1995 SCIP_CALL( SCIPcreateSolCopy(scip, &heurdata->startcand, solcand) );
1996 SCIP_CALL( SCIPunlinkSol(scip, heurdata->startcand) );
1997 heurdata->startcandviol = violation;
1998
1999 /* remember which heuristic proposed the candidate */
2000 SCIPsolSetHeur(heurdata->startcand, SCIPgetSolHeur(scip, solcand));
2001 }
2002
2003 return SCIP_OKAY;
2004}
2005
2006/** gets startpoint candidate to be used in next call to NLP heuristic, or NULL if none */
2007SCIP_SOL* SCIPgetStartCandidateHeurSubNlp(
2008 SCIP* scip, /**< original SCIP data structure */
2009 SCIP_HEUR* heur /**< heuristic data structure */
2010 )
2011{
2012 SCIP_HEURDATA* heurdata;
2013
2014 assert(scip != NULL);
2015 assert(heur != NULL);
2016 assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
2017
2018 heurdata = SCIPheurGetData(heur);
2019 assert(heurdata != NULL);
2020
2021 return heurdata->startcand;
2022}
cons_bounddisjunction.h
constraint handler for bound disjunction constraints
cons_setppc.h
Constraint handler for the set partitioning / packing / covering constraints .
NULL
#define NULL
Definition: def.h:266
SCIP_MAXSTRLEN
#define SCIP_MAXSTRLEN
Definition: def.h:287
SCIP_Longint
#define SCIP_Longint
Definition: def.h:157
SCIP_INVALID
#define SCIP_INVALID
Definition: def.h:192
SCIP_Bool
#define SCIP_Bool
Definition: def.h:91
MIN
#define MIN(x, y)
Definition: def.h:242
MAX3
#define MAX3(x, y, z)
Definition: def.h:246
SCIP_Real
#define SCIP_Real
Definition: def.h:172
TRUE
#define TRUE
Definition: def.h:93
FALSE
#define FALSE
Definition: def.h:94
MAX
#define MAX(x, y)
Definition: def.h:238
SCIPABORT
#define SCIPABORT()
Definition: def.h:345
REALABS
#define REALABS(x)
Definition: def.h:196
SCIP_CALL
#define SCIP_CALL(x)
Definition: def.h:373
SCIPcreateConsBounddisjunction
SCIP_RETCODE SCIPcreateConsBounddisjunction(SCIP *scip, SCIP_CONS **cons, const char *name, int nvars, SCIP_VAR **vars, SCIP_BOUNDTYPE *boundtypes, SCIP_Real *bounds, SCIP_Bool initial, SCIP_Bool separate, SCIP_Bool enforce, SCIP_Bool check, SCIP_Bool propagate, SCIP_Bool local, SCIP_Bool modifiable, SCIP_Bool dynamic, SCIP_Bool removable, SCIP_Bool stickingatnode)
Definition: cons_bounddisjunction.c:3286
SCIPcreateConsSetcover
SCIP_RETCODE SCIPcreateConsSetcover(SCIP *scip, SCIP_CONS **cons, const char *name, int nvars, SCIP_VAR **vars, SCIP_Bool initial, SCIP_Bool separate, SCIP_Bool enforce, SCIP_Bool check, SCIP_Bool propagate, SCIP_Bool local, SCIP_Bool modifiable, SCIP_Bool dynamic, SCIP_Bool removable, SCIP_Bool stickingatnode)
Definition: cons_setppc.c:9484
SCIPmergeNLPIStatistics
void SCIPmergeNLPIStatistics(SCIP *sourcescip, SCIP *targetscip, SCIP_Bool reset)
Definition: scip_copy.c:1330
SCIPcopyVars
SCIP_RETCODE SCIPcopyVars(SCIP *sourcescip, SCIP *targetscip, SCIP_HASHMAP *varmap, SCIP_HASHMAP *consmap, SCIP_VAR **fixedvars, SCIP_Real *fixedvals, int nfixedvars, SCIP_Bool global)
Definition: scip_copy.c:1178
SCIPcopyPlugins
SCIP_RETCODE SCIPcopyPlugins(SCIP *sourcescip, SCIP *targetscip, SCIP_Bool copyreaders, SCIP_Bool copypricers, SCIP_Bool copyconshdlrs, SCIP_Bool copyconflicthdlrs, SCIP_Bool copypresolvers, SCIP_Bool copyrelaxators, SCIP_Bool copyseparators, SCIP_Bool copycutselectors, SCIP_Bool copypropagators, SCIP_Bool copyheuristics, SCIP_Bool copyeventhdlrs, SCIP_Bool copynodeselectors, SCIP_Bool copybranchrules, SCIP_Bool copydisplays, SCIP_Bool copydialogs, SCIP_Bool copytables, SCIP_Bool copyexprhdlrs, SCIP_Bool copynlpis, SCIP_Bool passmessagehdlr, SCIP_Bool *valid)
Definition: scip_copy.c:275
SCIPcopyProb
SCIP_RETCODE SCIPcopyProb(SCIP *sourcescip, SCIP *targetscip, SCIP_HASHMAP *varmap, SCIP_HASHMAP *consmap, SCIP_Bool global, const char *name)
Definition: scip_copy.c:527
SCIPcopyConss
SCIP_RETCODE SCIPcopyConss(SCIP *sourcescip, SCIP *targetscip, SCIP_HASHMAP *varmap, SCIP_HASHMAP *consmap, SCIP_Bool global, SCIP_Bool enablepricing, SCIP_Bool *valid)
Definition: scip_copy.c:1730
SCIPcopyParamSettings
SCIP_RETCODE SCIPcopyParamSettings(SCIP *sourcescip, SCIP *targetscip)
Definition: scip_copy.c:2564
SCIPisTransformed
SCIP_Bool SCIPisTransformed(SCIP *scip)
Definition: scip_general.c:606
SCIPfree
SCIP_RETCODE SCIPfree(SCIP **scip)
Definition: scip_general.c:349
SCIPcreate
SCIP_RETCODE SCIPcreate(SCIP **scip)
Definition: scip_general.c:317
SCIPgetStatus
SCIP_STATUS SCIPgetStatus(SCIP *scip)
Definition: scip_general.c:508
SCIPgetStage
SCIP_STAGE SCIPgetStage(SCIP *scip)
Definition: scip_general.c:390
SCIPgetNIntVars
int SCIPgetNIntVars(SCIP *scip)
Definition: scip_prob.c:2082
SCIPgetOrigVarsData
SCIP_RETCODE SCIPgetOrigVarsData(SCIP *scip, SCIP_VAR ***vars, int *nvars, int *nbinvars, int *nintvars, int *nimplvars, int *ncontvars)
Definition: scip_prob.c:2357
SCIPgetProbName
const char * SCIPgetProbName(SCIP *scip)
Definition: scip_prob.c:1067
SCIPsetObjlimit
SCIP_RETCODE SCIPsetObjlimit(SCIP *scip, SCIP_Real objlimit)
Definition: scip_prob.c:1422
SCIPgetVarsData
SCIP_RETCODE SCIPgetVarsData(SCIP *scip, SCIP_VAR ***vars, int *nvars, int *nbinvars, int *nintvars, int *nimplvars, int *ncontvars)
Definition: scip_prob.c:1866
SCIPgetNVars
int SCIPgetNVars(SCIP *scip)
Definition: scip_prob.c:1992
SCIPaddCons
SCIP_RETCODE SCIPaddCons(SCIP *scip, SCIP_CONS *cons)
Definition: scip_prob.c:2770
SCIPgetNConss
int SCIPgetNConss(SCIP *scip)
Definition: scip_prob.c:3043
SCIPgetVars
SCIP_VAR ** SCIPgetVars(SCIP *scip)
Definition: scip_prob.c:1947
SCIPgetNBinVars
int SCIPgetNBinVars(SCIP *scip)
Definition: scip_prob.c:2037
SCIPhashmapFree
void SCIPhashmapFree(SCIP_HASHMAP **hashmap)
Definition: misc.c:3111
SCIPhashmapEntryGetImage
void * SCIPhashmapEntryGetImage(SCIP_HASHMAPENTRY *entry)
Definition: misc.c:3573
SCIPhashmapGetImage
void * SCIPhashmapGetImage(SCIP_HASHMAP *hashmap, void *origin)
Definition: misc.c:3264
SCIPhashmapGetNEntries
int SCIPhashmapGetNEntries(SCIP_HASHMAP *hashmap)
Definition: misc.c:3544
SCIPhashmapGetEntry
SCIP_HASHMAPENTRY * SCIPhashmapGetEntry(SCIP_HASHMAP *hashmap, int entryidx)
Definition: misc.c:3552
SCIPhashmapCreate
SCIP_RETCODE SCIPhashmapCreate(SCIP_HASHMAP **hashmap, BMS_BLKMEM *blkmem, int mapsize)
Definition: misc.c:3077
SCIPhashmapEntryGetOrigin
void * SCIPhashmapEntryGetOrigin(SCIP_HASHMAPENTRY *entry)
Definition: misc.c:3563
SCIPgetLocalDualbound
SCIP_Real SCIPgetLocalDualbound(SCIP *scip)
Definition: scip_prob.c:3567
SCIPinfoMessage
void SCIPinfoMessage(SCIP *scip, FILE *file, const char *formatstr,...)
Definition: scip_message.c:208
SCIPverbMessage
void SCIPverbMessage(SCIP *scip, SCIP_VERBLEVEL msgverblevel, FILE *file, const char *formatstr,...)
Definition: scip_message.c:225
SCIPdebugMsg
#define SCIPdebugMsg
Definition: scip_message.h:78
SCIPwarningMessage
void SCIPwarningMessage(SCIP *scip, const char *formatstr,...)
Definition: scip_message.c:120
SCIPgetStartCandidateHeurSubNlp
SCIP_SOL * SCIPgetStartCandidateHeurSubNlp(SCIP *scip, SCIP_HEUR *heur)
Definition: heur_subnlp.c:2007
SCIPapplyHeurSubNlp
SCIP_RETCODE SCIPapplyHeurSubNlp(SCIP *scip, SCIP_HEUR *heur, SCIP_RESULT *result, SCIP_SOL *refpoint, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:1768
SCIPupdateStartpointHeurSubNlp
SCIP_RETCODE SCIPupdateStartpointHeurSubNlp(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL *solcand, SCIP_Real violation)
Definition: heur_subnlp.c:1943
SCIPisParamFixed
SCIP_Bool SCIPisParamFixed(SCIP *scip, const char *name)
Definition: scip_param.c:219
SCIPaddIntParam
SCIP_RETCODE SCIPaddIntParam(SCIP *scip, const char *name, const char *desc, int *valueptr, SCIP_Bool isadvanced, int defaultvalue, int minvalue, int maxvalue, SCIP_DECL_PARAMCHGD((*paramchgd)), SCIP_PARAMDATA *paramdata)
Definition: scip_param.c:83
SCIPsetLongintParam
SCIP_RETCODE SCIPsetLongintParam(SCIP *scip, const char *name, SCIP_Longint value)
Definition: scip_param.c:545
SCIPaddRealParam
SCIP_RETCODE SCIPaddRealParam(SCIP *scip, const char *name, const char *desc, SCIP_Real *valueptr, SCIP_Bool isadvanced, SCIP_Real defaultvalue, SCIP_Real minvalue, SCIP_Real maxvalue, SCIP_DECL_PARAMCHGD((*paramchgd)), SCIP_PARAMDATA *paramdata)
Definition: scip_param.c:139
SCIPsetIntParam
SCIP_RETCODE SCIPsetIntParam(SCIP *scip, const char *name, int value)
Definition: scip_param.c:487
SCIPgetRealParam
SCIP_RETCODE SCIPgetRealParam(SCIP *scip, const char *name, SCIP_Real *value)
Definition: scip_param.c:307
SCIPresetParam
SCIP_RETCODE SCIPresetParam(SCIP *scip, const char *name)
Definition: scip_param.c:835
SCIPsetPresolving
SCIP_RETCODE SCIPsetPresolving(SCIP *scip, SCIP_PARAMSETTING paramsetting, SCIP_Bool quiet)
Definition: scip_param.c:953
SCIPaddBoolParam
SCIP_RETCODE SCIPaddBoolParam(SCIP *scip, const char *name, const char *desc, SCIP_Bool *valueptr, SCIP_Bool isadvanced, SCIP_Bool defaultvalue, SCIP_DECL_PARAMCHGD((*paramchgd)), SCIP_PARAMDATA *paramdata)
Definition: scip_param.c:57
SCIPsetBoolParam
SCIP_RETCODE SCIPsetBoolParam(SCIP *scip, const char *name, SCIP_Bool value)
Definition: scip_param.c:429
SCIPsetRealParam
SCIP_RETCODE SCIPsetRealParam(SCIP *scip, const char *name, SCIP_Real value)
Definition: scip_param.c:603
SCIPincludeHeurSubNlp
SCIP_RETCODE SCIPincludeHeurSubNlp(SCIP *scip)
Definition: heur_subnlp.c:1672
SCIPgetNLPBranchCands
int SCIPgetNLPBranchCands(SCIP *scip)
Definition: scip_branch.c:428
SCIPreleaseCons
SCIP_RETCODE SCIPreleaseCons(SCIP *scip, SCIP_CONS **cons)
Definition: scip_cons.c:1174
SCIPincludeEventhdlrBasic
SCIP_RETCODE SCIPincludeEventhdlrBasic(SCIP *scip, SCIP_EVENTHDLR **eventhdlrptr, const char *name, const char *desc, SCIP_DECL_EVENTEXEC((*eventexec)), SCIP_EVENTHDLRDATA *eventhdlrdata)
Definition: scip_event.c:104
SCIPeventGetType
SCIP_EVENTTYPE SCIPeventGetType(SCIP_EVENT *event)
Definition: event.c:1030
SCIPcatchVarEvent
SCIP_RETCODE SCIPcatchVarEvent(SCIP *scip, SCIP_VAR *var, SCIP_EVENTTYPE eventtype, SCIP_EVENTHDLR *eventhdlr, SCIP_EVENTDATA *eventdata, int *filterpos)
Definition: scip_event.c:354
SCIPdropVarEvent
SCIP_RETCODE SCIPdropVarEvent(SCIP *scip, SCIP_VAR *var, SCIP_EVENTTYPE eventtype, SCIP_EVENTHDLR *eventhdlr, SCIP_EVENTDATA *eventdata, int filterpos)
Definition: scip_event.c:400
SCIPeventGetVar
SCIP_VAR * SCIPeventGetVar(SCIP_EVENT *event)
Definition: event.c:1053
SCIPeventGetNewbound
SCIP_Real SCIPeventGetNewbound(SCIP_EVENT *event)
Definition: event.c:1242
SCIPsetHeurExitsol
SCIP_RETCODE SCIPsetHeurExitsol(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEUREXITSOL((*heurexitsol)))
Definition: scip_heur.c:242
SCIPsetHeurCopy
SCIP_RETCODE SCIPsetHeurCopy(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURCOPY((*heurcopy)))
Definition: scip_heur.c:162
SCIPheurGetData
SCIP_HEURDATA * SCIPheurGetData(SCIP_HEUR *heur)
Definition: heur.c:1364
SCIPincludeHeurBasic
SCIP_RETCODE SCIPincludeHeurBasic(SCIP *scip, SCIP_HEUR **heur, const char *name, const char *desc, char dispchar, int priority, int freq, int freqofs, int maxdepth, SCIP_HEURTIMING timingmask, SCIP_Bool usessubscip, SCIP_DECL_HEUREXEC((*heurexec)), SCIP_HEURDATA *heurdata)
Definition: scip_heur.c:117
SCIPsetHeurFree
SCIP_RETCODE SCIPsetHeurFree(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURFREE((*heurfree)))
Definition: scip_heur.c:178
SCIPheurGetNSolsFound
SCIP_Longint SCIPheurGetNSolsFound(SCIP_HEUR *heur)
Definition: heur.c:1589
SCIPheurSetTimingmask
void SCIPheurSetTimingmask(SCIP_HEUR *heur, SCIP_HEURTIMING timingmask)
Definition: heur.c:1493
SCIPheurGetNCalls
SCIP_Longint SCIPheurGetNCalls(SCIP_HEUR *heur)
Definition: heur.c:1579
SCIPsetHeurInitsol
SCIP_RETCODE SCIPsetHeurInitsol(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURINITSOL((*heurinitsol)))
Definition: scip_heur.c:226
SCIPheurGetFreqofs
int SCIPheurGetFreqofs(SCIP_HEUR *heur)
Definition: heur.c:1559
SCIPsetHeurInit
SCIP_RETCODE SCIPsetHeurInit(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURINIT((*heurinit)))
Definition: scip_heur.c:194
SCIPheurGetFreq
int SCIPheurGetFreq(SCIP_HEUR *heur)
Definition: heur.c:1538
SCIPheurGetName
const char * SCIPheurGetName(SCIP_HEUR *heur)
Definition: heur.c:1453
SCIPgetLPSolstat
SCIP_LPSOLSTAT SCIPgetLPSolstat(SCIP *scip)
Definition: scip_lp.c:168
SCIPfreeBlockMemoryArray
#define SCIPfreeBlockMemoryArray(scip, ptr, num)
Definition: scip_mem.h:110
SCIPallocClearBlockMemoryArray
#define SCIPallocClearBlockMemoryArray(scip, ptr, num)
Definition: scip_mem.h:97
SCIPallocBufferArray
#define SCIPallocBufferArray(scip, ptr, num)
Definition: scip_mem.h:124
SCIPfreeBufferArray
#define SCIPfreeBufferArray(scip, ptr)
Definition: scip_mem.h:136
SCIPfreeBlockMemory
#define SCIPfreeBlockMemory(scip, ptr)
Definition: scip_mem.h:108
SCIPfreeBufferArrayNull
#define SCIPfreeBufferArrayNull(scip, ptr)
Definition: scip_mem.h:137
SCIPallocBlockMemory
#define SCIPallocBlockMemory(scip, ptr)
Definition: scip_mem.h:89
SCIPgetNNlpis
int SCIPgetNNlpis(SCIP *scip)
Definition: scip_nlpi.c:200
SCIPhasNLPContinuousNonlinearity
SCIP_RETCODE SCIPhasNLPContinuousNonlinearity(SCIP *scip, SCIP_Bool *result)
Definition: scip_nlp.c:125
SCIPisNLPConstructed
SCIP_Bool SCIPisNLPConstructed(SCIP *scip)
Definition: scip_nlp.c:110
SCIPgetNLPSolstat
SCIP_NLPSOLSTAT SCIPgetNLPSolstat(SCIP *scip)
Definition: scip_nlp.c:574
SCIPsolveNLP
#define SCIPsolveNLP(...)
Definition: scip_nlp.h:361
SCIPsetNLPInitialGuess
SCIP_RETCODE SCIPsetNLPInitialGuess(SCIP *scip, SCIP_Real *initialguess)
Definition: scip_nlp.c:474
SCIPgetNLPObjval
SCIP_Real SCIPgetNLPObjval(SCIP *scip)
Definition: scip_nlp.c:645
SCIPgetNNLPVars
int SCIPgetNNLPVars(SCIP *scip)
Definition: scip_nlp.c:201
SCIPgetNLPVars
SCIP_VAR ** SCIPgetNLPVars(SCIP *scip)
Definition: scip_nlp.c:179
SCIPhasNLPSolution
SCIP_Bool SCIPhasNLPSolution(SCIP *scip)
Definition: scip_nlp.c:671
SCIPgetNLPTermstat
SCIP_NLPTERMSTAT SCIPgetNLPTermstat(SCIP *scip)
Definition: scip_nlp.c:596
SCIPgetNLPStatistics
SCIP_RETCODE SCIPgetNLPStatistics(SCIP *scip, SCIP_NLPSTATISTICS *statistics)
Definition: scip_nlp.c:621
SCIPgetNPresolRounds
int SCIPgetNPresolRounds(SCIP *scip)
Definition: scip_presol.c:288
SCIPgetNActivePricers
int SCIPgetNActivePricers(SCIP *scip)
Definition: scip_pricer.c:348
SCIPcreateSol
SCIP_RETCODE SCIPcreateSol(SCIP *scip, SCIP_SOL **sol, SCIP_HEUR *heur)
Definition: scip_sol.c:180
SCIPcreateSolCopy
SCIP_RETCODE SCIPcreateSolCopy(SCIP *scip, SCIP_SOL **sol, SCIP_SOL *sourcesol)
Definition: scip_sol.c:470
SCIPfreeSol
SCIP_RETCODE SCIPfreeSol(SCIP *scip, SCIP_SOL **sol)
Definition: scip_sol.c:837
SCIPgetSolHeur
SCIP_HEUR * SCIPgetSolHeur(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1536
SCIPgetNSols
int SCIPgetNSols(SCIP *scip)
Definition: scip_sol.c:2066
SCIPsolGetHeur
SCIP_HEUR * SCIPsolGetHeur(SCIP_SOL *sol)
Definition: sol.c:2804
SCIPunlinkSol
SCIP_RETCODE SCIPunlinkSol(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1042
SCIPgetSols
SCIP_SOL ** SCIPgetSols(SCIP *scip)
Definition: scip_sol.c:2115
SCIPcheckSol
SCIP_RETCODE SCIPcheckSol(SCIP *scip, SCIP_SOL *sol, SCIP_Bool printreason, SCIP_Bool completely, SCIP_Bool checkbounds, SCIP_Bool checkintegrality, SCIP_Bool checklprows, SCIP_Bool *feasible)
Definition: scip_sol.c:3247
SCIPtrySolFree
SCIP_RETCODE SCIPtrySolFree(SCIP *scip, SCIP_SOL **sol, SCIP_Bool printreason, SCIP_Bool completely, SCIP_Bool checkbounds, SCIP_Bool checkintegrality, SCIP_Bool checklprows, SCIP_Bool *stored)
Definition: scip_sol.c:3046
SCIPsetSolVal
SCIP_RETCODE SCIPsetSolVal(SCIP *scip, SCIP_SOL *sol, SCIP_VAR *var, SCIP_Real val)
Definition: scip_sol.c:1073
SCIPgetSolVal
SCIP_Real SCIPgetSolVal(SCIP *scip, SCIP_SOL *sol, SCIP_VAR *var)
Definition: scip_sol.c:1213
SCIPgetSolTransObj
SCIP_Real SCIPgetSolTransObj(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1343
SCIPsolSetHeur
void SCIPsolSetHeur(SCIP_SOL *sol, SCIP_HEUR *heur)
Definition: sol.c:2849
SCIPtransformProb
SCIP_RETCODE SCIPtransformProb(SCIP *scip)
Definition: scip_solve.c:223
SCIPpresolve
SCIP_RETCODE SCIPpresolve(SCIP *scip)
Definition: scip_solve.c:2332
SCIPfreeTransform
SCIP_RETCODE SCIPfreeTransform(SCIP *scip)
Definition: scip_solve.c:3350
SCIPsolve
SCIP_RETCODE SCIPsolve(SCIP *scip)
Definition: scip_solve.c:2502
SCIPgetPrimalbound
SCIP_Real SCIPgetPrimalbound(SCIP *scip)
Definition: scip_solvingstats.c:1524
SCIPgetUpperbound
SCIP_Real SCIPgetUpperbound(SCIP *scip)
Definition: scip_solvingstats.c:1548
SCIPgetNNodes
SCIP_Longint SCIPgetNNodes(SCIP *scip)
Definition: scip_solvingstats.c:211
SCIPprintStatistics
SCIP_RETCODE SCIPprintStatistics(SCIP *scip, FILE *file)
Definition: scip_solvingstats.c:4180
SCIPgetSolvingTime
SCIP_Real SCIPgetSolvingTime(SCIP *scip)
Definition: scip_timing.c:378
SCIPinfinity
SCIP_Real SCIPinfinity(SCIP *scip)
Definition: scip_numerics.c:510
SCIPisRelGT
SCIP_Bool SCIPisRelGT(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:1195
SCIPisFeasEQ
SCIP_Bool SCIPisFeasEQ(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:780
SCIPisPositive
SCIP_Bool SCIPisPositive(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:570
SCIPisLE
SCIP_Bool SCIPisLE(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:471
SCIPisInfinity
SCIP_Bool SCIPisInfinity(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:521
SCIPround
SCIP_Real SCIPround(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:655
SCIPisFeasIntegral
SCIP_Bool SCIPisFeasIntegral(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:881
SCIPfeastol
SCIP_Real SCIPfeastol(SCIP *scip)
Definition: scip_numerics.c:165
SCIPdualfeastol
SCIP_Real SCIPdualfeastol(SCIP *scip)
Definition: scip_numerics.c:195
SCIPceil
SCIP_Real SCIPceil(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:643
SCIPisEQ
SCIP_Bool SCIPisEQ(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:445
SCIPisZero
SCIP_Bool SCIPisZero(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:558
SCIPtightenVarLb
SCIP_RETCODE SCIPtightenVarLb(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound, SCIP_Bool force, SCIP_Bool *infeasible, SCIP_Bool *tightened)
Definition: scip_var.c:5326
SCIPvarGetOrigvarSum
SCIP_RETCODE SCIPvarGetOrigvarSum(SCIP_VAR **var, SCIP_Real *scalar, SCIP_Real *constant)
Definition: var.c:12773
SCIPvarIsActive
SCIP_Bool SCIPvarIsActive(SCIP_VAR *var)
Definition: var.c:17747
SCIPvarGetUbLocal
SCIP_Real SCIPvarGetUbLocal(SCIP_VAR *var)
Definition: var.c:18143
SCIPtightenVarUb
SCIP_RETCODE SCIPtightenVarUb(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound, SCIP_Bool force, SCIP_Bool *infeasible, SCIP_Bool *tightened)
Definition: scip_var.c:5443
SCIPvarGetType
SCIP_VARTYPE SCIPvarGetType(SCIP_VAR *var)
Definition: var.c:17583
SCIPvarGetUbGlobal
SCIP_Real SCIPvarGetUbGlobal(SCIP_VAR *var)
Definition: var.c:18087
SCIPvarGetProbindex
int SCIPvarGetProbindex(SCIP_VAR *var)
Definition: var.c:17767
SCIPvarGetName
const char * SCIPvarGetName(SCIP_VAR *var)
Definition: var.c:17418
SCIPreleaseVar
SCIP_RETCODE SCIPreleaseVar(SCIP *scip, SCIP_VAR **var)
Definition: scip_var.c:1248
SCIPchgVarLbGlobal
SCIP_RETCODE SCIPchgVarLbGlobal(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound)
Definition: scip_var.c:5066
SCIPgetNegatedVar
SCIP_RETCODE SCIPgetNegatedVar(SCIP *scip, SCIP_VAR *var, SCIP_VAR **negvar)
Definition: scip_var.c:1527
SCIPvarGetLbLocal
SCIP_Real SCIPvarGetLbLocal(SCIP_VAR *var)
Definition: var.c:18133
SCIPchgVarUbGlobal
SCIP_RETCODE SCIPchgVarUbGlobal(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound)
Definition: scip_var.c:5155
SCIPvarGetLbGlobal
SCIP_Real SCIPvarGetLbGlobal(SCIP_VAR *var)
Definition: var.c:18077
SCIPvarGetNLPSol
SCIP_Real SCIPvarGetNLPSol(SCIP_VAR *var)
Definition: var.c:18464
SCIPcaptureVar
SCIP_RETCODE SCIPcaptureVar(SCIP *scip, SCIP_VAR *var)
Definition: scip_var.c:1214
SCIPsnprintf
int SCIPsnprintf(char *t, int len, const char *s,...)
Definition: misc.c:10880
processNLPSol
static SCIP_RETCODE processNLPSol(SCIP *scip, SCIP_HEUR *heur, SCIP_HEUR *authorheur, SCIP_RESULT *result, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:591
SCIP_DECL_HEURINITSOL
static SCIP_DECL_HEURINITSOL(heurInitsolSubNlp)
Definition: heur_subnlp.c:1495
HEUR_TIMING
#define HEUR_TIMING
Definition: heur_subnlp.c:76
SCIP_DECL_HEUREXITSOL
static SCIP_DECL_HEUREXITSOL(heurExitsolSubNlp)
Definition: heur_subnlp.c:1509
HEUR_FREQOFS
#define HEUR_FREQOFS
Definition: heur_subnlp.c:74
HEUR_DESC
#define HEUR_DESC
Definition: heur_subnlp.c:70
createSolFromNLP
static SCIP_RETCODE createSolFromNLP(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL **sol, SCIP_HEUR *authorheur)
Definition: heur_subnlp.c:525
forbidFixation
static SCIP_RETCODE forbidFixation(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:1260
createSolFromSubScipSol
static SCIP_RETCODE createSolFromSubScipSol(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL **sol, SCIP_SOL *subsol, SCIP_HEUR *authorheur)
Definition: heur_subnlp.c:707
SCIP_DECL_EVENTEXEC
static SCIP_DECL_EVENTEXEC(processVarEvent)
Definition: heur_subnlp.c:468
SCIP_DECL_HEURFREE
static SCIP_DECL_HEURFREE(heurFreeSubNlp)
Definition: heur_subnlp.c:1446
HEUR_DISPCHAR
#define HEUR_DISPCHAR
Definition: heur_subnlp.c:71
HEUR_MAXDEPTH
#define HEUR_MAXDEPTH
Definition: heur_subnlp.c:75
HEUR_PRIORITY
#define HEUR_PRIORITY
Definition: heur_subnlp.c:72
HEUR_NAME
#define HEUR_NAME
Definition: heur_subnlp.c:69
calcIterLimit
static int calcIterLimit(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:774
SCIP_DECL_HEURCOPY
static SCIP_DECL_HEURCOPY(heurCopySubNlp)
Definition: heur_subnlp.c:1432
solveSubNLP
static SCIP_RETCODE solveSubNLP(SCIP *scip, SCIP_HEUR *heur, SCIP_RESULT *result, SCIP_SOL *refpoint, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:799
runHeuristic
static SCIP_RETCODE runHeuristic(SCIP *scip, SCIP_Bool *runheur)
Definition: heur_subnlp.c:136
createSubSCIP
static SCIP_RETCODE createSubSCIP(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:219
SCIP_DECL_HEUREXEC
static SCIP_DECL_HEUREXEC(heurExecSubNlp)
Definition: heur_subnlp.c:1539
freeSubSCIP
static SCIP_RETCODE freeSubSCIP(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:166
HEUR_FREQ
#define HEUR_FREQ
Definition: heur_subnlp.c:73
SCIP_DECL_HEURINIT
static SCIP_DECL_HEURINIT(heurInitSubNlp)
Definition: heur_subnlp.c:1466
HEUR_USESSUBSCIP
#define HEUR_USESSUBSCIP
Definition: heur_subnlp.c:77
heur_subnlp.h
NLP local search primal heuristic using sub-SCIPs.
memory.h
memory allocation routines
BMSclearMemory
#define BMSclearMemory(ptr)
Definition: memory.h:129
SCIPblkmem
BMS_BLKMEM * SCIPblkmem(SCIP *scip)
Definition: scip_mem.c:57
nlpi_ipopt.h
Ipopt NLP interface.
pub_event.h
public methods for managing events
pub_heur.h
public methods for primal heuristics
pub_message.h
public methods for message output
SCIPerrorMessage
#define SCIPerrorMessage
Definition: pub_message.h:64
SCIPdebug
#define SCIPdebug(x)
Definition: pub_message.h:93
pub_misc.h
public data structures and miscellaneous methods
pub_sol.h
public methods for primal CIP solutions
pub_var.h
public methods for problem variables
scip_branch.h
public methods for branching rule plugins and branching
scip_cons.h
public methods for constraint handler plugins and constraints
scip_copy.h
public methods for problem copies
scip_event.h
public methods for event handler plugins and event handlers
scip_general.h
general public methods
scip_heur.h
public methods for primal heuristic plugins and divesets
scip_lp.h
public methods for the LP relaxation, rows and columns
scip_mem.h
public methods for memory management
scip_message.h
public methods for message handling
scip_nlp.h
public methods for nonlinear relaxation
scip_nlpi.h
public methods for NLPI solver interfaces
scip_numerics.h
public methods for numerical tolerances
scip_param.h
public methods for SCIP parameter handling
scip_presol.h
public methods for presolving plugins
scip_pricer.h
public methods for variable pricer plugins
scip_prob.h
public methods for global and local (sub)problems
scip_sol.h
public methods for solutions
scip_solve.h
public solving methods
scip_solvingstats.h
public methods for querying solving statistics
scip_timing.h
public methods for timing
scip_var.h
public methods for SCIP variables
SCIP_Cons
Definition: struct_cons.h:47
SCIP_Eventhdlr
Definition: struct_event.h:205
SCIP_HashMapEntry
Definition: struct_misc.h:131
SCIP_HashMap
Definition: struct_misc.h:138
SCIP_Heur
Definition: struct_heur.h:98
SCIP_NlpStatistics
Definition: type_nlpi.h:198
SCIP_NlpStatistics::totaltime
SCIP_Real totaltime
Definition: type_nlpi.h:200
SCIP_NlpStatistics::boundviol
SCIP_Real boundviol
Definition: type_nlpi.h:204
SCIP_NlpStatistics::niterations
int niterations
Definition: type_nlpi.h:199
SCIP_NlpStatistics::consviol
SCIP_Real consviol
Definition: type_nlpi.h:203
SCIP_Sol
Definition: struct_sol.h:74
SCIP_Var
Definition: struct_var.h:208
SCIP_EVENTTYPE_GUBCHANGED
#define SCIP_EVENTTYPE_GUBCHANGED
Definition: type_event.h:76
SCIP_EVENTTYPE_GBDCHANGED
#define SCIP_EVENTTYPE_GBDCHANGED
Definition: type_event.h:120
SCIP_EVENTDATA
struct SCIP_EventData SCIP_EVENTDATA
Definition: type_event.h:173
SCIP_EVENTTYPE_GLBCHANGED
#define SCIP_EVENTTYPE_GLBCHANGED
Definition: type_event.h:75
SCIP_HEURDATA
struct SCIP_HeurData SCIP_HEURDATA
Definition: type_heur.h:77
SCIP_BOUNDTYPE_UPPER
@ SCIP_BOUNDTYPE_UPPER
Definition: type_lp.h:57
SCIP_BOUNDTYPE_LOWER
@ SCIP_BOUNDTYPE_LOWER
Definition: type_lp.h:56
SCIP_BOUNDTYPE
enum SCIP_BoundType SCIP_BOUNDTYPE
Definition: type_lp.h:59
SCIP_LPSOLSTAT_OPTIMAL
@ SCIP_LPSOLSTAT_OPTIMAL
Definition: type_lp.h:43
SCIP_LPSOLSTAT_UNBOUNDEDRAY
@ SCIP_LPSOLSTAT_UNBOUNDEDRAY
Definition: type_lp.h:45
SCIP_VERBLEVEL_MINIMAL
@ SCIP_VERBLEVEL_MINIMAL
Definition: type_message.h:54
SCIP_NLPTERMSTAT_OKAY
@ SCIP_NLPTERMSTAT_OKAY
Definition: type_nlpi.h:173
SCIP_NLPTERMSTAT_ITERLIMIT
@ SCIP_NLPTERMSTAT_ITERLIMIT
Definition: type_nlpi.h:175
SCIP_NLPTERMSTAT_OUTOFMEMORY
@ SCIP_NLPTERMSTAT_OUTOFMEMORY
Definition: type_nlpi.h:180
SCIP_NLPSOLSTAT_GLOBINFEASIBLE
@ SCIP_NLPSOLSTAT_GLOBINFEASIBLE
Definition: type_nlpi.h:164
SCIP_NLPSOLSTAT_LOCINFEASIBLE
@ SCIP_NLPSOLSTAT_LOCINFEASIBLE
Definition: type_nlpi.h:163
SCIP_NLPSOLSTAT_FEASIBLE
@ SCIP_NLPSOLSTAT_FEASIBLE
Definition: type_nlpi.h:162
SCIP_PARAMSETTING_OFF
@ SCIP_PARAMSETTING_OFF
Definition: type_paramset.h:63
SCIP_PARAMSETTING_DEFAULT
@ SCIP_PARAMSETTING_DEFAULT
Definition: type_paramset.h:59
SCIP_PARAMSETTING_FAST
@ SCIP_PARAMSETTING_FAST
Definition: type_paramset.h:62
SCIP_PARAMSETTING
enum SCIP_ParamSetting SCIP_PARAMSETTING
Definition: type_paramset.h:65
SCIP_DIDNOTRUN
@ SCIP_DIDNOTRUN
Definition: type_result.h:42
SCIP_CUTOFF
@ SCIP_CUTOFF
Definition: type_result.h:48
SCIP_DELAYED
@ SCIP_DELAYED
Definition: type_result.h:43
SCIP_DIDNOTFIND
@ SCIP_DIDNOTFIND
Definition: type_result.h:44
SCIP_FOUNDSOL
@ SCIP_FOUNDSOL
Definition: type_result.h:56
SCIP_RESULT
enum SCIP_Result SCIP_RESULT
Definition: type_result.h:61
SCIP_OKAY
@ SCIP_OKAY
Definition: type_retcode.h:42
SCIP_ERROR
@ SCIP_ERROR
Definition: type_retcode.h:43
SCIP_RETCODE
enum SCIP_Retcode SCIP_RETCODE
Definition: type_retcode.h:63
SCIP_STAGE_SOLVED
@ SCIP_STAGE_SOLVED
Definition: type_set.h:54
SCIP_STAGE_PRESOLVING
@ SCIP_STAGE_PRESOLVING
Definition: type_set.h:49
SCIP_STAGE_SOLVING
@ SCIP_STAGE_SOLVING
Definition: type_set.h:53
SCIP_STAGE_PRESOLVED
@ SCIP_STAGE_PRESOLVED
Definition: type_set.h:51
SCIP_STATUS_OPTIMAL
@ SCIP_STATUS_OPTIMAL
Definition: type_stat.h:61
SCIP_STATUS_TOTALNODELIMIT
@ SCIP_STATUS_TOTALNODELIMIT
Definition: type_stat.h:45
SCIP_STATUS_BESTSOLLIMIT
@ SCIP_STATUS_BESTSOLLIMIT
Definition: type_stat.h:57
SCIP_STATUS_SOLLIMIT
@ SCIP_STATUS_SOLLIMIT
Definition: type_stat.h:56
SCIP_STATUS_UNBOUNDED
@ SCIP_STATUS_UNBOUNDED
Definition: type_stat.h:63
SCIP_STATUS_GAPLIMIT
@ SCIP_STATUS_GAPLIMIT
Definition: type_stat.h:53
SCIP_STATUS_USERINTERRUPT
@ SCIP_STATUS_USERINTERRUPT
Definition: type_stat.h:43
SCIP_STATUS_INFORUNBD
@ SCIP_STATUS_INFORUNBD
Definition: type_stat.h:64
SCIP_STATUS_STALLNODELIMIT
@ SCIP_STATUS_STALLNODELIMIT
Definition: type_stat.h:48
SCIP_STATUS_TIMELIMIT
@ SCIP_STATUS_TIMELIMIT
Definition: type_stat.h:51
SCIP_STATUS_INFEASIBLE
@ SCIP_STATUS_INFEASIBLE
Definition: type_stat.h:62
SCIP_STATUS_NODELIMIT
@ SCIP_STATUS_NODELIMIT
Definition: type_stat.h:44
SCIP_STATUS_MEMLIMIT
@ SCIP_STATUS_MEMLIMIT
Definition: type_stat.h:52
SCIP_HEURTIMING_DURINGLPLOOP
#define SCIP_HEURTIMING_DURINGLPLOOP
Definition: type_timing.h:79
SCIP_VARTYPE_BINARY
@ SCIP_VARTYPE_BINARY
Definition: type_var.h:62