Scippy

SCIP

Solving Constraint Integer Programs

heur_subnlp.c
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2 /* */
3 /* This file is part of the program and library */
4 /* SCIP --- Solving Constraint Integer Programs */
5 /* */
6 /* Copyright 2002-2022 Zuse Institute Berlin */
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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 */
84 struct 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 */
135 static
136 SCIP_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 */
165 static
166 SCIP_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 */
218 static
219 SCIP_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 
396  /* we remember here which way (continuous or not) we went, in case all binary and integer vars get fixed in root */
397  heurdata->continuous = SCIPgetNBinVars(heurdata->subscip) == 0 && SCIPgetNIntVars(heurdata->subscip) == 0;
398  if( !heurdata->continuous )
399  {
400  /* set presolve maxrounds and emphasis; always disable components presolver
401  * heuristics and separators were not copied into subscip, so should not need to switch off
402  */
403  if( !SCIPisParamFixed(heurdata->subscip, "presolving/maxrounds") )
404  {
405  SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "presolving/maxrounds", heurdata->maxpresolverounds) );
406  }
407  SCIP_CALL( SCIPsetPresolving(heurdata->subscip, (SCIP_PARAMSETTING)heurdata->presolveemphasis, TRUE) );
408  if( !SCIPisParamFixed(heurdata->subscip, "constraints/components/maxprerounds") )
409  {
410  SCIP_CALL( SCIPsetIntParam(heurdata->subscip, "constraints/components/maxprerounds", 0) );
411  }
412  }
413  else
414  {
415  /* for continuous problems, disable presolve and move subscip into a stage where it has a NLP
416  * the only reason why we don't solve the NLP in the main SCIP is that we want global variable bounds for the NLP
417  */
418  SCIP_RETCODE retcode;
419 
420  SCIP_CALL( SCIPtransformProb(heurdata->subscip) );
421 
422  SCIP_CALL( SCIPsetPresolving(heurdata->subscip, SCIP_PARAMSETTING_OFF, TRUE) );
423  SCIP_CALL( SCIPpresolve(heurdata->subscip) );
424 
425  if( SCIPgetStage(heurdata->subscip) != SCIP_STAGE_PRESOLVED || SCIPgetNVars(heurdata->subscip) == 0 )
426  {
427  /* presolve found problem infeasible, solved it, or stopped due to some limit
428  * all a bit strange, since problem should be the same as original, presolve was disabled, and we didn't set any limits
429  * we will give up and not run the heuristic
430  */
431  SCIP_CALL( freeSubSCIP(scip, heurdata) );
432  return SCIP_OKAY;
433  }
434 
435  /* 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) */
436  SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 0LL) );
437  retcode = SCIPsolve(heurdata->subscip);
438 
439  /* errors in solving the subproblem should not kill the overall solving process
440  * hence, the return code is caught and a warning is printed
441  */
442  if( retcode != SCIP_OKAY )
443  {
444  SCIPwarningMessage(scip, "Error while initializing subproblem in subnlp heuristic; sub-SCIP terminated with code <%d>\n", retcode);
445  SCIP_CALL( freeSubSCIP(scip, heurdata) );
446  return SCIP_OKAY;
447  }
448 
449  /* If we are in stage "solved" (strange) or have no NLP (also strange), then do not run heuristic, too */
450  if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED || !SCIPisNLPConstructed(heurdata->subscip) )
451  {
452  SCIP_CALL( freeSubSCIP(scip, heurdata) );
453  return SCIP_OKAY;
454  }
455 
456  assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVING);
457  assert(SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_NODELIMIT);
458  assert(SCIPisNLPConstructed(heurdata->subscip));
459  }
460 
461  return SCIP_OKAY;
462 }
463 
464 /** process variable global bound change event */
465 static
466 SCIP_DECL_EVENTEXEC(processVarEvent)
467 {
468  SCIP_HEURDATA* heurdata;
469  SCIP_VAR* var;
470  SCIP_VAR* subvar;
471  int idx;
472 
473  assert(scip != NULL);
474  assert(event != NULL);
475  assert(eventdata != NULL);
476  assert(eventhdlr != NULL);
477 
478  heurdata = (SCIP_HEURDATA*)eventdata;
479  assert(heurdata != NULL);
480 
481  var = SCIPeventGetVar(event);
482  assert(var != NULL);
483 
484  idx = SCIPvarGetProbindex(var);
485  /* if event corresponds to an active variable, we can easily look up the corresponding subvar
486  * if it is an inactive variable that has been copied to the subproblem,
487  * then we need to check the subscip2scip mapping
488  * @todo we could do this faster if we keep the variables mapping from SCIPcopy around
489  */
490  if( idx >= 0 )
491  {
492  assert(idx < heurdata->nvars);
493 
494  subvar = heurdata->var_scip2subscip[idx];
495  }
496  else
497  {
498  for( idx = 0; idx < heurdata->nsubvars; ++idx )
499  {
500  if( heurdata->var_subscip2scip[idx] == var )
501  break;
502  }
503  assert(idx < heurdata->nsubvars);
504  subvar = SCIPgetVars(heurdata->subscip)[idx];
505  }
506  assert(subvar != NULL);
507 
508  if( SCIPeventGetType(event) & SCIP_EVENTTYPE_GLBCHANGED )
509  {
510  SCIP_CALL( SCIPchgVarLbGlobal(heurdata->subscip, subvar, SCIPvarGetLbGlobal(var)) );
511  }
512 
513  if( SCIPeventGetType(event) & SCIP_EVENTTYPE_GUBCHANGED )
514  {
515  SCIP_CALL( SCIPchgVarUbGlobal(heurdata->subscip, subvar, SCIPvarGetUbGlobal(var)) );
516  }
517 
518  return SCIP_OKAY;
519 }
520 
521 /* creates a SCIP_SOL in our SCIP space out of the solution from NLP solver in sub-SCIP */
522 static
523 SCIP_RETCODE createSolFromNLP(
524  SCIP* scip, /**< SCIP data structure */
525  SCIP_HEUR* heur, /**< heuristic data structure */
526  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 */
527  SCIP_HEUR* authorheur /**< the heuristic which should be registered as author of the solution */
528  )
529 {
530  SCIP_HEURDATA* heurdata;
531  SCIP_VAR** vars;
532  int nvars;
533  SCIP_VAR* var;
534  SCIP_VAR* subvar;
535  SCIP_Real solval;
536  int i;
537 
538  assert(scip != NULL);
539  assert(heur != NULL);
540  assert(sol != NULL);
541 
542  heurdata = SCIPheurGetData(heur);
543  assert(heurdata != NULL);
544  assert(SCIPhasNLPSolution(heurdata->subscip));
545 
546  if( *sol == NULL )
547  {
548  SCIP_CALL( SCIPcreateSol(scip, sol, authorheur) );
549  }
550  else
551  {
552  SCIPsolSetHeur(*sol, authorheur);
553  }
554 
555  SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
556 
557  assert(nvars >= heurdata->nvars);
558  for( i = 0; i < heurdata->nvars; ++i )
559  {
560  var = vars[i];
561  assert(var != NULL);
562  assert(SCIPvarIsActive(var)); /* SCIPgetVarsData should have given us only active vars */
563 
564  subvar = heurdata->var_scip2subscip[i];
565  if( subvar == NULL )
566  solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
567  else
568  solval = SCIPvarGetNLPSol(subvar);
569 
570  assert(solval != SCIP_INVALID); /*lint !e777*/
571  SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
572  }
573 
574  for( ; i < nvars; ++i )
575  {
576  var = vars[i];
577  assert(var != NULL);
578  assert(SCIPvarIsActive(var)); /* SCIPgetVarsData should have given us only active vars */
579 
580  solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
581  SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
582  }
583 
584  return SCIP_OKAY;
585 }
586 
587 /** creates SCIP solution from NLP and tries adding to SCIP or only checks feasibility */
588 static
589 SCIP_RETCODE processNLPSol(
590  SCIP* scip, /**< original SCIP data structure */
591  SCIP_HEUR* heur, /**< heuristic data structure */
592  SCIP_HEUR* authorheur, /**< the heuristic that should be the author of solution, if any */
593  SCIP_RESULT* result, /**< buffer to store result FOUNDSOL if a solution has been found and accepted */
594  SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
595 )
596 {
597  SCIP_HEURDATA* heurdata;
598 
599  assert(scip != NULL);
600  assert(heur != NULL);
601  assert(result != NULL);
602 
603  heurdata = SCIPheurGetData(heur);
604  assert(heurdata != NULL);
605 
606  assert(SCIPhasNLPSolution(heurdata->subscip));
607 
608  if( resultsol == NULL )
609  {
610  /* resultsol NULL means we should try adding the sol to SCIP */
611  if( SCIPisLE(scip, SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip)) )
612  {
613  /* solution is feasible and should improve upper bound, so try adding it to SCIP */
614  SCIP_SOL* sol;
615  SCIP_Bool stored;
616 
617  sol = NULL;
618  SCIP_CALL( createSolFromNLP(scip, heur, &sol, authorheur) );
619 
620  heurdata->lastsol = sol; /* remember just the pointer so we might recognize if this solution comes back as startingpoint */
621 #ifdef SCIP_DEBUG
622  /* print the infeasibilities to stdout */
623  SCIP_CALL( SCIPtrySolFree(scip, &sol, TRUE, TRUE, TRUE, FALSE, TRUE, &stored) );
624 #else
625  SCIP_CALL( SCIPtrySolFree(scip, &sol, FALSE, FALSE, TRUE, FALSE, TRUE, &stored) );
626 #endif
627 
628  if( stored )
629  {
630  /* SCIP stored solution (yippi!), so we are done */
631  if( heurdata->nlpverblevel >= 1 )
632  {
633  SCIPinfoMessage(scip, NULL, "SCIP stored solution from NLP solve\n");
634  }
635  else
636  {
637  SCIPdebugMsg(scip, "SCIP stored solution from NLP solve\n");
638  }
639 
640  *result = SCIP_FOUNDSOL;
641  }
642  else
643  {
644  if( heurdata->nlpverblevel >= 1 )
645  {
646  SCIPinfoMessage(scip, NULL, "solution reported by NLP solver not stored by SCIP\n");
647  }
648  else
649  {
650  SCIPdebugMsg(scip, "solution reported by NLP solver not stored by SCIP\n");
651  }
652  }
653  }
654  else if( heurdata->nlpverblevel >= 1 )
655  {
656  SCIPinfoMessage(scip, NULL, "subnlp solution objval %e is above the primal bound %e\n",
657  SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip));
658  }
659  }
660  else
661  {
662  /* only create a solution and pass it back in resultsol, do not add to SCIP */
663  SCIP_Bool feasible;
664 
665  SCIP_CALL( createSolFromNLP(scip, heur, &resultsol, authorheur) );
666 
667  heurdata->lastsol = resultsol;
668 #ifdef SCIP_DEBUG
669  /* print the infeasibilities to stdout */
670  SCIP_CALL( SCIPcheckSol(scip, resultsol, TRUE, TRUE, TRUE, FALSE, TRUE, &feasible) );
671 #else
672  SCIP_CALL( SCIPcheckSol(scip, resultsol, FALSE, FALSE, TRUE, FALSE, TRUE, &feasible) );
673 #endif
674  if( feasible )
675  {
676  /* SCIP find solution feasible, so we are done */
677  if( heurdata->nlpverblevel >= 1 )
678  {
679  SCIPinfoMessage(scip, NULL, "solution reported by NLP solver feasible for SCIP\n");
680  }
681  else
682  {
683  SCIPdebugMsg(scip, "solution reported by NLP solver feasible for SCIP\n");
684  }
685  *result = SCIP_FOUNDSOL;
686  }
687  else
688  {
689  if( heurdata->nlpverblevel >= 1 )
690  {
691  SCIPinfoMessage(scip, NULL, "solution reported by NLP solver not feasible for SCIP\n");
692  }
693  else
694  {
695  SCIPdebugMsg(scip, "solution reported by NLP solver not feasible for SCIP\n");
696  }
697  }
698  }
699 
700  return SCIP_OKAY;
701 }
702 
703 /* creates a SCIP_SOL in our SCIP space out of the SCIP_SOL from a sub-SCIP */
704 static
705 SCIP_RETCODE createSolFromSubScipSol(
706  SCIP* scip, /**< SCIP data structure */
707  SCIP_HEUR* heur, /**< heuristic data structure */
708  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 */
709  SCIP_SOL* subsol, /**< solution of sub-SCIP */
710  SCIP_HEUR* authorheur /**< the heuristic which should be registered as author of the solution */
711  )
712 {
713  SCIP_HEURDATA* heurdata;
714  SCIP_VAR** vars;
715  int nvars;
716  SCIP_VAR* var;
717  SCIP_VAR* subvar;
718  SCIP_Real solval;
719  int i;
720 
721  assert(scip != NULL);
722  assert(heur != NULL);
723  assert(sol != NULL);
724  assert(subsol != NULL);
725 
726  heurdata = SCIPheurGetData(heur);
727  assert(heurdata != NULL);
728 
729  if( *sol == NULL )
730  {
731  SCIP_CALL( SCIPcreateSol(scip, sol, authorheur) );
732  }
733  else
734  {
735  SCIPsolSetHeur(*sol, authorheur);
736  }
737 
738  SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
739 
740  assert(nvars >= heurdata->nvars);
741  for( i = 0; i < heurdata->nvars; ++i )
742  {
743  var = vars[i];
744  assert(var != NULL);
745  assert(SCIPvarIsActive(var));
746 
747  subvar = heurdata->var_scip2subscip[i];
748  if( subvar == NULL )
749  solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
750  else
751  solval = SCIPgetSolVal(heurdata->subscip, subsol, subvar);
752 
753  assert(solval != SCIP_INVALID); /*lint !e777*/
754  SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
755  }
756 
757  for( ; i < nvars; ++i )
758  {
759  var = vars[i];
760  assert(var != NULL);
761  assert(SCIPvarIsActive(var));
762 
763  solval = MIN(MAX(0.0, SCIPvarGetLbLocal(var)), SCIPvarGetUbLocal(var)); /*lint !e666*/
764  SCIP_CALL( SCIPsetSolVal(scip, *sol, var, solval) );
765  }
766 
767  return SCIP_OKAY;
768 }
769 
770 /** finds an iteration limit */ /*lint --e{715}*/
771 static
772 int calcIterLimit(
773  SCIP* scip, /**< original SCIP data structure */
774  SCIP_HEURDATA* heurdata /**< heuristic data */
775  )
776 {
777  /* if we hit more often an iterlimit than we were successful (termstatus=okay), then allow for more iterations:
778  * take twice the maximal iterusage on solves that hit the iterlimit
779  */
780  if( heurdata->nnlpsolvesiterlim > heurdata->nnlpsolvesokay )
781  return MAX(heurdata->itermin, 2 * heurdata->iterusediterlim); /*lint !e712*/
782 
783  /* if we had sufficiently many successful solves, then take twice the average of previous iterusages on successful solves */
784  if( heurdata->nnlpsolvesokay >= heurdata->ninitsolves )
785  return MAX(heurdata->itermin, 2 * heurdata->iterusedokay / heurdata->nnlpsolvesokay); /*lint !e712*/
786 
787  /* if we had too few successful solves, then still ensure that we allow for at least iterinit iterations */
788  if( heurdata->nnlpsolvesokay > 0 )
789  return MAX3(heurdata->itermin, heurdata->iterinit, 2 * heurdata->iterusedokay / heurdata->nnlpsolvesokay); /*lint !e712*/
790 
791  /* 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 */
792  return MAX(heurdata->itermin, heurdata->iterinit);
793 }
794 
795 /** solves the subNLP specified in subscip */
796 static
797 SCIP_RETCODE solveSubNLP(
798  SCIP* scip, /**< original SCIP data structure */
799  SCIP_HEUR* heur, /**< heuristic data structure */
800  SCIP_RESULT* result, /**< buffer to store result, DIDNOTFIND, FOUNDSOL, or CUTOFF */
801  SCIP_SOL* refpoint, /**< point to take fixation of discrete variables from, and startpoint for NLP solver; if NULL, then LP solution is used */
802  SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
803  )
804 {
805  SCIP_HEURDATA* heurdata = SCIPheurGetData(heur);
806  SCIP_RETCODE retcode;
807  SCIP_Real* startpoint;
808  SCIP_VAR* var;
809  SCIP_VAR* subvar;
810  int i;
811  SCIP_HEUR* authorheur; /* the heuristic which will be the author of a solution, if found */
812  SCIP_Real timelimit;
813  SCIP_Bool expectinfeas;
814  SCIP_NLPSTATISTICS nlpstatistics;
815 
816  assert(scip != NULL);
817  assert(heur != NULL);
818  assert(heurdata != NULL);
819  assert(result != NULL);
820  assert(SCIPisTransformed(heurdata->subscip));
821 
822  /* get remaining SCIP solve time; if no time left, then stop */
823  SCIP_CALL( SCIPgetRealParam(scip, "limits/time", &timelimit) );
824  if( !SCIPisInfinity(scip, timelimit) )
825  {
826  timelimit -= SCIPgetSolvingTime(scip);
827  if( timelimit <= 0.0 )
828  return SCIP_OKAY;
829  }
830  /* set timelimit for NLP solve and in case presolve is unexpectedly expensive */
831  SCIP_CALL( SCIPsetRealParam(heurdata->subscip, "limits/time", timelimit) );
832 
833  /* if the refpoint comes from a heuristic, then make it the author of a found solution,
834  * otherwise let the subNLP heuristic claim authorship
835  * TODO: I doubt that this has much effect; for the statistics, the number of solutions found by a heuristic
836  * seems to be computed as the increase in number of solutions before and after a heuristic is run
837  * check this and maybe change
838  */
839  if( refpoint == NULL || SCIPsolGetHeur(refpoint) == NULL )
840  authorheur = heur;
841  else
842  authorheur = SCIPsolGetHeur(refpoint);
843 
844  if( !heurdata->continuous )
845  {
846  /* presolve sub-SCIP
847  * set node limit to 1 so that presolve can go
848  */
849  SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 1LL) );
850  SCIP_CALL( SCIPpresolve(heurdata->subscip) );
851 
852  /* count one presolve round as on NLP iteration for now
853  * plus one extra for all the setup cost
854  * this is mainly to avoid that the primal heuristics runs all the time on instances that are solved in the subscip-presolve
855  */
856  heurdata->iterused += 1 + SCIPgetNPresolRounds(scip); /*lint !e776*/
857 
858  if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED )
859  {
860  /* presolve probably found the subproblem infeasible */
861  SCIPdebugMsg(scip, "SCIP returned from presolve in stage solved with status %d and %d sols\n", SCIPgetStatus(heurdata->subscip), SCIPgetNSols(heurdata->subscip));
862  /* if presolve found subproblem infeasible, report this to caller by setting *result to cutoff */
863  if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
864  *result = SCIP_CUTOFF;
865  }
866  else if( SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVING )
867  {
868  /* presolve was stopped because some still existing limit was hit (e.g., memory) */
869  SCIPdebugMsg(scip, "SCIP returned from presolve in stage presolving with status %d and %d sols\n", SCIPgetStatus(heurdata->subscip), SCIPgetNSols(heurdata->subscip));
870  /* if presolve found subproblem infeasible, report this to caller by setting *result to cutoff */
871  if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
872  *result = SCIP_CUTOFF;
873  }
874  else
875  {
876  assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVED);
877 
878  if( SCIPgetNVars(heurdata->subscip) > 0 )
879  {
880  /* 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) */
881  SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 0LL) );
882  retcode = SCIPsolve(heurdata->subscip);
883 
884  /* If no NLP was constructed, then there were no nonlinearities after presolve.
885  * So we increase the nodelimit to 1 and hope that SCIP will find some solution to this probably linear subproblem.
886  */
887  if( retcode == SCIP_OKAY && SCIPgetStage(heurdata->subscip) != SCIP_STAGE_SOLVED && !SCIPisNLPConstructed(heurdata->subscip) )
888  {
889  SCIP_CALL( SCIPsetLongintParam(heurdata->subscip, "limits/nodes", 1LL) );
890  retcode = SCIPsolve(heurdata->subscip);
891  }
892  }
893  else
894  {
895  /* If all variables were removed by presolve, but presolve did not end with status SOLVED,
896  * then we run solve, still with nodelimit=1, and hope to find some (maybe trivial) solution.
897  */
898  retcode = SCIPsolve(heurdata->subscip);
899  }
900 
901  /* errors in solving the subproblem should not kill the overall solving process
902  * hence, the return code is caught and a warning is printed
903  */
904  if( retcode != SCIP_OKAY )
905  {
906  SCIPwarningMessage(scip, "Error while solving subproblem in subnlp heuristic; sub-SCIP terminated with code <%d>\n", retcode);
907  return SCIP_OKAY;
908  }
909  }
910 
911  /* we should either have variables, or the problem was trivial, in which case it should have been presolved or solved */
912  assert(SCIPgetNVars(heurdata->subscip) > 0 || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_PRESOLVING || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED);
913 
914  SCIPdebug( SCIP_CALL( SCIPprintStatistics(heurdata->subscip, NULL) ); )
915 
916  /* if sub-SCIP found solutions already, then pass them to main scip */
917  for( i = 0; i < SCIPgetNSols(heurdata->subscip); ++i )
918  {
919  if( resultsol == NULL )
920  {
921  SCIP_Bool stored;
922  SCIP_SOL* sol;
923 
924  sol = NULL;
925  SCIP_CALL( createSolFromSubScipSol(scip, heur, &sol, SCIPgetSols(heurdata->subscip)[i], authorheur) );
926 
927  heurdata->lastsol = sol; /* remember just the pointer so we might recognize if this solution comes back as startingpoint */
928  SCIP_CALL( SCIPtrySolFree(scip, &sol, FALSE, FALSE, TRUE, FALSE, TRUE, &stored) );
929  if( stored )
930  {
931  if( heurdata->nlpverblevel >= 1 )
932  {
933  SCIPinfoMessage(scip, NULL, "SCIP stored solution from sub-SCIP root node\n");
934  }
935  else
936  {
937  SCIPdebugMsg(scip, "SCIP stored solution from sub-SCIP root node\n");
938  }
939  *result = SCIP_FOUNDSOL;
940  break;
941  }
942  else
943  {
944  if( heurdata->nlpverblevel >= 1 )
945  {
946  SCIPinfoMessage(scip, NULL, "SCIP did not store sub-SCIP optimal solution\n");
947  }
948  else
949  {
950  SCIPdebugMsg(scip, "SCIP did not store sub-SCIP optimal solution\n");
951  }
952  }
953  }
954  else
955  {
956  SCIP_Bool feasible;
957 
958  SCIP_CALL( createSolFromSubScipSol(scip, heur, &resultsol, SCIPgetSols(heurdata->subscip)[i], authorheur) );
959 
960  heurdata->lastsol = resultsol;
961  SCIP_CALL( SCIPcheckSol(scip, resultsol, FALSE, FALSE, TRUE, FALSE, TRUE, &feasible) );
962  if( feasible )
963  {
964  if( heurdata->nlpverblevel >= 1 )
965  {
966  SCIPinfoMessage(scip, NULL, "SCIP solution from sub-SCIP root node is feasible\n");
967  }
968  else
969  {
970  SCIPdebugMsg(scip, "SCIP solution from sub-SCIP root node is feasible\n");
971  }
972  *result = SCIP_FOUNDSOL;
973  break;
974  }
975  else
976  {
977  if( heurdata->nlpverblevel >= 1 )
978  {
979  SCIPinfoMessage(scip, NULL, "SCIP solution from sub-SCIP root node is not feasible\n");
980  }
981  else
982  {
983  SCIPdebugMsg(scip, "SCIP solution from sub-SCIP root node is not feasible\n");
984  }
985  }
986  }
987  }
988 
989  /* if subscip is infeasible here, we signal this to the caller */
990  if( SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_INFEASIBLE )
991  {
992  if( heurdata->nlpverblevel >= 1 )
993  {
994  SCIPinfoMessage(scip, NULL, "sub-SCIP detected infeasibility\n");
995  }
996  else
997  {
998  SCIPdebugMsg(scip, "sub-SCIP detected infeasibility\n");
999  }
1000 
1001  assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED);
1002  *result = SCIP_CUTOFF;
1003  return SCIP_OKAY;
1004  }
1005 
1006  /* if we stopped for some other reason, or there is no NLP, we also stop */
1007  if( SCIPgetStage(heurdata->subscip) <= SCIP_STAGE_PRESOLVED || SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVED || !SCIPisNLPConstructed(heurdata->subscip) )
1008  return SCIP_OKAY;
1009 
1010  /* in most cases, the status should be nodelimit
1011  * in some cases, if the sub-SCIP is very easy, it may report optimal, so we do not need invoke an NLP solver
1012  * if the presolve found the problem infeasible, then there is no use in solving an NLP
1013  * if the user interrupted or a timelimit was reached, then we should also stop here
1014  * unbounded is very unlikely to happen, in most cases, it should have been concluded in the main scip already
1015  */
1016  switch( SCIPgetStatus(heurdata->subscip) )
1017  {
1018  case SCIP_STATUS_NODELIMIT:
1019  break; /* this is the status that is most likely happening */
1020  case SCIP_STATUS_TOTALNODELIMIT:
1021  case SCIP_STATUS_STALLNODELIMIT:
1022  case SCIP_STATUS_GAPLIMIT:
1023  case SCIP_STATUS_SOLLIMIT:
1024  case SCIP_STATUS_BESTSOLLIMIT:
1025  /* these should not happen, but if one does, it's safe to return */
1026  SCIPABORT(); /*lint -fallthrough*/
1027  case SCIP_STATUS_OPTIMAL:
1028  case SCIP_STATUS_INFEASIBLE:
1029  case SCIP_STATUS_USERINTERRUPT:
1030  case SCIP_STATUS_TIMELIMIT:
1031  case SCIP_STATUS_MEMLIMIT:
1032  case SCIP_STATUS_UNBOUNDED:
1033  case SCIP_STATUS_INFORUNBD:
1034  return SCIP_OKAY;
1035  default:
1036  SCIPerrorMessage("unexpected status of sub-SCIP: <%d>\n", SCIPgetStatus(heurdata->subscip));
1037  return SCIP_ERROR;
1038  } /*lint !e788*/
1039  }
1040  else
1041  {
1042  /* for continuous problem, createSubSCIP() should have put us into a state where we can invoke the NLP solver */
1043  assert(SCIPisNLPConstructed(heurdata->subscip));
1044  assert(SCIPgetStage(heurdata->subscip) == SCIP_STAGE_SOLVING);
1045  assert(SCIPgetStatus(heurdata->subscip) == SCIP_STATUS_NODELIMIT);
1046  }
1047 
1048  /* set starting values (=refpoint, if not NULL; otherwise LP solution (or pseudo solution)) */
1049  SCIP_CALL( SCIPallocBufferArray(scip, &startpoint, SCIPgetNNLPVars(heurdata->subscip)) );
1050 
1051  if( heurdata->nlpverblevel >= 3 )
1052  {
1053  SCIPinfoMessage(scip, NULL, "set NLP starting point\n");
1054  }
1055 
1056  for( i = 0; i < SCIPgetNNLPVars(heurdata->subscip); ++i )
1057  {
1058  SCIP_Real scalar;
1059  SCIP_Real constant;
1060 
1061  subvar = SCIPgetNLPVars(heurdata->subscip)[i];
1062 
1063  /* gets corresponding original variable */
1064  scalar = 1.0;
1065  constant = 0.0;
1066  SCIP_CALL( SCIPvarGetOrigvarSum(&subvar, &scalar, &constant) );
1067  if( subvar == NULL )
1068  {
1069  startpoint[i] = constant;
1070 
1071  if( heurdata->nlpverblevel >= 3 && !SCIPisZero(heurdata->subscip, startpoint[i]) )
1072  {
1073  SCIPinfoMessage(scip, NULL, "%s = %e\n", SCIPvarGetName(SCIPgetNLPVars(heurdata->subscip)[i]), startpoint[i]);
1074  }
1075 
1076  continue;
1077  }
1078 
1079  assert(SCIPvarGetProbindex(subvar) >= 0);
1080  assert(SCIPvarGetProbindex(subvar) < heurdata->nsubvars);
1081  var = heurdata->var_subscip2scip[SCIPvarGetProbindex(subvar)];
1082  if( var == NULL || REALABS(SCIPgetSolVal(scip, refpoint, var)) > 1.0e+12 )
1083  startpoint[i] = MIN(MAX(0.0, SCIPvarGetLbGlobal(subvar)), SCIPvarGetUbGlobal(subvar)); /*lint !e666*/
1084  else
1085  /* scalar*subvar+constant corresponds to nlpvar[i], so nlpvar[i] gets value scalar*varval+constant */
1086  startpoint[i] = scalar * SCIPgetSolVal(scip, refpoint, var) + constant;
1087 
1088  if( heurdata->nlpverblevel >= 3 && !SCIPisZero(heurdata->subscip, startpoint[i]) )
1089  {
1090  SCIPinfoMessage(scip, NULL, "%s = %e\n", SCIPvarGetName(SCIPgetNLPVars(heurdata->subscip)[i]), startpoint[i]);
1091  }
1092  }
1093  SCIP_CALL( SCIPsetNLPInitialGuess(heurdata->subscip, startpoint) );
1094 
1095  SCIPfreeBufferArray(scip, &startpoint);
1096 
1097  *result = SCIP_DIDNOTFIND;
1098 
1099  /* if we had many (fraction > expectinfeas) infeasible NLPs, then tell NLP solver to expect an infeasible problem */
1100  expectinfeas = FALSE;
1101  if( heurdata->expectinfeas == 0.0 ) /* to keep original behavior on default settings */
1102  expectinfeas = TRUE;
1103  else if( heurdata->nnlpsolvesokay > heurdata->ninitsolves && heurdata->nnlpsolvesinfeas > heurdata->expectinfeas * heurdata->nnlpsolvesokay )
1104  expectinfeas = TRUE;
1105 
1106  /* let the NLP solver do its magic */
1107  SCIPdebugMsg(scip, "start NLP solve with iteration limit %d\n", calcIterLimit(scip, heurdata));
1108  SCIP_CALL( SCIPsolveNLP(heurdata->subscip,
1109  .iterlimit = calcIterLimit(scip, heurdata),
1110  .opttol = heurdata->opttol,
1111  .feastol = heurdata->feastol,
1112  .verblevel = (unsigned short)heurdata->nlpverblevel,
1113  .expectinfeas = expectinfeas
1114  ) ); /*lint !e666*/
1115 
1116  SCIPdebugMsg(scip, "NLP solver returned with termination status %d and solution status %d, objective value is %g\n",
1117  SCIPgetNLPTermstat(heurdata->subscip), SCIPgetNLPSolstat(heurdata->subscip), SCIPgetNLPObjval(heurdata->subscip));
1118 
1119  /* add NLP solve statistics from subscip to main SCIP, so they show up in final statistics
1120  * for continuous problems, we also ask to reset statistics, since we do not retransform subSCIP in the next run (which would reset all stats)
1121  * (merging statistics once in exitsol is too late, since they may be printed before)
1122  */
1123  SCIPmergeNLPIStatistics(heurdata->subscip, scip, heurdata->continuous);
1124 
1125  if( SCIPgetNLPTermstat(heurdata->subscip) >= SCIP_NLPTERMSTAT_OUTOFMEMORY )
1126  {
1127  /* oops, something did not go well at all */
1128  if( heurdata->nlpverblevel >= 1 )
1129  {
1130  SCIPinfoMessage(scip, NULL, "NLP solver in subNLP heuristic for problem <%s> returned with bad termination status %d.\n",
1131  SCIPgetProbName(scip), SCIPgetNLPTermstat(heurdata->subscip));
1132  }
1133 
1134  ++(heurdata->nseriousnlpierror);
1135  SCIPverbMessage(scip, SCIP_VERBLEVEL_MINIMAL, NULL,
1136  "NLP solver in subNLP heuristic for problem <%s> returned with bad termination status %d. This was the %d%s successive time.\n",
1137  SCIPgetProbName(scip), SCIPgetNLPTermstat(heurdata->subscip), heurdata->nseriousnlpierror,
1138  heurdata->nseriousnlpierror == 1 ? "st" : heurdata->nseriousnlpierror == 2 ? "nd" : heurdata->nseriousnlpierror == 3 ? "rd" : "th");
1139  if( heurdata->nseriousnlpierror >= 5 )
1140  {
1141  SCIPverbMessage(scip, SCIP_VERBLEVEL_MINIMAL, NULL, "Will not run subNLP heuristic again for this run.\n");
1142  SCIP_CALL( freeSubSCIP(scip, heurdata) );
1143  }
1144  return SCIP_OKAY;
1145  }
1146  heurdata->nseriousnlpierror = 0;
1147 
1148  SCIP_CALL( SCIPgetNLPStatistics(heurdata->subscip, &nlpstatistics) );
1149 
1150  SCIPdebugMsg(scip, "NLP solver used %d iterations and %g seconds; violation cons %g, bounds %g\n",
1151  nlpstatistics.niterations, nlpstatistics.totaltime, nlpstatistics.consviol, nlpstatistics.boundviol);
1152 
1153  heurdata->iterused += nlpstatistics.niterations;
1154  ++heurdata->nnlpsolves;
1155  if( SCIPgetNLPTermstat(heurdata->subscip) == SCIP_NLPTERMSTAT_OKAY )
1156  {
1157  ++heurdata->nnlpsolvesokay;
1158  heurdata->iterusedokay += nlpstatistics.niterations;
1159 
1160  if( (SCIPgetNLPSolstat(heurdata->subscip) == SCIP_NLPSOLSTAT_GLOBINFEASIBLE) || (SCIPgetNLPSolstat(heurdata->subscip) == SCIP_NLPSOLSTAT_LOCINFEASIBLE) )
1161  ++heurdata->nnlpsolvesinfeas;
1162  }
1163  else if( SCIPgetNLPTermstat(heurdata->subscip) == SCIP_NLPTERMSTAT_ITERLIMIT )
1164  {
1165  ++heurdata->nnlpsolvesiterlim;
1166  heurdata->iterusediterlim = MAX(heurdata->iterusediterlim, nlpstatistics.niterations);
1167  }
1168 
1169  if( SCIPgetNLPSolstat(heurdata->subscip) > SCIP_NLPSOLSTAT_FEASIBLE )
1170  return SCIP_OKAY;
1171 
1172  /* create SCIP solution, check whether feasible, and try adding to SCIP (if resultsol==NULL) */
1173  SCIP_CALL( processNLPSol(scip, heur, authorheur, result, resultsol) );
1174 
1175  if( *result == SCIP_FOUNDSOL || !SCIPisLE(scip, SCIPgetNLPObjval(heurdata->subscip), SCIPgetUpperbound(scip)) )
1176  return SCIP_OKAY;
1177 
1178  /* if solution was not added to SCIP, then either
1179  * - the objective function value was not good enough,
1180  * - the NLP was missing some constraints of the original CIP, or
1181  * - the solution is feasible for the presolved CIP, but slightly infeasible for the unpresolved problem
1182  *
1183  * The first case we can check easily (see if() above).
1184  * For the last case, we try whether tightening the feasibility tolerance for the NLP solve may help.
1185  * If that doesn't help, we guess that we are in the second case and will not try a tighter feastol anymore.
1186  */
1187 
1188  /* if we tried with a tighter feastol before, but solution was still not accepted, then don't try again */
1189  if( heurdata->tighterfeastolfailed )
1190  return SCIP_OKAY;
1191 
1192  /* if resolve with tighter feastol is disabled, then don't do anything */
1193  if( heurdata->feastolfactor == 1.0 )
1194  return SCIP_OKAY;
1195 
1196  /* if we have already used a tighter feastol, then give up */
1197  if( heurdata->feastol < SCIPfeastol(scip) )
1198  return SCIP_OKAY;
1199 
1200  /* if original CIP is continuous, then we have not done any presolve, so it shouldn't have caused problems */
1201  if( heurdata->continuous )
1202  return SCIP_OKAY;
1203 
1204  /* if solution is NLP-feasible for a tightened tolerance already, then there is no use in resolving with that tighter feastol */
1205  if( MAX(nlpstatistics.consviol, nlpstatistics.boundviol) <= heurdata->feastolfactor * heurdata->feastol )
1206  return SCIP_OKAY;
1207 
1208  /* let the NLP solver redo its magic
1209  * as iterlimit, we use the number of iterations it took for the first solve, or itermin
1210  */
1211  SCIPdebugMsg(scip, "start NLP solve with iteration limit %d\n", calcIterLimit(scip, heurdata));
1212  SCIP_CALL( SCIPsolveNLP(heurdata->subscip,
1213  .iterlimit = MAX(heurdata->itermin, nlpstatistics.niterations),
1214  .opttol = heurdata->opttol,
1215  .feastol = heurdata->feastolfactor * heurdata->feastol,
1216  .verblevel = (unsigned short)heurdata->nlpverblevel,
1217  .warmstart = TRUE
1218  ) ); /*lint !e666*/
1219 
1220  SCIPdebugMsg(scip, "NLP solver returned with termination status %d and solution status %d, objective value is %g\n",
1221  SCIPgetNLPTermstat(heurdata->subscip), SCIPgetNLPSolstat(heurdata->subscip), SCIPgetNLPObjval(heurdata->subscip));
1222 
1223  /* add NLP solve statistics from subscip to main SCIP again, so they show up in final statistics */
1224  SCIPmergeNLPIStatistics(heurdata->subscip, scip, heurdata->continuous);
1225 
1226  /* some serious problem: just pretend it didn't happen */
1227  if( SCIPgetNLPTermstat(heurdata->subscip) >= SCIP_NLPTERMSTAT_OUTOFMEMORY )
1228  return SCIP_OKAY;
1229 
1230  SCIP_CALL( SCIPgetNLPStatistics(heurdata->subscip, &nlpstatistics) );
1231  SCIPdebugMsg(scip, "NLP solver used %d iterations and %g seconds; violation cons %g, bounds %g\n",
1232  nlpstatistics.niterations, nlpstatistics.totaltime, nlpstatistics.consviol, nlpstatistics.boundviol);
1233 
1234  /* we account only the extra iterations for this unusual NLP solve, but don't add anything else to our statistics (nnlpsolved, etc) */
1235  heurdata->iterused += nlpstatistics.niterations;
1236 
1237  /* if failed to get a feasible NLP solution now, then nothing to do */
1238  if( SCIPgetNLPSolstat(heurdata->subscip) > SCIP_NLPSOLSTAT_FEASIBLE )
1239  return SCIP_OKAY;
1240 
1241  SCIP_CALL( processNLPSol(scip, heur, authorheur, result, resultsol) );
1242 
1243  /* if successful, then use tighter feastol for all NLP solves from now on
1244  * if still not accepted, then don't try this again
1245  * (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)
1246  */
1247  if( *result == SCIP_FOUNDSOL )
1248  heurdata->feastol *= heurdata->feastolfactor;
1249  else
1250  heurdata->tighterfeastolfailed = TRUE;
1251 
1252  return SCIP_OKAY;
1253 }
1254 
1255 
1256 /** adds a set covering or bound disjunction constraint to the original problem */
1257 static
1258 SCIP_RETCODE forbidFixation(
1259  SCIP* scip, /**< SCIP data structure */
1260  SCIP_HEURDATA* heurdata /**< heuristic data */
1261  )
1262 {
1263  SCIP_VAR** subvars;
1264  int nsubvars;
1265  int nsubbinvars;
1266  int nsubintvars;
1267  SCIP_VAR* var;
1268  SCIP_VAR* subvar;
1269  SCIP_CONS* cons;
1270  SCIP_VAR** consvars;
1271  int nconsvars;
1272  char name[SCIP_MAXSTRLEN];
1273  int i;
1274  SCIP_Real fixval;
1275 
1276  assert(scip != NULL);
1277 
1278  SCIP_CALL( SCIPgetOrigVarsData(heurdata->subscip, &subvars, &nsubvars, &nsubbinvars, &nsubintvars, NULL, NULL) );
1279  assert(nsubvars == heurdata->nsubvars);
1280 
1281  if( nsubbinvars == 0 && nsubintvars == 0 )
1282  {
1283  /* If we did not fix any discrete variables but found the "sub"CIP infeasible, then also the CIP is infeasible. */
1284  SCIPdebugMsg(scip, "heur_subnlp found subCIP infeasible after fixing no variables, something is strange here...\n");
1285  return SCIP_OKAY;
1286  }
1287 
1288  /* initialize */
1289  cons = NULL;
1290  consvars = NULL;
1291 
1292  /* create constraint name */
1293  (void) SCIPsnprintf(name, SCIP_MAXSTRLEN, "subnlp_cutoff");
1294 
1295  /* if all discrete variables in the CIP are binary, then we create a set covering constraint
1296  * sum_{x_i fixed at 0} x_i + sum_{x_i fixed at 1} ~x_i >= 1
1297  */
1298  if( nsubintvars == 0 )
1299  {
1300  /* allocate memory for constraint variables */
1301  SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nsubbinvars) );
1302 
1303  /* get fixations of discrete variables
1304  * to be sure, we take the values that were put into the subCIP before
1305  */
1306  nconsvars = 0;
1307  for( i = nsubbinvars - 1; i >= 0; --i )
1308  {
1309  subvar = subvars[i];
1310  assert(SCIPvarGetProbindex(subvar) == i);
1311 
1312  var = heurdata->var_subscip2scip[i];
1313  assert(var != NULL || SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetUbGlobal(subvar))); /* otherwise we should have exited in the variable fixation loop */
1314  if( var == NULL )
1315  continue;
1316 
1317  fixval = SCIPvarGetLbGlobal(subvar);
1318  assert(fixval == SCIPvarGetUbGlobal(subvar)); /* variable should be fixed in sub-SCIP */ /*lint !e777*/
1319  assert(fixval == 0.0 || fixval == 1.0); /* we have rounded values before fixing */
1320 
1321  if( fixval == 0.0 )
1322  {
1323  /* variable fixed at lower bound */
1324  consvars[nconsvars] = var;
1325  }
1326  else
1327  {
1328  SCIP_CALL( SCIPgetNegatedVar(scip, var, &consvars[nconsvars]) );
1329  }
1330 
1331  ++nconsvars;
1332  }
1333 
1334  /* create conflict constraint
1335  * In undercover, ConsLogicor is used, since then the inequality is not added to the LP.
1336  * However, I may want to use Setcover to avoid that the same fixing is computed by some LP based heuristic again.
1337  */
1338  SCIP_CALL( SCIPcreateConsSetcover(scip, &cons, name, nconsvars, consvars,
1339  FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
1340  }
1341  else
1342  {
1343  /* if there are also integer variable, then create a bound disjunction constraint
1344  * x_1 >= fixval_1 + 1 || x_1 <= fixval_1 - 1 || x_2 >= fixval_2 + 1 || x_2 <= fixval_2 - 1 || ...
1345  */
1346  SCIP_BOUNDTYPE* boundtypes;
1347  SCIP_Real* bounds;
1348 
1349  /* allocate memory for constraint variables, boundtypes, and bounds
1350  * (there should be at most two literals for each integer variable)
1351  */
1352  SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nsubbinvars + 2*nsubintvars) );
1353  SCIP_CALL( SCIPallocBufferArray(scip, &boundtypes, nsubbinvars + 2*nsubintvars) );
1354  SCIP_CALL( SCIPallocBufferArray(scip, &bounds, nsubbinvars + 2*nsubintvars) );
1355 
1356  /* get fixations of discrete variables
1357  * to be sure, we take the values that were put into the subCIP before
1358  */
1359  nconsvars = 0;
1360  for( i = nsubbinvars + nsubintvars - 1; i >= 0; --i )
1361  {
1362  subvar = subvars[i];
1363  assert(SCIPvarGetProbindex(subvar) == i);
1364 
1365  var = heurdata->var_subscip2scip[i];
1366  assert(var != NULL || SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetUbGlobal(subvar))); /* otherwise we should have exited in the variable fixation loop */
1367 
1368  if( var == NULL )
1369  continue;
1370 
1371  fixval = SCIPvarGetLbGlobal(subvar);
1372  assert(fixval == SCIPvarGetUbGlobal(subvar)); /* variable should be fixed in sub-SCIP */ /*lint !e777*/
1373  assert(SCIPceil(scip, fixval - 0.5) == fixval); /* we have rounded values before fixing */ /*lint !e777*/
1374  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*/
1375 
1376  if( SCIPvarGetLbGlobal(var) < fixval )
1377  {
1378  assert(nconsvars < nsubbinvars + 2*nsubintvars);
1379 
1380  /* literal x_i <= fixval-1 */
1381  boundtypes[nconsvars] = SCIP_BOUNDTYPE_UPPER;
1382  bounds[nconsvars] = fixval - 1.0;
1383  consvars[nconsvars] = var;
1384  ++nconsvars;
1385  }
1386 
1387  if( SCIPvarGetUbGlobal(var) > fixval )
1388  {
1389  assert(nconsvars < nsubbinvars + 2*nsubintvars);
1390 
1391  /* literal x_i >= fixval+1 */
1392  boundtypes[nconsvars] = SCIP_BOUNDTYPE_LOWER;
1393  bounds[nconsvars] = fixval + 1.0;
1394  consvars[nconsvars] = var;
1395  ++nconsvars;
1396  }
1397  }
1398 
1399  /* create conflict constraint */
1400  SCIP_CALL( SCIPcreateConsBounddisjunction(scip, &cons, name, nconsvars, consvars, boundtypes, bounds,
1401  FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
1402 
1403  SCIPfreeBufferArray(scip, &bounds);
1404  SCIPfreeBufferArray(scip, &boundtypes);
1405  SCIPfreeBufferArray(scip, &consvars);
1406  }
1407 
1408  /* add and release constraint if created successfully */
1409  if( cons != NULL )
1410  {
1411  SCIPdebugMsg(scip, "adding constraint to forbid fixation in main problem\n");
1412  /* SCIPdebugPrintCons(scip, cons, NULL); */
1413  SCIP_CALL( SCIPaddCons(scip, cons) );
1414  SCIP_CALL( SCIPreleaseCons(scip, &cons) );
1415  }
1416 
1417  /* free memory */
1418  SCIPfreeBufferArrayNull(scip, &consvars);
1419 
1420  return SCIP_OKAY;
1421 }
1422 
1423 
1424 /*
1425  * Callback methods of primal heuristic
1426  */
1427 
1428 /** copy method for primal heuristic plugins (called when SCIP copies plugins) */
1429 static
1430 SCIP_DECL_HEURCOPY(heurCopySubNlp)
1431 { /*lint --e{715}*/
1432  assert(scip != NULL);
1433  assert(heur != NULL);
1434  assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
1435 
1436  /* call inclusion method of primal heuristic */
1437  SCIP_CALL( SCIPincludeHeurSubNlp(scip) );
1438 
1439  return SCIP_OKAY;
1440 }
1441 
1442 /** destructor of primal heuristic to free user data (called when SCIP is exiting) */
1443 static
1444 SCIP_DECL_HEURFREE(heurFreeSubNlp)
1445 {
1446  SCIP_HEURDATA* heurdata;
1447  assert(scip != NULL);
1448  assert(heur != NULL);
1449 
1450  heurdata = SCIPheurGetData(heur);
1451  assert(heurdata != NULL);
1452  assert(heurdata->subscip == NULL);
1453  assert(heurdata->var_subscip2scip == NULL);
1454  assert(heurdata->var_scip2subscip == NULL);
1455  assert(heurdata->startcand == NULL);
1456 
1457  SCIPfreeBlockMemory(scip, &heurdata);
1458 
1459  return SCIP_OKAY;
1460 }
1461 
1462 /** initialization method of primal heuristic (called after problem was transformed) */
1463 static
1464 SCIP_DECL_HEURINIT(heurInitSubNlp)
1465 { /*lint --e{715}*/
1466  SCIP_HEURDATA* heurdata;
1467 
1468  assert(scip != NULL);
1469  assert(heur != NULL);
1470 
1471  heurdata = SCIPheurGetData(heur);
1472  assert(heurdata != NULL);
1473  assert(heurdata->subscip == NULL);
1474 
1475  /* reset or initialize some flags and counters */
1476  heurdata->feastol = SCIPfeastol(scip);
1477  heurdata->tighterfeastolfailed = FALSE;
1478  heurdata->triedsetupsubscip = FALSE;
1479  heurdata->nseriousnlpierror = 0;
1480  heurdata->iterused = 0;
1481  heurdata->iterusedokay = 0;
1482  heurdata->iterusediterlim = 0;
1483  heurdata->nnlpsolves = 0;
1484  heurdata->nnlpsolvesokay = 0;
1485  heurdata->nnlpsolvesiterlim = 0;
1486  heurdata->nnlpsolvesinfeas = 0;
1487 
1488  return SCIP_OKAY;
1489 }
1490 
1491 /** solving process initialization method of primal heuristic (called when branch and bound process is about to begin) */
1492 static
1493 SCIP_DECL_HEURINITSOL(heurInitsolSubNlp)
1494 {
1495  assert(scip != NULL);
1496  assert(heur != NULL);
1497 
1498  /* if the heuristic is called at the root node, we want to be called directly after the initial root LP solve */
1499  if( SCIPheurGetFreqofs(heur) == 0 )
1500  SCIPheurSetTimingmask(heur, SCIP_HEURTIMING_DURINGLPLOOP | HEUR_TIMING);
1501 
1502  return SCIP_OKAY;
1503 }
1504 
1505 /** solving process deinitialization method of primal heuristic (called before branch and bound process data is freed) */
1506 static
1507 SCIP_DECL_HEUREXITSOL(heurExitsolSubNlp)
1508 {
1509  SCIP_HEURDATA* heurdata;
1510  assert(scip != NULL);
1511  assert(heur != NULL);
1512 
1513  /* get heuristic's data */
1514  heurdata = SCIPheurGetData(heur);
1515  assert(heurdata != NULL);
1516 
1517  if( heurdata->subscip != NULL )
1518  {
1519  SCIP_CALL( freeSubSCIP(scip, heurdata) );
1520  heurdata->triedsetupsubscip = FALSE;
1521  }
1522 
1523  /* free start candidate */
1524  if( heurdata->startcand != NULL )
1525  {
1526  SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1527  }
1528 
1529  SCIPheurSetTimingmask(heur, HEUR_TIMING);
1530 
1531  return SCIP_OKAY;
1532 }
1533 
1534 
1535 /** execution method of primal heuristic */
1536 static
1537 SCIP_DECL_HEUREXEC(heurExecSubNlp)
1538 { /*lint --e{666,715}*/
1539  SCIP_HEURDATA* heurdata;
1540  SCIP_Bool runheur;
1541  SCIP_Real itercontingent;
1542 
1543  assert(scip != NULL);
1544  assert(heur != NULL);
1545 
1546  /* obviously, we did not do anything yet */
1547  *result = SCIP_DIDNOTRUN;
1548 
1549  /* get heuristic's data */
1550  heurdata = SCIPheurGetData(heur);
1551  assert(heurdata != NULL);
1552 
1553  /* if triedsetupsubscip and keepcopy and subscip == NULL, then we tried to setup a subSCIP before, but failed due to some serious error
1554  * thus, we should do not need to try again
1555  *
1556  * otherwise, we continue and let SCIPapplyHeurSubNlp try to create subscip
1557  */
1558  if( heurdata->subscip == NULL && heurdata->keepcopy && heurdata->triedsetupsubscip )
1559  return SCIP_OKAY;
1560 
1561  /* before we run the heuristic for the first time, check whether we want to run the heuristic at all */
1562  if( SCIPheurGetNCalls(heur) == 0 )
1563  {
1564  SCIP_CALL( runHeuristic(scip, &runheur) );
1565  if( !runheur )
1566  return SCIP_OKAY;
1567  }
1568 
1569  if( heurdata->startcand == NULL )
1570  {
1571  /* if no start candidate is given, we consider the LP solution of the current node */
1572 
1573  /* however, if the node was already detected to be infeasible, then there is no point to look at its LP solution */
1574  if( nodeinfeasible )
1575  return SCIP_OKAY;
1576 
1577  /* at least if we are not called the first time, we call the heuristic only if an optimal LP solution is available
1578  * if we are called the first time and the LP is unbounded, then we are quite desperate and still give the NLP a try
1579  */
1580  if( SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
1581  {
1582  if( SCIPgetNNodes(scip) > 1 || SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_UNBOUNDEDRAY )
1583  {
1584  *result = SCIP_DELAYED;
1585  SCIPdebugMsg(scip, "NLP heuristic delayed because no start candidate given and no LP solution available; LP status = %d\n", SCIPgetLPSolstat(scip));
1586  return SCIP_OKAY;
1587  }
1588  else
1589  {
1590  SCIPdebugMsg(scip, "LP is unbounded in root node, so we are quite desperate; run NLP heuristic and pray\n");
1591  }
1592  }
1593  else if( SCIPgetNLPBranchCands(scip) > 0 )
1594  {
1595  /* only call heuristic, if there are no fractional variables */
1596  *result = SCIP_DELAYED;
1597  SCIPdebugMsg(scip, "NLP heuristic delayed because no start candidate given and current LP solution is fractional\n");
1598  return SCIP_OKAY;
1599  }
1600  else if( !SCIPisInfinity(scip, SCIPgetPrimalbound(scip)) && SCIPisEQ(scip, SCIPgetLocalDualbound(scip), SCIPgetPrimalbound(scip)) )
1601  {
1602  /* only call heuristic, if there is still room for improvement in the current node */
1603  SCIPdebugMsg(scip, "NLP heuristic delayed because lower and upper bound coincide in current node\n");
1604  return SCIP_OKAY;
1605  }
1606  SCIPdebugMsg(scip, "using current LP solution as startcand\n");
1607  }
1608  else
1609  {
1610  SCIPdebugMsg(scip, "have startcand from heur %s\n", SCIPsolGetHeur(heurdata->startcand) ? SCIPheurGetName(SCIPsolGetHeur(heurdata->startcand)) : "NULL");
1611  }
1612 
1613  /* check if enough nodes have been processed so that we want to run the heuristic again */
1614 
1615  /* compute the contingent on number of iterations that the NLP solver is allowed to use
1616  * we make it depending on the current number of processed nodes
1617  */
1618  itercontingent = heurdata->nodesfactor * (SCIPgetNNodes(scip) + heurdata->nodesoffset);
1619  /* weight by previous success of heuristic if we have been running already
1620  * require at least ninitsolves many runs that didn't run into the NLP iterlimit
1621  * (so if we are still in the phase of finding a good iterlimit, do not consider success rate so far)
1622  */
1623  if( heurdata->successrateexp > 0.0 && SCIPheurGetNCalls(heur) - heurdata->nnlpsolvesiterlim >= heurdata->ninitsolves )
1624  itercontingent *= pow((SCIPheurGetNSolsFound(heur) + 1.0) / (SCIPheurGetNCalls(heur) + 1.0), heurdata->successrateexp);
1625  /* subtract the number of iterations used for all NLP solves so far */
1626  itercontingent -= heurdata->iterused;
1627 
1628  /* check whether the itercontingent is sufficient for the iteration limit we would use */
1629  if( itercontingent < calcIterLimit(scip, heurdata) )
1630  {
1631  /* not enough iterations left to start NLP solver */
1632  SCIPdebugMsg(scip, "skip NLP heuristic; contingent=%f; iterlimit=%d; success ratio=%g\n",
1633  itercontingent, calcIterLimit(scip, heurdata), pow((SCIPheurGetNSolsFound(heur) + 1.0) / (SCIPheurGetNCalls(heur) + 1.0), heurdata->successrateexp));
1634  return SCIP_OKAY;
1635  }
1636 
1637  /* so far we have not found any solution, but now we are willing to search for one */
1638  *result = SCIP_DIDNOTFIND;
1639 
1640  if( heurdata->nlpverblevel >= 1 )
1641  {
1642  SCIPinfoMessage(scip, NULL, "calling subnlp heuristic\n");
1643  }
1644 
1645  SCIP_CALL( SCIPapplyHeurSubNlp(scip, heur, result, heurdata->startcand, NULL) );
1646 
1647  /* SCIP does not like cutoff as return, so we say didnotfind, since we did not find a solution */
1648  if( *result == SCIP_CUTOFF )
1649  *result = SCIP_DIDNOTFIND;
1650 
1651  /* forget startcand */
1652  if( heurdata->startcand != NULL )
1653  {
1654  SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1655  }
1656 
1657  /* reset timing, if it was changed temporary (at the root node) */
1658  if( heurtiming != HEUR_TIMING )
1659  SCIPheurSetTimingmask(heur, HEUR_TIMING);
1660 
1661  return SCIP_OKAY;
1662 }
1663 
1664 
1665 /*
1666  * primal heuristic specific interface methods
1667  */
1668 
1669 /** creates the NLP local search primal heuristic and includes it in SCIP */
1670 SCIP_RETCODE SCIPincludeHeurSubNlp(
1671  SCIP* scip /**< SCIP data structure */
1672  )
1673 {
1674  SCIP_HEURDATA* heurdata;
1675  SCIP_HEUR* heur;
1676 
1677  /* create Nlp primal heuristic data */
1678  SCIP_CALL( SCIPallocBlockMemory(scip, &heurdata) );
1679  BMSclearMemory(heurdata);
1680 
1681  /* include variable event handler */
1682  heurdata->eventhdlr = NULL;
1683  SCIP_CALL( SCIPincludeEventhdlrBasic(scip, &heurdata->eventhdlr, HEUR_NAME, "propagates a global bound change to the sub-SCIP",
1684  processVarEvent, NULL) );
1685  assert(heurdata->eventhdlr != NULL);
1686 
1687  /* include primal heuristic */
1688  SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
1689  HEUR_NAME, HEUR_DESC, HEUR_DISPCHAR, HEUR_PRIORITY, HEUR_FREQ, HEUR_FREQOFS,
1690  HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecSubNlp, heurdata) );
1691 
1692  assert(heur != NULL);
1693 
1694  /* set non-NULL pointers to callback methods */
1695  SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopySubNlp) );
1696  SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeSubNlp) );
1697  SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitSubNlp) );
1698  SCIP_CALL( SCIPsetHeurInitsol(scip, heur, heurInitsolSubNlp) );
1699  SCIP_CALL( SCIPsetHeurExitsol(scip, heur, heurExitsolSubNlp) );
1700 
1701  /* add Nlp primal heuristic parameters */
1702  SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/nlpverblevel",
1703  "verbosity level of NLP solver",
1704  &heurdata->nlpverblevel, FALSE, 0, 0, USHRT_MAX, NULL, NULL) );
1705 
1706  SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/nodesoffset",
1707  "number of nodes added to the current number of nodes when computing itercontingent (higher value runs heuristic more often in early search)",
1708  &heurdata->nodesoffset, FALSE, 1600, 0, INT_MAX, NULL, NULL) );
1709 
1710  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/nodesfactor",
1711  "factor on number of nodes in SCIP (plus nodesoffset) to compute itercontingent (higher value runs heuristics more frequently)",
1712  &heurdata->nodesfactor, FALSE, 0.3, 0.0, SCIPinfinity(scip), NULL, NULL) );
1713 
1714  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/successrateexp",
1715  "exponent for power of success rate to be multiplied with itercontingent (lower value decreases impact of success rate)",
1716  &heurdata->successrateexp, FALSE, 1.0, 0.0, DBL_MAX, NULL, NULL) );
1717 
1718  SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/iterinit",
1719  "number of iterations used for initial NLP solves",
1720  &heurdata->iterinit, FALSE, 300, 0, INT_MAX, NULL, NULL) );
1721 
1722  SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/ninitsolves",
1723  "number of successful NLP solves until switching to iterlimit guess and using success rate",
1724  &heurdata->ninitsolves, FALSE, 2, 0, INT_MAX, NULL, NULL) );
1725 
1726  SCIP_CALL( SCIPaddIntParam (scip, "heuristics/" HEUR_NAME "/itermin",
1727  "minimal number of iterations for NLP solves",
1728  &heurdata->itermin, FALSE, 20, 0, INT_MAX, NULL, NULL) );
1729 
1730  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/opttol",
1731  "absolute optimality tolerance to use for NLP solves",
1732  &heurdata->opttol, TRUE, SCIPdualfeastol(scip), 0.0, 1.0, NULL, NULL) );
1733 
1734  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/feastolfactor",
1735  "factor on SCIP feasibility tolerance for NLP solves if resolving when NLP solution not feasible in CIP",
1736  &heurdata->feastolfactor, FALSE, 0.1, 0.0, 1.0, NULL, NULL) );
1737 
1738  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/maxpresolverounds",
1739  "limit on number of presolve rounds in sub-SCIP (-1 for unlimited, 0 for no presolve)",
1740  &heurdata->maxpresolverounds, FALSE, -1, -1, INT_MAX, NULL, NULL) );
1741 
1742  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/presolveemphasis",
1743  "presolve emphasis in sub-SCIP (0: default, 1: aggressive, 2: fast, 3: off)",
1744  &heurdata->presolveemphasis, FALSE, (int)SCIP_PARAMSETTING_FAST, (int)SCIP_PARAMSETTING_DEFAULT, (int)SCIP_PARAMSETTING_OFF, NULL, NULL) );
1745 
1746  SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/setcutoff",
1747  "whether to set cutoff in sub-SCIP to current primal bound",
1748  &heurdata->setcutoff, FALSE, TRUE, NULL, NULL) );
1749 
1750  SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/forbidfixings",
1751  "whether to add constraints that forbid specific fixings that turned out to be infeasible",
1752  &heurdata->forbidfixings, FALSE, FALSE, NULL, NULL) );
1753 
1754  SCIP_CALL( SCIPaddBoolParam (scip, "heuristics/" HEUR_NAME "/keepcopy",
1755  "whether to keep SCIP copy or to create new copy each time heuristic is applied",
1756  &heurdata->keepcopy, TRUE, TRUE, NULL, NULL) );
1757 
1758  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/expectinfeas",
1759  "percentage of NLP solves with infeasible status required to tell NLP solver to expect an infeasible NLP",
1760  &heurdata->expectinfeas, FALSE, 0.0, 0.0, 1.0, NULL, NULL) );
1761 
1762  return SCIP_OKAY;
1763 }
1764 
1765 /** main procedure of the subNLP heuristic */
1766 SCIP_RETCODE SCIPapplyHeurSubNlp(
1767  SCIP* scip, /**< original SCIP data structure */
1768  SCIP_HEUR* heur, /**< heuristic data structure */
1769  SCIP_RESULT* result, /**< pointer to store result of: did not run, solution found, no solution found, or fixing is infeasible (cutoff) */
1770  SCIP_SOL* refpoint, /**< point to take fixation of discrete variables from, and startpoint for NLP solver; if NULL, then LP solution is used */
1771  SCIP_SOL* resultsol /**< a solution where to store found solution values, if any, or NULL if to try adding to SCIP */
1772  )
1773 {
1774  SCIP_HEURDATA* heurdata;
1775  SCIP_VAR* var;
1776  SCIP_VAR* subvar;
1777  int i;
1778  SCIP_Real cutoff = SCIPinfinity(scip);
1779 
1780  assert(scip != NULL);
1781  assert(heur != NULL);
1782 
1783  /* get heuristic's data */
1784  heurdata = SCIPheurGetData(heur);
1785  assert(heurdata != NULL);
1786 
1787  /* try to setup NLP if not tried before */
1788  if( heurdata->subscip == NULL && !heurdata->triedsetupsubscip )
1789  {
1790  SCIP_CALL( createSubSCIP(scip, heurdata) );
1791  }
1792 
1793  *result = SCIP_DIDNOTRUN;
1794 
1795  /* if subSCIP could not be created, then do not run */
1796  if( heurdata->subscip == NULL )
1797  return SCIP_OKAY;
1798 
1799  assert(heurdata->nsubvars > 0);
1800  assert(heurdata->var_subscip2scip != NULL);
1801 
1802  /* fix discrete variables in sub-SCIP */
1803  if( !heurdata->continuous )
1804  {
1805  SCIP_Real fixval;
1806  SCIP_VAR** subvars;
1807  int nsubvars;
1808  int nsubbinvars;
1809  int nsubintvars;
1810  SCIP_Bool infeas;
1811  SCIP_Bool tightened;
1812 
1813  /* transform sub-SCIP, so variable fixing are easily undone by free-transform */
1814  assert(!SCIPisTransformed(heurdata->subscip));
1815  SCIP_CALL( SCIPtransformProb(heurdata->subscip) );
1816 
1817  SCIP_CALL( SCIPgetOrigVarsData(heurdata->subscip, &subvars, &nsubvars, &nsubbinvars, &nsubintvars, NULL, NULL) );
1818  assert(nsubvars == heurdata->nsubvars);
1819 
1820  /* fix discrete variables to values in startpoint */
1821  for( i = nsubbinvars + nsubintvars - 1; i >= 0; --i )
1822  {
1823  subvar = subvars[i];
1824  assert(SCIPvarGetProbindex(subvar) == i);
1825 
1826  var = heurdata->var_subscip2scip[i];
1827  assert(var != NULL);
1828 
1829  /* at this point, variables in subscip and in our scip should have same bounds */
1830  assert(SCIPisEQ(scip, SCIPvarGetLbGlobal(subvar), SCIPvarGetLbGlobal(var)));
1831  assert(SCIPisEQ(scip, SCIPvarGetUbGlobal(subvar), SCIPvarGetUbGlobal(var)));
1832 
1833  fixval = SCIPgetSolVal(scip, refpoint, var);
1834 
1835  /* only run heuristic on integer feasible points (unless we are on an unbounded LP) */
1836  if( !SCIPisFeasIntegral(scip, fixval) )
1837  {
1838  if( refpoint != NULL || SCIPgetLPSolstat(scip) == SCIP_LPSOLSTAT_OPTIMAL )
1839  {
1840  SCIPdebugMsg(scip, "skip NLP heuristic because start candidate not integer feasible: var <%s> has value %g\n", SCIPvarGetName(var), fixval);
1841  goto CLEANUP;
1842  }
1843  }
1844  /* if we do not really have a startpoint, then we should take care that we do not fix variables to very large values
1845  * thus, we set to 0.0 here and project on bounds below
1846  */
1847  if( REALABS(fixval) > 1E+10 && refpoint == NULL && SCIPgetLPSolstat(scip) != SCIP_LPSOLSTAT_OPTIMAL )
1848  fixval = 0.0;
1849 
1850  /* fixing variables to infinity causes problems, we should not have been passed such a solution as refpoint */
1851  assert(!SCIPisInfinity(scip, REALABS(fixval)));
1852 
1853  /* round fractional variables to the nearest integer */
1854  fixval = SCIPround(scip, fixval);
1855 
1856  /* adjust value to the global bounds of the corresponding SCIP variable */
1857  fixval = MAX(fixval, SCIPvarGetLbGlobal(var)); /*lint !e666*/
1858  fixval = MIN(fixval, SCIPvarGetUbGlobal(var)); /*lint !e666*/
1859 
1860  /* SCIPdebugMsg(scip, "fix variable <%s> to %g\n", SCIPvarGetName(var), fixval); */
1861  SCIP_CALL( SCIPtightenVarLb(heurdata->subscip, subvar, fixval, TRUE, &infeas, &tightened) );
1862  if( !infeas )
1863  {
1864  SCIP_CALL( SCIPtightenVarUb(heurdata->subscip, subvar, fixval, TRUE, &infeas, &tightened) );
1865  }
1866  if( infeas )
1867  {
1868  SCIPdebugMsg(scip, "skip NLP heuristic because start candidate not feasible: fixing var <%s> to value %g is infeasible\n", SCIPvarGetName(var), fixval);
1869  goto CLEANUP;
1870  }
1871  }
1872 
1873  /* if there is already a solution, possibly add an objective cutoff in sub-SCIP
1874  * we do this here only for problems with discrete variables, since the cutoff may be useful when presolving the subscip
1875  * for the NLP solver, a cutoff is useless at best
1876  */
1877  if( SCIPgetNSols(scip) > 0 && heurdata->setcutoff )
1878  {
1879  cutoff = SCIPgetUpperbound(scip);
1880  assert( !SCIPisInfinity(scip, cutoff) );
1881 
1882  SCIP_CALL( SCIPsetObjlimit(heurdata->subscip, cutoff) );
1883  SCIPdebugMsg(scip, "set objective limit %g\n", cutoff);
1884  }
1885  }
1886  else
1887  {
1888  /* for continuous problems, we should already be in the transformed stage */
1889  assert(SCIPisTransformed(heurdata->subscip));
1890  }
1891 
1892  /* solve the subNLP and try to add solution to SCIP */
1893  SCIP_CALL( solveSubNLP(scip, heur, result, refpoint, resultsol) );
1894 
1895  if( heurdata->subscip == NULL )
1896  {
1897  /* something horrible must have happened that we decided to give up completely on this heuristic */
1898  *result = SCIP_DIDNOTFIND;
1899  return SCIP_OKAY;
1900  }
1901 
1902  if( *result == SCIP_CUTOFF )
1903  {
1904  if( heurdata->subscipisvalid && SCIPgetNActivePricers(scip) == 0 )
1905  {
1906  /* 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 */
1907  if( SCIPisInfinity(scip, cutoff) && heurdata->forbidfixings )
1908  {
1909  SCIP_CALL( forbidFixation(scip, heurdata) );
1910  }
1911  }
1912  else
1913  {
1914  /* if the subNLP turned out to be globally infeasible but we are not sure that we have a valid copy, we change to DIDNOTFIND */
1915  *result = SCIP_DIDNOTFIND;
1916  }
1917  }
1918 
1919  CLEANUP:
1920  if( !heurdata->continuous )
1921  {
1922  SCIP_CALL( SCIPfreeTransform(heurdata->subscip) );
1923  }
1924 
1925  /* if the heuristic was applied before solving has started, then destroy subSCIP, since EXITSOL may not be called
1926  * also if keepcopy is disabled, then destroy subSCIP
1927  */
1928  if( SCIPgetStage(scip) < SCIP_STAGE_SOLVING || !heurdata->keepcopy )
1929  {
1930  SCIP_CALL( freeSubSCIP(scip, heurdata) );
1931  heurdata->triedsetupsubscip = FALSE;
1932  }
1933 
1934  return SCIP_OKAY;
1935 }
1936 
1937 /** updates the starting point for the NLP heuristic
1938  *
1939  * Is called by a constraint handler that handles nonlinear constraints when a check on feasibility of a solution fails.
1940  */
1941 SCIP_RETCODE SCIPupdateStartpointHeurSubNlp(
1942  SCIP* scip, /**< SCIP data structure */
1943  SCIP_HEUR* heur, /**< NLP heuristic */
1944  SCIP_SOL* solcand, /**< solution candidate */
1945  SCIP_Real violation /**< constraint violation of solution candidate */
1946  )
1947 {
1948  SCIP_HEURDATA* heurdata;
1949 
1950  assert(scip != NULL);
1951  assert(heur != NULL);
1952  assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
1953  assert(solcand != NULL);
1954  assert(SCIPisPositive(scip, violation));
1955 
1956  /* too early or the game is over already: no more interest in starting points */
1957  if( SCIPgetStage(scip) != SCIP_STAGE_SOLVING )
1958  return SCIP_OKAY;
1959 
1960  heurdata = SCIPheurGetData(heur);
1961  assert(heurdata != NULL);
1962 
1963  if( heurdata->subscip == NULL )
1964  {
1965  /* 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 */
1966  SCIP_Bool runheur;
1967  if( heurdata->triedsetupsubscip )
1968  return SCIP_OKAY;
1969  if( SCIPheurGetFreq(heur) < 0 )
1970  return SCIP_OKAY;
1971  SCIP_CALL( runHeuristic(scip, &runheur) );
1972  if( !runheur )
1973  return SCIP_OKAY;
1974  }
1975 
1976  /* if the solution is the one we created (last), then it is useless to use it as starting point again
1977  * (we cannot check SCIPsolGetHeur()==heur, as subnlp may not be registered as author of the solution)
1978  */
1979  if( heurdata->lastsol == solcand )
1980  return SCIP_OKAY;
1981 
1982  SCIPdebugMsg(scip, "consider solution candidate with violation %g and objective %g from %s\n",
1983  violation, SCIPgetSolTransObj(scip, solcand), SCIPsolGetHeur(solcand) ? SCIPheurGetName(SCIPsolGetHeur(solcand)) : "tree");
1984 
1985  /* 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 */
1986  if( heurdata->startcand == NULL || violation < heurdata->startcandviol ||
1987  SCIPisRelGT(scip, SCIPgetSolTransObj(scip, heurdata->startcand), SCIPgetSolTransObj(scip, solcand)) )
1988  {
1989  if( heurdata->startcand != NULL )
1990  {
1991  SCIP_CALL( SCIPfreeSol(scip, &heurdata->startcand) );
1992  }
1993  SCIP_CALL( SCIPcreateSolCopy(scip, &heurdata->startcand, solcand) );
1994  SCIP_CALL( SCIPunlinkSol(scip, heurdata->startcand) );
1995  heurdata->startcandviol = violation;
1996 
1997  /* remember which heuristic proposed the candidate */
1998  SCIPsolSetHeur(heurdata->startcand, SCIPgetSolHeur(scip, solcand));
1999  }
2000 
2001  return SCIP_OKAY;
2002 }
2003 
2004 /** gets startpoint candidate to be used in next call to NLP heuristic, or NULL if none */
2005 SCIP_SOL* SCIPgetStartCandidateHeurSubNlp(
2006  SCIP* scip, /**< original SCIP data structure */
2007  SCIP_HEUR* heur /**< heuristic data structure */
2008  )
2009 {
2010  SCIP_HEURDATA* heurdata;
2011 
2012  assert(scip != NULL);
2013  assert(heur != NULL);
2014  assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
2015 
2016  heurdata = SCIPheurGetData(heur);
2017  assert(heurdata != NULL);
2018 
2019  return heurdata->startcand;
2020 }
SCIP_RESULT
enum SCIP_Result SCIP_RESULT
Definition: type_result.h:61
SCIPsetHeurExitsol
SCIP_RETCODE SCIPsetHeurExitsol(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEUREXITSOL((*heurexitsol)))
Definition: scip_heur.c:242
solveSubNLP
static SCIP_RETCODE solveSubNLP(SCIP *scip, SCIP_HEUR *heur, SCIP_RESULT *result, SCIP_SOL *refpoint, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:797
SCIPfreeBlockMemoryArray
#define SCIPfreeBlockMemoryArray(scip, ptr, num)
Definition: scip_mem.h:110
SCIP_BOUNDTYPE
enum SCIP_BoundType SCIP_BOUNDTYPE
Definition: type_lp.h:59
SCIPgetNIntVars
int SCIPgetNIntVars(SCIP *scip)
Definition: scip_prob.c:2090
SCIP_HEURTIMING_DURINGLPLOOP
#define SCIP_HEURTIMING_DURINGLPLOOP
Definition: type_timing.h:80
SCIP_DIDNOTRUN
Definition: type_result.h:42
SCIPhashmapEntryGetImage
void * SCIPhashmapEntryGetImage(SCIP_HASHMAPENTRY *entry)
Definition: misc.c:3519
SCIP_NLPSOLSTAT_GLOBINFEASIBLE
Definition: type_nlpi.h:164
SCIPchgVarLbGlobal
SCIP_RETCODE SCIPchgVarLbGlobal(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound)
Definition: scip_var.c:4949
SCIP_FOUNDSOL
Definition: type_result.h:56
SCIPgetSolvingTime
SCIP_Real SCIPgetSolvingTime(SCIP *scip)
Definition: scip_timing.c:378
createSubSCIP
static SCIP_RETCODE createSubSCIP(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:219
SCIPfeastol
SCIP_Real SCIPfeastol(SCIP *scip)
Definition: scip_numerics.c:167
SCIP_DELAYED
Definition: type_result.h:43
SCIPisNLPConstructed
SCIP_Bool SCIPisNLPConstructed(SCIP *scip)
Definition: scip_nlp.c:110
SCIP_NLPSOLSTAT_LOCINFEASIBLE
Definition: type_nlpi.h:163
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:5209
SCIPisFeasEQ
SCIP_Bool SCIPisFeasEQ(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:782
scip_param.h
public methods for SCIP parameter handling
SCIPgetStage
SCIP_STAGE SCIPgetStage(SCIP *scip)
Definition: scip_general.c:365
scip_mem.h
public methods for memory management
SCIP_DECL_HEURINIT
static SCIP_DECL_HEURINIT(heurInitSubNlp)
Definition: heur_subnlp.c:1464
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
SCIPgetPrimalbound
SCIP_Real SCIPgetPrimalbound(SCIP *scip)
Definition: scip_solvingstats.c:1531
SCIPvarGetLbGlobal
SCIP_Real SCIPvarGetLbGlobal(SCIP_VAR *var)
Definition: var.c:17919
SCIPgetRealParam
SCIP_RETCODE SCIPgetRealParam(SCIP *scip, const char *name, SCIP_Real *value)
Definition: scip_param.c:307
SCIP_MAXSTRLEN
#define SCIP_MAXSTRLEN
Definition: def.h:302
SCIPincludeHeurSubNlp
SCIP_RETCODE SCIPincludeHeurSubNlp(SCIP *scip)
Definition: heur_subnlp.c:1670
SCIPallocClearBlockMemoryArray
#define SCIPallocClearBlockMemoryArray(scip, ptr, num)
Definition: scip_mem.h:97
SCIPisPositive
SCIP_Bool SCIPisPositive(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:572
SCIPvarGetLbLocal
SCIP_Real SCIPvarGetLbLocal(SCIP_VAR *var)
Definition: var.c:17975
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:9330
scip_solve.h
public solving methods
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
scip_timing.h
public methods for timing
SCIPgetNLPSolstat
SCIP_NLPSOLSTAT SCIPgetNLPSolstat(SCIP *scip)
Definition: scip_nlp.c:541
SCIP_Var
Definition: struct_var.h:207
SCIP_STATUS_INFORUNBD
Definition: type_stat.h:64
SCIPhashmapEntryGetOrigin
void * SCIPhashmapEntryGetOrigin(SCIP_HASHMAPENTRY *entry)
Definition: misc.c:3509
SCIPreleaseVar
SCIP_RETCODE SCIPreleaseVar(SCIP *scip, SCIP_VAR **var)
Definition: scip_var.c:1254
createSolFromNLP
static SCIP_RETCODE createSolFromNLP(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL **sol, SCIP_HEUR *authorheur)
Definition: heur_subnlp.c:523
SCIPdualfeastol
SCIP_Real SCIPdualfeastol(SCIP *scip)
Definition: scip_numerics.c:197
SCIP_NlpStatistics::niterations
int niterations
Definition: type_nlpi.h:199
SCIPgetVarsData
SCIP_RETCODE SCIPgetVarsData(SCIP *scip, SCIP_VAR ***vars, int *nvars, int *nbinvars, int *nintvars, int *nimplvars, int *ncontvars)
Definition: scip_prob.c:1874
SCIPgetSols
SCIP_SOL ** SCIPgetSols(SCIP *scip)
Definition: scip_sol.c:2263
FALSE
#define FALSE
Definition: def.h:96
scip_presol.h
public methods for presolving plugins
SCIPhashmapCreate
SCIP_RETCODE SCIPhashmapCreate(SCIP_HASHMAP **hashmap, BMS_BLKMEM *blkmem, int mapsize)
Definition: misc.c:3023
SCIPapplyHeurSubNlp
SCIP_RETCODE SCIPapplyHeurSubNlp(SCIP *scip, SCIP_HEUR *heur, SCIP_RESULT *result, SCIP_SOL *refpoint, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:1766
SCIPgetNActivePricers
int SCIPgetNActivePricers(SCIP *scip)
Definition: scip_pricer.c:348
SCIPinfinity
SCIP_Real SCIPinfinity(SCIP *scip)
Definition: scip_numerics.c:512
SCIPsnprintf
int SCIPsnprintf(char *t, int len, const char *s,...)
Definition: misc.c:10764
TRUE
#define TRUE
Definition: def.h:95
SCIPdebug
#define SCIPdebug(x)
Definition: pub_message.h:93
HEUR_FREQ
#define HEUR_FREQ
Definition: heur_subnlp.c:73
SCIP_RETCODE
enum SCIP_Retcode SCIP_RETCODE
Definition: type_retcode.h:63
SCIP_VARTYPE_BINARY
Definition: type_var.h:62
SCIPsetPresolving
SCIP_RETCODE SCIPsetPresolving(SCIP *scip, SCIP_PARAMSETTING paramsetting, SCIP_Bool quiet)
Definition: scip_param.c:932
SCIPheurGetFreqofs
int SCIPheurGetFreqofs(SCIP_HEUR *heur)
Definition: heur.c:1556
SCIPvarGetProbindex
int SCIPvarGetProbindex(SCIP_VAR *var)
Definition: var.c:17609
SCIP_PARAMSETTING_DEFAULT
Definition: type_paramset.h:59
SCIP_EVENTTYPE_GLBCHANGED
#define SCIP_EVENTTYPE_GLBCHANGED
Definition: type_event.h:75
SCIP_HEURDATA
struct SCIP_HeurData SCIP_HEURDATA
Definition: type_heur.h:76
HEUR_FREQOFS
#define HEUR_FREQOFS
Definition: heur_subnlp.c:74
pub_var.h
public methods for problem variables
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:5326
SCIPfreeBlockMemory
#define SCIPfreeBlockMemory(scip, ptr)
Definition: scip_mem.h:108
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
SCIP_STATUS_MEMLIMIT
Definition: type_stat.h:52
SCIPgetOrigVarsData
SCIP_RETCODE SCIPgetOrigVarsData(SCIP *scip, SCIP_VAR ***vars, int *nvars, int *nbinvars, int *nintvars, int *nimplvars, int *ncontvars)
Definition: scip_prob.c:2365
SCIPhashmapGetImage
void * SCIPhashmapGetImage(SCIP_HASHMAP *hashmap, void *origin)
Definition: misc.c:3210
SCIPisEQ
SCIP_Bool SCIPisEQ(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:447
SCIPfreeBufferArray
#define SCIPfreeBufferArray(scip, ptr)
Definition: scip_mem.h:136
SCIPcreate
SCIP_RETCODE SCIPcreate(SCIP **scip)
Definition: scip_general.c:292
cons_setppc.h
Constraint handler for the set partitioning / packing / covering constraints .
SCIPallocBlockMemory
#define SCIPallocBlockMemory(scip, ptr)
Definition: scip_mem.h:89
SCIPisTransformed
SCIP_Bool SCIPisTransformed(SCIP *scip)
Definition: scip_general.c:575
SCIPgetNLPBranchCands
int SCIPgetNLPBranchCands(SCIP *scip)
Definition: scip_branch.c:428
scip_var.h
public methods for SCIP variables
SCIPsetRealParam
SCIP_RETCODE SCIPsetRealParam(SCIP *scip, const char *name, SCIP_Real value)
Definition: scip_param.c:603
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
createSolFromSubScipSol
static SCIP_RETCODE createSolFromSubScipSol(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL **sol, SCIP_SOL *subsol, SCIP_HEUR *authorheur)
Definition: heur_subnlp.c:705
SCIP_DECL_HEUREXITSOL
static SCIP_DECL_HEUREXITSOL(heurExitsolSubNlp)
Definition: heur_subnlp.c:1507
SCIP_DECL_HEURINITSOL
static SCIP_DECL_HEURINITSOL(heurInitsolSubNlp)
Definition: heur_subnlp.c:1493
SCIPwarningMessage
void SCIPwarningMessage(SCIP *scip, const char *formatstr,...)
Definition: scip_message.c:120
SCIPchgVarUbGlobal
SCIP_RETCODE SCIPchgVarUbGlobal(SCIP *scip, SCIP_VAR *var, SCIP_Real newbound)
Definition: scip_var.c:5038
SCIPdebugMsg
#define SCIPdebugMsg
Definition: scip_message.h:78
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
SCIPcopyParamSettings
SCIP_RETCODE SCIPcopyParamSettings(SCIP *sourcescip, SCIP *targetscip)
Definition: scip_copy.c:2559
SCIPprintStatistics
SCIP_RETCODE SCIPprintStatistics(SCIP *scip, FILE *file)
Definition: scip_solvingstats.c:4136
SCIPinfoMessage
void SCIPinfoMessage(SCIP *scip, FILE *file, const char *formatstr,...)
Definition: scip_message.c:208
SCIPgetStartCandidateHeurSubNlp
SCIP_SOL * SCIPgetStartCandidateHeurSubNlp(SCIP *scip, SCIP_HEUR *heur)
Definition: heur_subnlp.c:2005
SCIP_STATUS_USERINTERRUPT
Definition: type_stat.h:43
scip_numerics.h
public methods for numerical tolerances
SCIPisRelGT
SCIP_Bool SCIPisRelGT(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:1197
SCIP_STATUS_NODELIMIT
Definition: type_stat.h:44
SCIP_PARAMSETTING
enum SCIP_ParamSetting SCIP_PARAMSETTING
Definition: type_paramset.h:65
SCIP_DECL_HEUREXEC
static SCIP_DECL_HEUREXEC(heurExecSubNlp)
Definition: heur_subnlp.c:1537
scip_solvingstats.h
public methods for querying solving statistics
SCIP_Sol
Definition: struct_sol.h:73
SCIPgetProbName
const char * SCIPgetProbName(SCIP *scip)
Definition: scip_prob.c:1075
SCIPgetNNlpis
int SCIPgetNNlpis(SCIP *scip)
Definition: scip_nlpi.c:199
HEUR_DESC
#define HEUR_DESC
Definition: heur_subnlp.c:70
SCIPvarGetUbGlobal
SCIP_Real SCIPvarGetUbGlobal(SCIP_VAR *var)
Definition: var.c:17929
scip_nlpi.h
public methods for NLPI solver interfaces
SCIP_HashMap
Definition: struct_misc.h:137
SCIPgetNNLPVars
int SCIPgetNNLPVars(SCIP *scip)
Definition: scip_nlp.c:201
SCIPcreateSolCopy
SCIP_RETCODE SCIPcreateSolCopy(SCIP *scip, SCIP_SOL **sol, SCIP_SOL *sourcesol)
Definition: scip_sol.c:618
SCIPgetNLPTermstat
SCIP_NLPTERMSTAT SCIPgetNLPTermstat(SCIP *scip)
Definition: scip_nlp.c:563
SCIPsetHeurInitsol
SCIP_RETCODE SCIPsetHeurInitsol(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURINITSOL((*heurinitsol)))
Definition: scip_heur.c:226
SCIP_STATUS_TOTALNODELIMIT
Definition: type_stat.h:45
SCIPsolve
SCIP_RETCODE SCIPsolve(SCIP *scip)
Definition: scip_solve.c:2622
SCIP_NLPSOLSTAT_FEASIBLE
Definition: type_nlpi.h:162
SCIPheurGetName
const char * SCIPheurGetName(SCIP_HEUR *heur)
Definition: heur.c:1450
SCIP_DIDNOTFIND
Definition: type_result.h:44
SCIP_Cons
Definition: struct_cons.h:46
SCIPerrorMessage
#define SCIPerrorMessage
Definition: pub_message.h:64
SCIP_NlpStatistics
Definition: type_nlpi.h:197
SCIPisParamFixed
SCIP_Bool SCIPisParamFixed(SCIP *scip, const char *name)
Definition: scip_param.c:219
SCIPaddCons
SCIP_RETCODE SCIPaddCons(SCIP *scip, SCIP_CONS *cons)
Definition: scip_prob.c:2778
SCIP_PARAMSETTING_OFF
Definition: type_paramset.h:63
SCIPhashmapGetNEntries
int SCIPhashmapGetNEntries(SCIP_HASHMAP *hashmap)
Definition: misc.c:3490
SCIPhashmapGetEntry
SCIP_HASHMAPENTRY * SCIPhashmapGetEntry(SCIP_HASHMAP *hashmap, int entryidx)
Definition: misc.c:3498
SCIPsetHeurFree
SCIP_RETCODE SCIPsetHeurFree(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURFREE((*heurfree)))
Definition: scip_heur.c:178
HEUR_DISPCHAR
#define HEUR_DISPCHAR
Definition: heur_subnlp.c:71
scip_event.h
public methods for event handler plugins and event handlers
SCIPhasNLPContinuousNonlinearity
SCIP_RETCODE SCIPhasNLPContinuousNonlinearity(SCIP *scip, SCIP_Bool *result)
Definition: scip_nlp.c:125
forbidFixation
static SCIP_RETCODE forbidFixation(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:1258
SCIP_BOUNDTYPE_LOWER
Definition: type_lp.h:56
SCIP_STATUS_OPTIMAL
Definition: type_stat.h:61
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
SCIPfreeBufferArrayNull
#define SCIPfreeBufferArrayNull(scip, ptr)
Definition: scip_mem.h:137
SCIPsetBoolParam
SCIP_RETCODE SCIPsetBoolParam(SCIP *scip, const char *name, SCIP_Bool value)
Definition: scip_param.c:429
SCIPgetStatus
SCIP_STATUS SCIPgetStatus(SCIP *scip)
Definition: scip_general.c:483
SCIPpresolve
SCIP_RETCODE SCIPpresolve(SCIP *scip)
Definition: scip_solve.c:2452
SCIPblkmem
BMS_BLKMEM * SCIPblkmem(SCIP *scip)
Definition: scip_mem.c:57
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:3176
freeSubSCIP
static SCIP_RETCODE freeSubSCIP(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:166
SCIP_EVENTDATA
struct SCIP_EventData SCIP_EVENTDATA
Definition: type_event.h:173
SCIPvarGetName
const char * SCIPvarGetName(SCIP_VAR *var)
Definition: var.c:17260
SCIPhashmapFree
void SCIPhashmapFree(SCIP_HASHMAP **hashmap)
Definition: misc.c:3057
SCIP_NlpStatistics::totaltime
SCIP_Real totaltime
Definition: type_nlpi.h:200
NULL
#define NULL
Definition: lpi_spx1.cpp:164
SCIPsolGetHeur
SCIP_HEUR * SCIPsolGetHeur(SCIP_SOL *sol)
Definition: sol.c:2613
SCIPgetSolTransObj
SCIP_Real SCIPgetSolTransObj(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1491
SCIPsetNLPInitialGuess
SCIP_RETCODE SCIPsetNLPInitialGuess(SCIP *scip, SCIP_Real *initialguess)
Definition: scip_nlp.c:441
REALABS
#define REALABS(x)
Definition: def.h:210
SCIPheurGetFreq
int SCIPheurGetFreq(SCIP_HEUR *heur)
Definition: heur.c:1535
SCIPgetLocalDualbound
SCIP_Real SCIPgetLocalDualbound(SCIP *scip)
Definition: scip_prob.c:3574
SCIP_OKAY
Definition: type_retcode.h:42
scip_copy.h
public methods for problem copies
pub_sol.h
public methods for primal CIP solutions
SCIP_CALL
#define SCIP_CALL(x)
Definition: def.h:393
HEUR_TIMING
#define HEUR_TIMING
Definition: heur_subnlp.c:76
SCIPverbMessage
void SCIPverbMessage(SCIP *scip, SCIP_VERBLEVEL msgverblevel, FILE *file, const char *formatstr,...)
Definition: scip_message.c:225
SCIPheurGetNCalls
SCIP_Longint SCIPheurGetNCalls(SCIP_HEUR *heur)
Definition: heur.c:1576
SCIP_Heur
Definition: struct_heur.h:97
SCIP_LPSOLSTAT_OPTIMAL
Definition: type_lp.h:43
scip_heur.h
public methods for primal heuristic plugins and divesets
HEUR_PRIORITY
#define HEUR_PRIORITY
Definition: heur_subnlp.c:72
scip_cons.h
public methods for constraint handler plugins and constraints
SCIP_ERROR
Definition: type_retcode.h:43
nlpi_ipopt.h
Ipopt NLP interface.
SCIPallocBufferArray
#define SCIPallocBufferArray(scip, ptr, num)
Definition: scip_mem.h:124
SCIPsetSolVal
SCIP_RETCODE SCIPsetSolVal(SCIP *scip, SCIP_SOL *sol, SCIP_VAR *var, SCIP_Real val)
Definition: scip_sol.c:1221
pub_misc.h
public data structures and miscellaneous methods
SCIP_NLPTERMSTAT_OUTOFMEMORY
Definition: type_nlpi.h:180
SCIPfreeTransform
SCIP_RETCODE SCIPfreeTransform(SCIP *scip)
Definition: scip_solve.c:3467
SCIPeventGetVar
SCIP_VAR * SCIPeventGetVar(SCIP_EVENT *event)
Definition: event.c:1053
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:3449
SCIP_Bool
#define SCIP_Bool
Definition: def.h:93
SCIPgetNLPStatistics
SCIP_RETCODE SCIPgetNLPStatistics(SCIP *scip, SCIP_NLPSTATISTICS *statistics)
Definition: scip_nlp.c:588
SCIP_DECL_HEURFREE
static SCIP_DECL_HEURFREE(heurFreeSubNlp)
Definition: heur_subnlp.c:1444
SCIPgetLPSolstat
SCIP_LPSOLSTAT SCIPgetLPSolstat(SCIP *scip)
Definition: scip_lp.c:168
SCIPeventGetType
SCIP_EVENTTYPE SCIPeventGetType(SCIP_EVENT *event)
Definition: event.c:1030
SCIP_HashMapEntry
Definition: struct_misc.h:130
SCIPsetObjlimit
SCIP_RETCODE SCIPsetObjlimit(SCIP *scip, SCIP_Real objlimit)
Definition: scip_prob.c:1430
SCIP_NlpStatistics::boundviol
SCIP_Real boundviol
Definition: type_nlpi.h:204
SCIPvarGetNLPSol
SCIP_Real SCIPvarGetNLPSol(SCIP_VAR *var)
Definition: var.c:18306
SCIP_VERBLEVEL_MINIMAL
Definition: type_message.h:52
HEUR_MAXDEPTH
#define HEUR_MAXDEPTH
Definition: heur_subnlp.c:75
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:1725
SCIPsolSetHeur
void SCIPsolSetHeur(SCIP_SOL *sol, SCIP_HEUR *heur)
Definition: sol.c:2658
MAX
#define MAX(x, y)
Definition: tclique_def.h:92
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:3240
SCIPsetIntParam
SCIP_RETCODE SCIPsetIntParam(SCIP *scip, const char *name, int value)
Definition: scip_param.c:487
SCIP_STAGE_PRESOLVING
Definition: type_set.h:49
SCIPfreeSol
SCIP_RETCODE SCIPfreeSol(SCIP *scip, SCIP_SOL **sol)
Definition: scip_sol.c:985
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
SCIPgetNSols
int SCIPgetNSols(SCIP *scip)
Definition: scip_sol.c:2214
HEUR_USESSUBSCIP
#define HEUR_USESSUBSCIP
Definition: heur_subnlp.c:77
SCIP_STAGE_SOLVED
Definition: type_set.h:54
SCIPgetNPresolRounds
int SCIPgetNPresolRounds(SCIP *scip)
Definition: scip_presol.c:288
SCIPisInfinity
SCIP_Bool SCIPisInfinity(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:523
runHeuristic
static SCIP_RETCODE runHeuristic(SCIP *scip, SCIP_Bool *runheur)
Definition: heur_subnlp.c:136
SCIP_STAGE_PRESOLVED
Definition: type_set.h:51
BMSclearMemory
#define BMSclearMemory(ptr)
Definition: memory.h:131
SCIP_EVENTTYPE_GBDCHANGED
#define SCIP_EVENTTYPE_GBDCHANGED
Definition: type_event.h:120
SCIPvarGetOrigvarSum
SCIP_RETCODE SCIPvarGetOrigvarSum(SCIP_VAR **var, SCIP_Real *scalar, SCIP_Real *constant)
Definition: var.c:12782
SCIP_NLPTERMSTAT_OKAY
Definition: type_nlpi.h:173
SCIPgetNBinVars
int SCIPgetNBinVars(SCIP *scip)
Definition: scip_prob.c:2045
scip_lp.h
public methods for the LP relaxation, rows and columns
scip_pricer.h
public methods for variable pricer plugins
SCIPgetNVars
int SCIPgetNVars(SCIP *scip)
Definition: scip_prob.c:2000
scip_nlp.h
public methods for nonlinear relaxation
SCIP_LPSOLSTAT_UNBOUNDEDRAY
Definition: type_lp.h:45
SCIP_STATUS_STALLNODELIMIT
Definition: type_stat.h:48
SCIP_NlpStatistics::consviol
SCIP_Real consviol
Definition: type_nlpi.h:203
SCIPhasNLPSolution
SCIP_Bool SCIPhasNLPSolution(SCIP *scip)
Definition: scip_nlp.c:638
scip_branch.h
public methods for branching rule plugins and branching
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
pub_event.h
public methods for managing events
scip_general.h
general public methods
SCIP_STATUS_GAPLIMIT
Definition: type_stat.h:53
SCIP_STATUS_SOLLIMIT
Definition: type_stat.h:54
scip_sol.h
public methods for solutions
SCIP_DECL_HEURCOPY
static SCIP_DECL_HEURCOPY(heurCopySubNlp)
Definition: heur_subnlp.c:1430
SCIP_BOUNDTYPE_UPPER
Definition: type_lp.h:57
SCIPgetNConss
int SCIPgetNConss(SCIP *scip)
Definition: scip_prob.c:3050
SCIPreleaseCons
SCIP_RETCODE SCIPreleaseCons(SCIP *scip, SCIP_CONS **cons)
Definition: scip_cons.c:1119
pub_message.h
public methods for message output
heur_subnlp.h
NLP local search primal heuristic using sub-SCIPs.
SCIPsetHeurInit
SCIP_RETCODE SCIPsetHeurInit(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURINIT((*heurinit)))
Definition: scip_heur.c:194
HEUR_NAME
#define HEUR_NAME
Definition: heur_subnlp.c:69
SCIPgetVars
SCIP_VAR ** SCIPgetVars(SCIP *scip)
Definition: scip_prob.c:1955
SCIPgetSolHeur
SCIP_HEUR * SCIPgetSolHeur(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1684
SCIPcaptureVar
SCIP_RETCODE SCIPcaptureVar(SCIP *scip, SCIP_VAR *var)
Definition: scip_var.c:1220
SCIP_Real
#define SCIP_Real
Definition: def.h:186
scip_message.h
public methods for message handling
SCIPheurSetTimingmask
void SCIPheurSetTimingmask(SCIP_HEUR *heur, SCIP_HEURTIMING timingmask)
Definition: heur.c:1490
SCIP_INVALID
#define SCIP_INVALID
Definition: def.h:206
SCIP_Longint
#define SCIP_Longint
Definition: def.h:171
SCIPheurGetNSolsFound
SCIP_Longint SCIPheurGetNSolsFound(SCIP_HEUR *heur)
Definition: heur.c:1586
SCIPunlinkSol
SCIP_RETCODE SCIPunlinkSol(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1190
SCIP_STAGE_SOLVING
Definition: type_set.h:53
SCIPvarGetType
SCIP_VARTYPE SCIPvarGetType(SCIP_VAR *var)
Definition: var.c:17425
SCIPtransformProb
SCIP_RETCODE SCIPtransformProb(SCIP *scip)
Definition: scip_solve.c:367
SCIPisZero
SCIP_Bool SCIPisZero(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:560
SCIPgetNLPObjval
SCIP_Real SCIPgetNLPObjval(SCIP *scip)
Definition: scip_nlp.c:612
SCIPisLE
SCIP_Bool SCIPisLE(SCIP *scip, SCIP_Real val1, SCIP_Real val2)
Definition: scip_numerics.c:473
SCIPmergeNLPIStatistics
void SCIPmergeNLPIStatistics(SCIP *sourcescip, SCIP *targetscip, SCIP_Bool reset)
Definition: scip_copy.c:1330
processNLPSol
static SCIP_RETCODE processNLPSol(SCIP *scip, SCIP_HEUR *heur, SCIP_HEUR *authorheur, SCIP_RESULT *result, SCIP_SOL *resultsol)
Definition: heur_subnlp.c:589
SCIPsetHeurCopy
SCIP_RETCODE SCIPsetHeurCopy(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURCOPY((*heurcopy)))
Definition: scip_heur.c:162
SCIPvarGetUbLocal
SCIP_Real SCIPvarGetUbLocal(SCIP_VAR *var)
Definition: var.c:17985
SCIPisFeasIntegral
SCIP_Bool SCIPisFeasIntegral(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:883
SCIPupdateStartpointHeurSubNlp
SCIP_RETCODE SCIPupdateStartpointHeurSubNlp(SCIP *scip, SCIP_HEUR *heur, SCIP_SOL *solcand, SCIP_Real violation)
Definition: heur_subnlp.c:1941
SCIPgetUpperbound
SCIP_Real SCIPgetUpperbound(SCIP *scip)
Definition: scip_solvingstats.c:1555
pub_heur.h
public methods for primal heuristics
SCIP_DECL_EVENTEXEC
static SCIP_DECL_EVENTEXEC(processVarEvent)
Definition: heur_subnlp.c:466
SCIP_PARAMSETTING_FAST
Definition: type_paramset.h:62
SCIP_EVENTTYPE_GUBCHANGED
#define SCIP_EVENTTYPE_GUBCHANGED
Definition: type_event.h:76
SCIPceil
SCIP_Real SCIPceil(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:645
SCIPheurGetData
SCIP_HEURDATA * SCIPheurGetData(SCIP_HEUR *heur)
Definition: heur.c:1361
cons_bounddisjunction.h
constraint handler for bound disjunction constraints
SCIPsolveNLP
#define SCIPsolveNLP(...)
Definition: scip_nlp.h:340
SCIPresetParam
SCIP_RETCODE SCIPresetParam(SCIP *scip, const char *name)
Definition: scip_param.c:814
SCIPgetNNodes
SCIP_Longint SCIPgetNNodes(SCIP *scip)
Definition: scip_solvingstats.c:212
SCIPABORT
#define SCIPABORT()
Definition: def.h:365
scip_prob.h
public methods for global and local (sub)problems
SCIPround
SCIP_Real SCIPround(SCIP *scip, SCIP_Real val)
Definition: scip_numerics.c:657
SCIP_NLPTERMSTAT_ITERLIMIT
Definition: type_nlpi.h:175
SCIPgetSolVal
SCIP_Real SCIPgetSolVal(SCIP *scip, SCIP_SOL *sol, SCIP_VAR *var)
Definition: scip_sol.c:1361
calcIterLimit
static int calcIterLimit(SCIP *scip, SCIP_HEURDATA *heurdata)
Definition: heur_subnlp.c:772
SCIP_STATUS_INFEASIBLE
Definition: type_stat.h:62
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
SCIP_STATUS_BESTSOLLIMIT
Definition: type_stat.h:57
SCIPsetLongintParam
SCIP_RETCODE SCIPsetLongintParam(SCIP *scip, const char *name, SCIP_Longint value)
Definition: scip_param.c:545
SCIP_CUTOFF
Definition: type_result.h:48
SCIP_Eventhdlr
Definition: struct_event.h:204
SCIPgetNegatedVar
SCIP_RETCODE SCIPgetNegatedVar(SCIP *scip, SCIP_VAR *var, SCIP_VAR **negvar)
Definition: scip_var.c:1533
SCIPgetNLPVars
SCIP_VAR ** SCIPgetNLPVars(SCIP *scip)
Definition: scip_nlp.c:179
SCIP_STATUS_TIMELIMIT
Definition: type_stat.h:51
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
SCIPvarIsActive
SCIP_Bool SCIPvarIsActive(SCIP_VAR *var)
Definition: var.c:17589
SCIPfree
SCIP_RETCODE SCIPfree(SCIP **scip)
Definition: scip_general.c:324
SCIPcreateSol
SCIP_RETCODE SCIPcreateSol(SCIP *scip, SCIP_SOL **sol, SCIP_HEUR *heur)
Definition: scip_sol.c:328
SCIP_STATUS_UNBOUNDED
Definition: type_stat.h:63
memory.h
memory allocation routines