Scippy

SCIP

Solving Constraint Integer Programs

heur_trustregion.c
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24 
25 /**@file heur_trustregion.c
26  * @ingroup DEFPLUGINS_HEUR
27  * @brief Large neighborhood search heuristic for Benders' decomposition based on trust region methods
28  * @author Stephen J. Maher
29  *
30  * The Trust Region heuristic draws upon trust region methods for solving optimization problems, especially in the
31  * context of Benders' decomposition. This heuristic has been developed to improve the heuristic performance of the
32  * Benders' decomposition algorithm within SCIP.
33  *
34  * The Trust Region heuristic copies the original SCIP instance and adds a constraint to penalize changes from the
35  * incumbent solution. Consider a problem that includes a set of binary variables \f$\mathcal{B}\f$. Given a feasible
36  * solution \f$\hat{x}\f$ to the original problem, we define the set \f$\mathcal{B}^{+}\f$ as the index set for the
37  * binary variables that are 1 in the input solution and \f$\mathcal{B}^{-}\f$ as the index set for binary variables
38  * that are 0. The trust region constraint, which is added to the sub-SCIP, is given by
39  *
40  * \f[
41  * \sum_{i \in \mathcal{B}^{+}}(1 - x_{i}) + \sum_{i \in \mathcal{B}^{-}}x_{i} \le \theta
42  * \f]
43  *
44  * The variable \f$\theta\f$ measure the distance, in terms of the binary variables, of candidate solutions to the input
45  * solution.
46  *
47  * In addition, an upper bounding constraint is explicitly added to enforce a minimum improvement from the heuristic,
48  * given by \f$f(x) \le f(\hat{x}) - \epsilon\f$. The parameter \f$\epsilon \ge 0\f$ denotes the minimum improvement
49  * that must be achieved by the heuristic.
50  *
51  * The objective function is then modified to \f$f(x) + M\theta\f$, where \f$M\f$ is a parameter for penalizing the
52  * distance of solutions from the input solution \f$\hat{x}\f$.
53  *
54  * If a new incumbent solution is found by this heuristic, then the Trust Region heuristic is immediately
55  * re-executed with this new incumbent solution.
56  */
57 
58 /*---+----1----+----2----+----3----+----4----+----5----+----6----+----7----+----8----+----9----+----0----+----1----+----2*/
59 
60 #include "blockmemshell/memory.h"
61 #include "scip/cons_linear.h"
62 #include "scip/heuristics.h"
63 #include "scip/heur_trustregion.h"
64 #include "scip/pub_event.h"
65 #include "scip/pub_heur.h"
66 #include "scip/pub_message.h"
67 #include "scip/pub_misc.h"
68 #include "scip/pub_sol.h"
69 #include "scip/pub_var.h"
70 #include "scip/scip_branch.h"
71 #include "scip/scip_cons.h"
72 #include "scip/scip_copy.h"
73 #include "scip/scip_event.h"
74 #include "scip/scip_general.h"
75 #include "scip/scip_heur.h"
76 #include "scip/scip_mem.h"
77 #include "scip/scip_message.h"
78 #include "scip/scip_nodesel.h"
79 #include "scip/scip_numerics.h"
80 #include "scip/scip_param.h"
81 #include "scip/scip_prob.h"
82 #include "scip/scip_sol.h"
83 #include "scip/scip_solve.h"
84 #include "scip/scip_solvingstats.h"
85 #include "scip/scip_var.h"
86 #include <string.h>
87 
88 #define HEUR_NAME "trustregion"
89 #define HEUR_DESC "LNS heuristic for Benders' decomposition based on trust region methods"
90 #define HEUR_DISPCHAR SCIP_HEURDISPCHAR_LNS
91 #define HEUR_PRIORITY -1102010
92 #define HEUR_FREQ -1
93 #define HEUR_FREQOFS 0
94 #define HEUR_MAXDEPTH -1
95 #define HEUR_TIMING SCIP_HEURTIMING_AFTERNODE
96 #define HEUR_USESSUBSCIP TRUE /**< does the heuristic use a secondary SCIP instance? */
97 
98 #define DEFAULT_MINBINVARS 10 /**< the minimum number of binary variables necessary to run the heuristic */
99 #define DEFAULT_NODESOFS 1000 /**< number of nodes added to the contingent of the total nodes */
100 #define DEFAULT_MAXNODES 10000 /**< maximum number of nodes to regard in the subproblem */
101 #define DEFAULT_MINNODES 100 /**< minimum number of nodes required to start the subproblem */
102 #define DEFAULT_NODESQUOT 0.05 /**< contingent of sub problem nodes in relation to original nodes */
103 #define DEFAULT_LPLIMFAC 1.5 /**< factor by which the limit on the number of LP depends on the node limit */
104 #define DEFAULT_NWAITINGNODES 1 /**< number of nodes without incumbent change that heuristic should wait */
105 #define DEFAULT_USELPROWS FALSE /**< should subproblem be created out of the rows in the LP rows,
106  * otherwise, the copy constructors of the constraints handlers are used */
107 #define DEFAULT_COPYCUTS TRUE /**< if DEFAULT_USELPROWS is FALSE, then should all active cuts from the cutpool
108  * of the original scip be copied to constraints of the subscip */
109 #define DEFAULT_BESTSOLLIMIT 3 /**< limit on number of improving incumbent solutions in sub-CIP */
110 
111 #define DEFAULT_VIOLPENALTY 100.0 /**< the penalty for violating the trust region */
112 #define DEFAULT_OBJMINIMPROVE 1e-2 /**< the minimum absolute improvement in the objective function value */
114 /* event handler properties */
115 #define EVENTHDLR_NAME "Trustregion"
116 #define EVENTHDLR_DESC "LP event handler for " HEUR_NAME " heuristic"
119 #define EXECUTE 0
120 #define WAITFORNEWSOL 1
123 /*
124  * Data structures
125  */
126 
127 /** primal heuristic data */
128 struct SCIP_HeurData
129 {
130  SCIP_SOL* lastsol; /**< the last incumbent trustregion used as reference point */
131  SCIP_Longint usednodes; /**< amount of nodes trust region used during all calls */
132  SCIP_Real nodesquot; /**< contingent of sub problem nodes in relation to original nodes */
133  SCIP_Real nodelimit; /**< the nodelimit employed in the current sub-SCIP, for the event handler*/
134  SCIP_Real lplimfac; /**< factor by which the limit on the number of LP depends on the node limit */
135  SCIP_Real violpenalty; /**< the penalty for violating the trust region */
136  SCIP_Real objminimprove; /**< the minimum absolute improvement in the objective function value */
137  int nwaitingnodes; /**< number of nodes without incumbent change that heuristic should wait */
138  int nodesofs; /**< number of nodes added to the contingent of the total nodes */
139  int minnodes; /**< minimum number of nodes required to start the subproblem */
140  int maxnodes; /**< maximum number of nodes to regard in the subproblem */
141  int minbinvars; /**< minimum number of binary variables necessary to run the heuristic */
142  int callstatus; /**< current status of trustregion heuristic */
143  int curminnodes; /**< current minimal number of nodes required to start the subproblem */
144  int bestsollimit; /**< limit on number of improving incumbent solutions in sub-CIP */
145  SCIP_Bool uselprows; /**< should subproblem be created out of the rows in the LP rows? */
146  SCIP_Bool copycuts; /**< if uselprows == FALSE, should all active cuts from cutpool be copied
147  * to constraints in subproblem? */
148 };
149 
150 
151 /*
152  * Local methods
153  */
154 
155 /** create the extra constraint of trust region and add it to \p subscip */
156 static
158  SCIP* scip, /**< SCIP data structure of the original problem */
159  SCIP* subscip, /**< SCIP data structure of the subproblem */
160  SCIP_VAR** subvars, /**< variables of the subproblem */
161  SCIP_HEURDATA* heurdata /**< heuristic's data structure */
162  )
163 {
164  SCIP_CONS* cons; /* trust region constraint to create */
165  SCIP_VAR** consvars;
166  SCIP_VAR** vars;
167  SCIP_SOL* bestsol;
168 
169  int nvars;
170  int nbinvars;
171  int nconsvars;
172  int i;
173  SCIP_Real lhs;
174  SCIP_Real rhs;
175  SCIP_Real* consvals;
176  char name[SCIP_MAXSTRLEN];
177 
178  /* adding the neighborhood constraint for the trust region heuristic */
179  SCIP_CALL( SCIPaddTrustregionNeighborhoodConstraint(scip, subscip, subvars, heurdata->violpenalty) );
180 
181  /* get the data of the variables and the best solution */
182  SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, &nbinvars, NULL, NULL, NULL) );
183  bestsol = SCIPgetBestSol(scip);
184  assert( bestsol != NULL );
185 
186  /* memory allocation */
187  SCIP_CALL( SCIPallocBufferArray(scip, &consvars, nvars + 1) );
188  SCIP_CALL( SCIPallocBufferArray(scip, &consvals, nvars + 1) );
189  nconsvars = 0;
190 
191  /* create the upper bounding constraint. An absolute minimum improvement is used for this heuristic. This is
192  * different to other LNS heuristics, where a relative improvement is used. The absolute improvement tries to take
193  * into account problem specific information that is available to the user, such as a minimum step in the objective
194  * limit if the objective function is integer
195  */
196  lhs = -SCIPinfinity(subscip);
197  rhs = SCIPgetSolTransObj(scip, bestsol) - heurdata->objminimprove;
198 
199  /* if the objective function is integer, then the floor of the RHS is taken */
200  if( SCIPisObjIntegral(scip) )
201  rhs = SCIPfeasFloor(scip, rhs);
202 
203  /* adding the coefficients to the upper bounding constraint */
204  for( i = 0; i < nvars; i++ )
205  {
206  if( subvars[i] == NULL )
207  continue;
208  consvals[nconsvars] = SCIPvarGetObj(subvars[i]);
209  consvars[nconsvars] = subvars[i];
210  ++nconsvars;
211  }
212 
213  /* creates trustregion constraint and adds it to subscip */
214  (void) SCIPsnprintf(name, SCIP_MAXSTRLEN, "%s_upperboundcons", SCIPgetProbName(scip));
215 
216  SCIP_CALL( SCIPcreateConsLinear(subscip, &cons, name, nconsvars, consvars, consvals,
217  lhs, rhs, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE) );
218  SCIP_CALL( SCIPaddCons(subscip, cons) );
219  SCIP_CALL( SCIPreleaseCons(subscip, &cons) );
220 
221  /* free local memory */
222  SCIPfreeBufferArray(scip, &consvals);
223  SCIPfreeBufferArray(scip, &consvars);
224 
225  return SCIP_OKAY;
226 }
227 
228 
229 /* ---------------- Callback methods of event handler ---------------- */
230 
231 /** event handler execution callback to interrupt the solution process */
232 static
233 SCIP_DECL_EVENTEXEC(eventExecTrustregion)
234 {
235  SCIP_HEURDATA* heurdata;
236 
237  assert(eventhdlr != NULL);
238  assert(eventdata != NULL);
239  assert(strcmp(SCIPeventhdlrGetName(eventhdlr), EVENTHDLR_NAME) == 0);
240  assert(event != NULL);
241  assert(SCIPeventGetType(event) & SCIP_EVENTTYPE_LPSOLVED);
242 
243  heurdata = (SCIP_HEURDATA*)eventdata;
244  assert(heurdata != NULL);
245 
246  /* interrupt solution process of sub-SCIP */
247  if( SCIPgetNLPs(scip) > heurdata->lplimfac * heurdata->nodelimit )
248  {
249  SCIPdebugMsg(scip, "interrupt after %" SCIP_LONGINT_FORMAT " LPs\n",SCIPgetNLPs(scip));
250  SCIP_CALL( SCIPinterruptSolve(scip) );
251  }
252 
253  return SCIP_OKAY;
254 }
255 
256 
257 /*
258  * Callback methods of primal heuristic
259  */
260 
261 /** copy method for primal heuristic plugins (called when SCIP copies plugins) */
262 static
263 SCIP_DECL_HEURCOPY(heurCopyTrustregion)
264 { /*lint --e{715}*/
265  assert(scip != NULL);
266  assert(heur != NULL);
267  assert(strcmp(SCIPheurGetName(heur), HEUR_NAME) == 0);
268 
269  /* call inclusion method of primal heuristic */
271 
272  return SCIP_OKAY;
273 }
274 
275 /** destructor of primal heuristic to free user data (called when SCIP is exiting) */
276 static
277 SCIP_DECL_HEURFREE(heurFreeTrustregion)
278 { /*lint --e{715}*/
279  SCIP_HEURDATA* heurdata;
280 
281  assert( heur != NULL );
282  assert( scip != NULL );
283 
284  /* get heuristic data */
285  heurdata = SCIPheurGetData(heur);
286  assert( heurdata != NULL );
287 
288  /* free heuristic data */
289  SCIPfreeBlockMemory(scip, &heurdata);
290  SCIPheurSetData(heur, NULL);
291 
292  return SCIP_OKAY;
293 }
294 
295 
296 /** initialization method of primal heuristic (called after problem was transformed) */
297 static
298 SCIP_DECL_HEURINIT(heurInitTrustregion)
299 { /*lint --e{715}*/
300  SCIP_HEURDATA* heurdata;
301 
302  assert( heur != NULL );
303  assert( scip != NULL );
304 
305  /* get heuristic's data */
306  heurdata = SCIPheurGetData(heur);
307  assert( heurdata != NULL );
308 
309  /* with a little abuse we initialize the heurdata as if trustregion would have finished its last step regularly */
310  heurdata->callstatus = WAITFORNEWSOL;
311  heurdata->lastsol = NULL;
312  heurdata->usednodes = 0;
313  heurdata->curminnodes = heurdata->minnodes;
314 
315  return SCIP_OKAY;
316 }
317 
318 /** sets up and solves the sub SCIP for the Trust Region heuristic */
319 static
321  SCIP* scip, /**< SCIP data structure */
322  SCIP* subscip, /**< the subproblem created by trustregion */
323  SCIP_HEUR* heur, /**< trustregion heuristic */
324  SCIP_Longint nsubnodes, /**< nodelimit for subscip */
325  SCIP_RESULT* result /**< result pointer */
326  )
327 {
328  SCIP_VAR** subvars;
329  SCIP_EVENTHDLR* eventhdlr;
330  SCIP_HEURDATA* heurdata;
331  SCIP_HASHMAP* varmapfw;
332  SCIP_VAR** vars;
333 
334  int nvars;
335  int i;
336 
337  SCIP_Bool success;
338 
339  assert(scip != NULL);
340  assert(subscip != NULL);
341  assert(heur != NULL);
342 
343  heurdata = SCIPheurGetData(heur);
344  assert(heurdata != NULL);
345 
346  /* get the data of the variables and the best solution */
347  SCIP_CALL( SCIPgetVarsData(scip, &vars, &nvars, NULL, NULL, NULL, NULL) );
348 
349  /* create the variable mapping hash map */
350  SCIP_CALL( SCIPhashmapCreate(&varmapfw, SCIPblkmem(subscip), nvars) );
351  success = FALSE;
352 
353  /* create a problem copy as sub SCIP */
354  SCIP_CALL( SCIPcopyLargeNeighborhoodSearch(scip, subscip, varmapfw, "trustregion", NULL, NULL, 0, heurdata->uselprows,
355  heurdata->copycuts, &success, NULL) );
356 
357  SCIPdebugMsg(scip, "Copying SCIP was %s successful.\n", success ? "" : "not ");
358 
359  /* if the subproblem could not be created, free memory and return */
360  if( !success )
361  {
362  *result = SCIP_DIDNOTRUN;
363  goto TERMINATE;
364  }
365 
366  /* create event handler for LP events */
367  eventhdlr = NULL;
368  SCIP_CALL( SCIPincludeEventhdlrBasic(subscip, &eventhdlr, EVENTHDLR_NAME, EVENTHDLR_DESC, eventExecTrustregion, NULL) );
369  if( eventhdlr == NULL )
370  {
371  /* free hash map */
372  SCIPhashmapFree(&varmapfw);
373 
374  SCIPerrorMessage("event handler for " HEUR_NAME " heuristic not found.\n");
375  return SCIP_PLUGINNOTFOUND;
376  }
377 
378  SCIP_CALL( SCIPallocBufferArray(scip, &subvars, nvars) );
379  for (i = 0; i < nvars; ++i)
380  subvars[i] = (SCIP_VAR*) SCIPhashmapGetImage(varmapfw, vars[i]);
381 
382  /* free hash map */
383  SCIPhashmapFree(&varmapfw);
384 
385  heurdata->nodelimit = nsubnodes;
386  SCIP_CALL( SCIPsetCommonSubscipParams(scip, subscip, nsubnodes, MAX(10, nsubnodes/10), heurdata->bestsollimit) );
387 
388  SCIP_CALL( addTrustRegionConstraints(scip, subscip, subvars, heurdata) );
389 
390  /* catch LP events of sub-SCIP */
391  if( !heurdata->uselprows )
392  {
393  assert(eventhdlr != NULL);
394 
395  SCIP_CALL( SCIPtransformProb(subscip) );
396  SCIP_CALL( SCIPcatchEvent(subscip, SCIP_EVENTTYPE_LPSOLVED, eventhdlr, (SCIP_EVENTDATA*) heurdata, NULL) );
397  }
398 
399  /* solve the subproblem */
400  SCIPdebugMsg(scip, "solving trust region subproblem with maxnodes %" SCIP_LONGINT_FORMAT "\n", nsubnodes);
401 
402  SCIP_CALL( SCIPsetIntParam(subscip, "heuristics/trysol/priority", 100000) );
403 
404  /* Errors in solving the subproblem should not kill the overall solving process
405  * Hence, the return code is caught and a warning is printed, only in debug mode, SCIP will stop.
406  */
407  SCIP_CALL_ABORT( SCIPsolve(subscip) );
408 
409  /* drop LP events of sub-SCIP */
410  if( !heurdata->uselprows )
411  {
412  assert(eventhdlr != NULL);
413 
414  SCIP_CALL( SCIPdropEvent(subscip, SCIP_EVENTTYPE_LPSOLVED, eventhdlr, (SCIP_EVENTDATA*) heurdata, -1) );
415  }
416 
417  /* print solving statistics of subproblem if we are in SCIP's debug mode */
419 
420  heurdata->usednodes += SCIPgetNNodes(subscip);
421  SCIPdebugMsg(scip, "trust region used %" SCIP_LONGINT_FORMAT "/%" SCIP_LONGINT_FORMAT " nodes\n",
422  SCIPgetNNodes(subscip), nsubnodes);
423 
424  /* checks the solutions of the sub SCIP and adds them to the main SCIP if feasible */
425  SCIP_CALL( SCIPtranslateSubSols(scip, subscip, heur, subvars, &success, NULL) );
426 
427  if( success )
428  *result = SCIP_FOUNDSOL;
429 
430  /* checking the status of the subscip */
431  heurdata->callstatus = WAITFORNEWSOL;
434  {
435  heurdata->callstatus = EXECUTE;
436  heurdata->curminnodes *= 2;
437  }
438 
439  TERMINATE:
440  /* free subproblem */
441  SCIPfreeBufferArray(scip, &subvars);
442 
443  return SCIP_OKAY;
444 }
445 
446 
447 /** execution method of primal heuristic */
448 static
449 SCIP_DECL_HEUREXEC(heurExecTrustregion)
450 { /*lint --e{715}*/
451  SCIP_Longint maxnnodes;
452  SCIP_Longint nsubnodes;
453 
454  SCIP_HEURDATA* heurdata;
455  SCIP* subscip;
456 
457  SCIP_SOL* bestsol;
458 
459  SCIP_Bool success;
460  SCIP_RETCODE retcode;
461 
462  assert(heur != NULL);
463  assert(scip != NULL);
464  assert(result != NULL);
465 
466  *result = SCIP_DIDNOTRUN;
467 
468  /* get heuristic's data */
469  heurdata = SCIPheurGetData(heur);
470  assert( heurdata != NULL );
471 
472  /* there should be enough binary variables that a trust region constraint makes sense */
473  if( SCIPgetNBinVars(scip) < heurdata->minbinvars )
474  return SCIP_OKAY;
475 
476  *result = SCIP_DELAYED;
477 
478  /* only call heuristic, if an IP solution is at hand */
479  if( SCIPgetNSols(scip) <= 0 )
480  return SCIP_OKAY;
481 
482  bestsol = SCIPgetBestSol(scip);
483  assert(bestsol != NULL);
484 
485  /* only call heuristic, if the best solution comes from transformed problem */
486  if( SCIPsolIsOriginal(bestsol) )
487  return SCIP_OKAY;
488 
489  /* only call heuristic, if enough nodes were processed since last incumbent */
490  if( SCIPgetNNodes(scip) - SCIPgetSolNodenum(scip, bestsol) < heurdata->nwaitingnodes)
491  return SCIP_OKAY;
492 
493  /* only call heuristic, if the best solution does not come from trivial heuristic */
494  if( SCIPsolGetHeur(bestsol) != NULL && strcmp(SCIPheurGetName(SCIPsolGetHeur(bestsol)), "trivial") == 0 )
495  return SCIP_OKAY;
496 
497  /* calculate the maximal number of branching nodes until heuristic is aborted */
498  maxnnodes = (SCIP_Longint)(heurdata->nodesquot * SCIPgetNNodes(scip));
499 
500  /* reward trust region if it found solutions often.
501  * In this case, the trust region heuristic is designed for Benders' decomposition and solutions found may not be
502  * added by this heuristic but by trysol. So we don't reward finding best solutions, but finding any solution. */
503  maxnnodes = (SCIP_Longint)(maxnnodes * (1.0 + 2.0*(SCIPheurGetNSolsFound(heur)+1.0)/(SCIPheurGetNCalls(heur)+1.0)));
504  maxnnodes -= 100 * SCIPheurGetNCalls(heur); /* count the setup costs for the sub-MIP as 100 nodes */
505  maxnnodes += heurdata->nodesofs;
506 
507  *result = SCIP_DIDNOTRUN;
508 
509  /* we continue to execute the trust region heuristic until no new best solution is found */
510  do
511  {
512  SCIP_RESULT heurresult;
513 
514  /* storing the best solution again since it is needed for the execution loop */
515  bestsol = SCIPgetBestSol(scip);
516 
517  /* reset minnodes if new solution was found */
518  if( heurdata->lastsol != bestsol )
519  {
520  heurdata->curminnodes = heurdata->minnodes;
521  heurdata->callstatus = EXECUTE;
522  heurdata->lastsol = bestsol;
523  }
524 
525  /* if no new solution was found and trust region also seems to fail, just keep on waiting */
526  if( heurdata->callstatus == WAITFORNEWSOL )
527  return SCIP_OKAY;
528 
529  /* determine the node limit for the current process */
530  nsubnodes = maxnnodes - heurdata->usednodes;
531  nsubnodes = MIN(nsubnodes, heurdata->maxnodes);
532 
533  /* check whether we have enough nodes left to call sub problem solving */
534  if( nsubnodes < heurdata->curminnodes )
535  return SCIP_OKAY;
536 
537  if( SCIPisStopped(scip) )
538  return SCIP_OKAY;
539 
540  /* check whether there is enough time and memory left */
541  SCIP_CALL( SCIPcheckCopyLimits(scip, &success) );
542 
543  /* abort if no time is left or there is not enough memory to create a copy of SCIP */
544  if( !success )
545  return SCIP_OKAY;
546 
547  heurresult = SCIP_DIDNOTFIND;
548 
549  SCIPdebugMsg(scip, "running trust region heuristic ...\n");
550 
551  SCIP_CALL( SCIPcreate(&subscip) );
552 
553  retcode = setupAndSolveSubscipTrustregion(scip, subscip, heur, nsubnodes, &heurresult);
554 
555  SCIP_CALL( SCIPfree(&subscip) );
556 
557  /* if the result is FOUNDSOL, this means that a solution was found during a previous execution of the heuristic.
558  * So the heuristic result should only be updated if the result is not FOUNDSOL.
559  */
560  if( *result != SCIP_FOUNDSOL )
561  *result = heurresult;
562  }
563  while( bestsol != SCIPgetBestSol(scip) && retcode == SCIP_OKAY );
564 
565  return retcode;
566 }
567 
568 
569 /*
570  * primal heuristic specific interface methods
571  */
572 
573 /** creates the trustregion primal heuristic and includes it in SCIP */
575  SCIP* scip /**< SCIP data structure */
576  )
577 {
578  SCIP_HEURDATA* heurdata;
579  SCIP_HEUR* heur;
580 
581  /* create Trustregion primal heuristic data */
582  SCIP_CALL( SCIPallocBlockMemory(scip, &heurdata) );
583 
584  /* include primal heuristic */
585  SCIP_CALL( SCIPincludeHeurBasic(scip, &heur,
587  HEUR_MAXDEPTH, HEUR_TIMING, HEUR_USESSUBSCIP, heurExecTrustregion, heurdata) );
588 
589  assert(heur != NULL);
590 
591  /* set non-NULL pointers to callback methods */
592  SCIP_CALL( SCIPsetHeurCopy(scip, heur, heurCopyTrustregion) );
593  SCIP_CALL( SCIPsetHeurFree(scip, heur, heurFreeTrustregion) );
594  SCIP_CALL( SCIPsetHeurInit(scip, heur, heurInitTrustregion) );
595 
596  /* add trustregion primal heuristic parameters */
597  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/nodesofs",
598  "number of nodes added to the contingent of the total nodes",
599  &heurdata->nodesofs, FALSE, DEFAULT_NODESOFS, 0, INT_MAX, NULL, NULL) );
600 
601  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/minbinvars",
602  "the number of binary variables necessary to run the heuristic",
603  &heurdata->minbinvars, FALSE, DEFAULT_MINBINVARS, 1, INT_MAX, NULL, NULL) );
604 
605  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/nodesquot",
606  "contingent of sub problem nodes in relation to the number of nodes of the original problem",
607  &heurdata->nodesquot, FALSE, DEFAULT_NODESQUOT, 0.0, 1.0, NULL, NULL) );
608 
609  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/lplimfac",
610  "factor by which the limit on the number of LP depends on the node limit",
611  &heurdata->lplimfac, TRUE, DEFAULT_LPLIMFAC, 1.0, SCIP_REAL_MAX, NULL, NULL) );
612 
613  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/minnodes",
614  "minimum number of nodes required to start the subproblem",
615  &heurdata->minnodes, TRUE, DEFAULT_MINNODES, 0, INT_MAX, NULL, NULL) );
616 
617  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/maxnodes",
618  "maximum number of nodes to regard in the subproblem",
619  &heurdata->maxnodes, TRUE, DEFAULT_MAXNODES, 0, INT_MAX, NULL, NULL) );
620 
621  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/nwaitingnodes",
622  "number of nodes without incumbent change that heuristic should wait",
623  &heurdata->nwaitingnodes, TRUE, DEFAULT_NWAITINGNODES, 0, INT_MAX, NULL, NULL) );
624 
625  SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/" HEUR_NAME "/uselprows",
626  "should subproblem be created out of the rows in the LP rows?",
627  &heurdata->uselprows, TRUE, DEFAULT_USELPROWS, NULL, NULL) );
628 
629  SCIP_CALL( SCIPaddBoolParam(scip, "heuristics/" HEUR_NAME "/copycuts",
630  "if uselprows == FALSE, should all active cuts from cutpool be copied to constraints in subproblem?",
631  &heurdata->copycuts, TRUE, DEFAULT_COPYCUTS, NULL, NULL) );
632 
633  SCIP_CALL( SCIPaddIntParam(scip, "heuristics/" HEUR_NAME "/bestsollimit",
634  "limit on number of improving incumbent solutions in sub-CIP",
635  &heurdata->bestsollimit, FALSE, DEFAULT_BESTSOLLIMIT, -1, INT_MAX, NULL, NULL) );
636 
637  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/violpenalty",
638  "the penalty for each change in the binary variables from the candidate solution",
639  &heurdata->violpenalty, FALSE, DEFAULT_VIOLPENALTY, 0.0, SCIP_REAL_MAX, NULL, NULL) );
640 
641  SCIP_CALL( SCIPaddRealParam(scip, "heuristics/" HEUR_NAME "/objminimprove",
642  "the minimum absolute improvement in the objective function value",
643  &heurdata->objminimprove, FALSE, DEFAULT_OBJMINIMPROVE, 0.0, SCIP_REAL_MAX, NULL, NULL) );
644 
645  return SCIP_OKAY;
646 }
enum SCIP_Result SCIP_RESULT
Definition: type_result.h:61
SCIP_Bool SCIPsolIsOriginal(SCIP_SOL *sol)
Definition: sol.c:2721
static SCIP_DECL_HEURINIT(heurInitTrustregion)
#define SCIP_EVENTTYPE_LPSOLVED
Definition: type_event.h:101
#define NULL
Definition: def.h:267
#define DEFAULT_LPLIMFAC
Large neighborhood search heuristic for Benders&#39; decomposition based on trust region methods...
static SCIP_DECL_HEURCOPY(heurCopyTrustregion)
public methods for SCIP parameter handling
public methods for node selector plugins
public methods for memory management
static SCIP_DECL_EVENTEXEC(eventExecTrustregion)
#define SCIP_MAXSTRLEN
Definition: def.h:288
public solving methods
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_RETCODE SCIPgetVarsData(SCIP *scip, SCIP_VAR ***vars, int *nvars, int *nbinvars, int *nintvars, int *nimplvars, int *ncontvars)
Definition: scip_prob.c:1866
#define FALSE
Definition: def.h:94
SCIP_RETCODE SCIPhashmapCreate(SCIP_HASHMAP **hashmap, BMS_BLKMEM *blkmem, int mapsize)
Definition: misc.c:3074
#define DEFAULT_VIOLPENALTY
const char * SCIPeventhdlrGetName(SCIP_EVENTHDLR *eventhdlr)
Definition: event.c:324
SCIP_Real SCIPinfinity(SCIP *scip)
int SCIPsnprintf(char *t, int len, const char *s,...)
Definition: misc.c:10877
#define TRUE
Definition: def.h:93
#define SCIPdebug(x)
Definition: pub_message.h:93
enum SCIP_Retcode SCIP_RETCODE
Definition: type_retcode.h:63
methods commonly used by primal heuristics
struct SCIP_HeurData SCIP_HEURDATA
Definition: type_heur.h:77
public methods for problem variables
#define SCIPfreeBlockMemory(scip, ptr)
Definition: scip_mem.h:108
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_RETCODE SCIPtranslateSubSols(SCIP *scip, SCIP *subscip, SCIP_HEUR *heur, SCIP_VAR **subvars, SCIP_Bool *success, int *solindex)
Definition: scip_copy.c:1448
#define HEUR_NAME
#define DEFAULT_BESTSOLLIMIT
void * SCIPhashmapGetImage(SCIP_HASHMAP *hashmap, void *origin)
Definition: misc.c:3261
#define SCIPfreeBufferArray(scip, ptr)
Definition: scip_mem.h:136
SCIP_RETCODE SCIPcreate(SCIP **scip)
Definition: scip_general.c:307
void SCIPheurSetData(SCIP_HEUR *heur, SCIP_HEURDATA *heurdata)
Definition: heur.c:1374
#define SCIPallocBlockMemory(scip, ptr)
Definition: scip_mem.h:89
public methods for SCIP variables
#define SCIPdebugMsg
Definition: scip_message.h:78
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
SCIP_RETCODE SCIPprintStatistics(SCIP *scip, FILE *file)
#define HEUR_USESSUBSCIP
public methods for numerical tolerances
SCIP_Real SCIPfeasFloor(SCIP *scip, SCIP_Real val)
#define HEUR_DESC
public methods for querying solving statistics
#define DEFAULT_OBJMINIMPROVE
const char * SCIPgetProbName(SCIP *scip)
Definition: scip_prob.c:1067
SCIP_RETCODE SCIPsolve(SCIP *scip)
Definition: scip_solve.c:2486
const char * SCIPheurGetName(SCIP_HEUR *heur)
Definition: heur.c:1453
#define SCIPerrorMessage
Definition: pub_message.h:64
SCIP_RETCODE SCIPaddCons(SCIP *scip, SCIP_CONS *cons)
Definition: scip_prob.c:2770
SCIP_RETCODE SCIPaddTrustregionNeighborhoodConstraint(SCIP *sourcescip, SCIP *targetscip, SCIP_VAR **subvars, SCIP_Real violpenalty)
Definition: heuristics.c:1025
SCIP_RETCODE SCIPsetHeurFree(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURFREE((*heurfree)))
Definition: scip_heur.c:178
public methods for event handler plugins and event handlers
static SCIP_DECL_HEUREXEC(heurExecTrustregion)
SCIP_STATUS SCIPgetStatus(SCIP *scip)
Definition: scip_general.c:498
BMS_BLKMEM * SCIPblkmem(SCIP *scip)
Definition: scip_mem.c:57
#define DEFAULT_NODESOFS
#define DEFAULT_USELPROWS
struct SCIP_EventData SCIP_EVENTDATA
Definition: type_event.h:173
void SCIPhashmapFree(SCIP_HASHMAP **hashmap)
Definition: misc.c:3108
#define EXECUTE
SCIP_HEUR * SCIPsolGetHeur(SCIP_SOL *sol)
Definition: sol.c:2804
SCIP_Real SCIPgetSolTransObj(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1347
#define WAITFORNEWSOL
public methods for problem copies
public methods for primal CIP solutions
#define SCIP_CALL(x)
Definition: def.h:380
SCIP_RETCODE SCIPincludeHeurTrustregion(SCIP *scip)
#define HEUR_MAXDEPTH
SCIP_Longint SCIPheurGetNCalls(SCIP_HEUR *heur)
Definition: heur.c:1579
public methods for primal heuristic plugins and divesets
public methods for constraint handler plugins and constraints
SCIP_RETCODE SCIPsetCommonSubscipParams(SCIP *sourcescip, SCIP *subscip, SCIP_Longint nsubnodes, SCIP_Longint nstallnodes, int bestsollimit)
Definition: scip_copy.c:3337
#define SCIPallocBufferArray(scip, ptr, num)
Definition: scip_mem.h:124
public data structures and miscellaneous methods
#define SCIP_Bool
Definition: def.h:91
SCIP_RETCODE SCIPcatchEvent(SCIP *scip, SCIP_EVENTTYPE eventtype, SCIP_EVENTHDLR *eventhdlr, SCIP_EVENTDATA *eventdata, int *filterpos)
Definition: scip_event.c:286
SCIP_EVENTTYPE SCIPeventGetType(SCIP_EVENT *event)
Definition: event.c:1030
#define DEFAULT_MINNODES
#define HEUR_DISPCHAR
#define MIN(x, y)
Definition: def.h:243
SCIP_RETCODE SCIPsetIntParam(SCIP *scip, const char *name, int value)
Definition: scip_param.c:487
SCIP_RETCODE SCIPdropEvent(SCIP *scip, SCIP_EVENTTYPE eventtype, SCIP_EVENTHDLR *eventhdlr, SCIP_EVENTDATA *eventdata, int filterpos)
Definition: scip_event.c:320
SCIP_Real SCIPvarGetObj(SCIP_VAR *var)
Definition: var.c:17927
static SCIP_DECL_HEURFREE(heurFreeTrustregion)
int SCIPgetNSols(SCIP *scip)
Definition: scip_sol.c:2070
Constraint handler for linear constraints in their most general form, .
int SCIPgetNBinVars(SCIP *scip)
Definition: scip_prob.c:2037
#define SCIP_REAL_MAX
Definition: def.h:174
SCIP_RETCODE SCIPcreateConsLinear(SCIP *scip, SCIP_CONS **cons, const char *name, int nvars, SCIP_VAR **vars, SCIP_Real *vals, SCIP_Real lhs, SCIP_Real rhs, 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)
#define SCIP_LONGINT_FORMAT
Definition: def.h:165
public methods for branching rule plugins and branching
SCIP_Bool SCIPisObjIntegral(SCIP *scip)
Definition: scip_prob.c:1562
public methods for managing events
general public methods
#define MAX(x, y)
Definition: def.h:239
SCIP_SOL * SCIPgetBestSol(SCIP *scip)
Definition: scip_sol.c:2169
public methods for solutions
#define DEFAULT_COPYCUTS
#define EVENTHDLR_NAME
SCIP_RETCODE SCIPreleaseCons(SCIP *scip, SCIP_CONS **cons)
Definition: scip_cons.c:1174
#define HEUR_FREQ
public methods for message output
static SCIP_RETCODE setupAndSolveSubscipTrustregion(SCIP *scip, SCIP *subscip, SCIP_HEUR *heur, SCIP_Longint nsubnodes, SCIP_RESULT *result)
SCIP_RETCODE SCIPsetHeurInit(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURINIT((*heurinit)))
Definition: scip_heur.c:194
#define DEFAULT_NODESQUOT
SCIP_RETCODE SCIPcopyLargeNeighborhoodSearch(SCIP *sourcescip, SCIP *subscip, SCIP_HASHMAP *varmap, const char *suffix, SCIP_VAR **fixedvars, SCIP_Real *fixedvals, int nfixedvars, SCIP_Bool uselprows, SCIP_Bool copycuts, SCIP_Bool *success, SCIP_Bool *valid)
Definition: heuristics.c:951
static SCIP_RETCODE addTrustRegionConstraints(SCIP *scip, SCIP *subscip, SCIP_VAR **subvars, SCIP_HEURDATA *heurdata)
#define SCIP_Real
Definition: def.h:173
SCIP_Bool SCIPisStopped(SCIP *scip)
Definition: scip_general.c:718
#define DEFAULT_MINBINVARS
#define HEUR_FREQOFS
public methods for message handling
#define HEUR_PRIORITY
#define SCIP_Longint
Definition: def.h:158
SCIP_Longint SCIPheurGetNSolsFound(SCIP_HEUR *heur)
Definition: heur.c:1589
#define DEFAULT_MAXNODES
SCIP_RETCODE SCIPcheckCopyLimits(SCIP *sourcescip, SCIP_Bool *success)
Definition: scip_copy.c:3253
SCIP_RETCODE SCIPtransformProb(SCIP *scip)
Definition: scip_solve.c:222
SCIP_RETCODE SCIPsetHeurCopy(SCIP *scip, SCIP_HEUR *heur, SCIP_DECL_HEURCOPY((*heurcopy)))
Definition: scip_heur.c:162
SCIP_RETCODE SCIPinterruptSolve(SCIP *scip)
Definition: scip_solve.c:3417
public methods for primal heuristics
#define SCIP_CALL_ABORT(x)
Definition: def.h:359
SCIP_HEURDATA * SCIPheurGetData(SCIP_HEUR *heur)
Definition: heur.c:1364
SCIP_Longint SCIPgetNNodes(SCIP *scip)
SCIP_Longint SCIPgetNLPs(SCIP *scip)
public methods for global and local (sub)problems
#define EVENTHDLR_DESC
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_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
#define HEUR_TIMING
SCIP_RETCODE SCIPfree(SCIP **scip)
Definition: scip_general.c:339
SCIP_Longint SCIPgetSolNodenum(SCIP *scip, SCIP_SOL *sol)
Definition: scip_sol.c:1513
memory allocation routines
#define DEFAULT_NWAITINGNODES