-
Notifications
You must be signed in to change notification settings - Fork 2.1k
/
gscip_solver.cc
1363 lines (1271 loc) · 54.7 KB
/
gscip_solver.cc
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
// Copyright 2010-2024 Google LLC
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "ortools/math_opt/solvers/gscip_solver.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <limits>
#include <memory>
#include <optional>
#include <string>
#include <utility>
#include <vector>
#include "absl/cleanup/cleanup.h"
#include "absl/container/flat_hash_map.h"
#include "absl/container/flat_hash_set.h"
#include "absl/log/check.h"
#include "absl/log/die_if_null.h"
#include "absl/memory/memory.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/str_join.h"
#include "absl/strings/string_view.h"
#include "absl/time/clock.h"
#include "absl/time/time.h"
#include "absl/types/span.h"
#include "google/protobuf/repeated_ptr_field.h"
#include "ortools/base/linked_hash_map.h"
#include "ortools/base/logging.h"
#include "ortools/base/map_util.h"
#include "ortools/base/protoutil.h"
#include "ortools/base/status_macros.h"
#include "ortools/gscip/gscip.h"
#include "ortools/gscip/gscip.pb.h"
#include "ortools/gscip/gscip_event_handler.h"
#include "ortools/gscip/gscip_parameters.h"
#include "ortools/math_opt/callback.pb.h"
#include "ortools/math_opt/core/invalid_indicators.h"
#include "ortools/math_opt/core/inverted_bounds.h"
#include "ortools/math_opt/core/math_opt_proto_utils.h"
#include "ortools/math_opt/core/solver_interface.h"
#include "ortools/math_opt/core/sparse_submatrix.h"
#include "ortools/math_opt/core/sparse_vector_view.h"
#include "ortools/math_opt/infeasible_subsystem.pb.h"
#include "ortools/math_opt/model.pb.h"
#include "ortools/math_opt/model_parameters.pb.h"
#include "ortools/math_opt/model_update.pb.h"
#include "ortools/math_opt/parameters.pb.h"
#include "ortools/math_opt/result.pb.h"
#include "ortools/math_opt/solution.pb.h"
#include "ortools/math_opt/solvers/gscip/gscip_solver_constraint_handler.h"
#include "ortools/math_opt/solvers/message_callback_data.h"
#include "ortools/math_opt/sparse_containers.pb.h"
#include "ortools/math_opt/validators/callback_validator.h"
#include "ortools/port/proto_utils.h"
#include "ortools/util/solve_interrupter.h"
#include "scip/type_cons.h"
#include "scip/type_var.h"
namespace operations_research {
namespace math_opt {
namespace {
constexpr double kInf = std::numeric_limits<double>::infinity();
constexpr SupportedProblemStructures kGscipSupportedStructures = {
.integer_variables = SupportType::kSupported,
.quadratic_objectives = SupportType::kSupported,
.quadratic_constraints = SupportType::kSupported,
.sos1_constraints = SupportType::kSupported,
.sos2_constraints = SupportType::kSupported,
.indicator_constraints = SupportType::kSupported};
int64_t SafeId(const VariablesProto& variables, int index) {
if (variables.ids().empty()) {
return index;
}
return variables.ids(index);
}
const std::string& EmptyString() {
static const std::string* const empty_string = new std::string;
return *empty_string;
}
const std::string& SafeName(const VariablesProto& variables, int index) {
if (variables.names().empty()) {
return EmptyString();
}
return variables.names(index);
}
int64_t SafeId(const LinearConstraintsProto& linear_constraints, int index) {
if (linear_constraints.ids().empty()) {
return index;
}
return linear_constraints.ids(index);
}
absl::string_view SafeName(const LinearConstraintsProto& linear_constraints,
int index) {
if (linear_constraints.names().empty()) {
return EmptyString();
}
return linear_constraints.names(index);
}
absl::flat_hash_map<int64_t, double> SparseDoubleVectorAsMap(
const SparseDoubleVectorProto& vector) {
CHECK_EQ(vector.ids_size(), vector.values_size());
absl::flat_hash_map<int64_t, double> result;
result.reserve(vector.ids_size());
for (int i = 0; i < vector.ids_size(); ++i) {
result[vector.ids(i)] = vector.values(i);
}
return result;
}
// Viewing matrix as a list of (row, column, value) tuples stored in row major
// order, does a linear scan from index scan_start to find the index of the
// first entry with row >= row_id. Returns the size the tuple list if there is
// no such entry.
inline int FindRowStart(const SparseDoubleMatrixProto& matrix,
const int64_t row_id, const int scan_start) {
int result = scan_start;
while (result < matrix.row_ids_size() && matrix.row_ids(result) < row_id) {
++result;
}
return result;
}
struct LinearConstraintView {
int64_t linear_constraint_id;
double lower_bound;
double upper_bound;
absl::string_view name;
absl::Span<const int64_t> variable_ids;
absl::Span<const double> coefficients;
};
// Iterates over the constraints from a LinearConstraints, outputting a
// LinearConstraintView for each constraint. Requires a SparseDoubleMatrixProto
// which may have data from additional constraints not in LinearConstraints.
//
// The running time to iterate through and read each element once is
// O(Size(*linear_constraints) + Size(*linear_constraint_matrix)).
class LinearConstraintIterator {
public:
LinearConstraintIterator(
const LinearConstraintsProto* linear_constraints,
const SparseDoubleMatrixProto* linear_constraint_matrix)
: linear_constraints_(ABSL_DIE_IF_NULL(linear_constraints)),
linear_constraint_matrix_(ABSL_DIE_IF_NULL(linear_constraint_matrix)) {
if (NumConstraints(*linear_constraints_) > 0) {
const int64_t first_constraint = SafeId(*linear_constraints_, 0);
matrix_start_ =
FindRowStart(*linear_constraint_matrix_, first_constraint, 0);
matrix_end_ = FindRowStart(*linear_constraint_matrix_,
first_constraint + 1, matrix_start_);
} else {
matrix_start_ = NumMatrixNonzeros(*linear_constraint_matrix_);
matrix_end_ = matrix_start_;
}
}
bool IsDone() const {
return current_con_ >= NumConstraints(*linear_constraints_);
}
// Call only if !IsDone(). Runs in O(1).
LinearConstraintView Current() const {
CHECK(!IsDone());
LinearConstraintView result;
result.lower_bound = linear_constraints_->lower_bounds(current_con_);
result.upper_bound = linear_constraints_->upper_bounds(current_con_);
result.name = SafeName(*linear_constraints_, current_con_);
result.linear_constraint_id = SafeId(*linear_constraints_, current_con_);
const auto vars_begin = linear_constraint_matrix_->column_ids().data();
result.variable_ids = absl::MakeConstSpan(vars_begin + matrix_start_,
vars_begin + matrix_end_);
const auto coefficients_begins =
linear_constraint_matrix_->coefficients().data();
result.coefficients = absl::MakeConstSpan(
coefficients_begins + matrix_start_, coefficients_begins + matrix_end_);
return result;
}
// Call only if !IsDone().
void Next() {
CHECK(!IsDone());
++current_con_;
if (IsDone()) {
matrix_start_ = NumMatrixNonzeros(*linear_constraint_matrix_);
matrix_end_ = matrix_start_;
return;
}
const int64_t current_row_id = SafeId(*linear_constraints_, current_con_);
matrix_start_ =
FindRowStart(*linear_constraint_matrix_, current_row_id, matrix_end_);
matrix_end_ = FindRowStart(*linear_constraint_matrix_, current_row_id + 1,
matrix_start_);
}
private:
// NOT OWNED
const LinearConstraintsProto* const linear_constraints_;
// NOT OWNED
const SparseDoubleMatrixProto* const linear_constraint_matrix_;
// An index into linear_constraints_, the constraint currently being viewed,
// or Size(linear_constraints_) when IsDone().
int current_con_ = 0;
// Informal: the interval [matrix_start_, matrix_end_) gives the indices in
// linear_constraint_matrix_ for linear_constraints_[current_con_]
//
// Invariant: if !IsDone():
// * matrix_start_: the first index in linear_constraint_matrix_ with row id
// >= RowId(linear_constraints_[current_con_])
// * matrix_end_: the first index in linear_constraint_matrix_ with row id
// >= RowId(linear_constraints_[current_con_]) + 1
//
// Implementation note: matrix_start_ and matrix_end_ equal
// Size(linear_constraint_matrix_) when IsDone().
int matrix_start_ = 0;
int matrix_end_ = 0;
};
inline GScipVarType GScipVarTypeFromIsInteger(const bool is_integer) {
return is_integer ? GScipVarType::kInteger : GScipVarType::kContinuous;
}
template <typename T>
SparseDoubleVectorProto FillSparseDoubleVector(
const gtl::linked_hash_map<int64_t, T>& id_map,
const absl::flat_hash_map<T, double>& value_map,
const SparseVectorFilterProto& filter) {
SparseVectorFilterPredicate predicate(filter);
SparseDoubleVectorProto result;
for (const auto [mathopt_id, scip_id] : id_map) {
const double value = value_map.at(scip_id);
if (predicate.AcceptsAndUpdate(mathopt_id, value)) {
result.add_ids(mathopt_id);
result.add_values(value);
}
}
return result;
}
} // namespace
absl::Status GScipSolver::AddVariables(
const VariablesProto& variables,
const absl::flat_hash_map<int64_t, double>& linear_objective_coefficients) {
for (int i = 0; i < NumVariables(variables); ++i) {
const int64_t id = SafeId(variables, i);
// SCIP is failing with an assert in SCIPcreateVar() when input bounds are
// inverted. That said, it is not an issue if the bounds are created
// non-inverted and later changed. Thus here we use this hack to bypass the
// assertion in this corner case.
const bool inverted_bounds =
variables.lower_bounds(i) > variables.upper_bounds(i);
ASSIGN_OR_RETURN(
SCIP_VAR* const v,
gscip_->AddVariable(
variables.lower_bounds(i),
inverted_bounds ? variables.lower_bounds(i)
: variables.upper_bounds(i),
gtl::FindWithDefault(linear_objective_coefficients, id),
GScipVarTypeFromIsInteger(variables.integers(i)),
SafeName(variables, i)));
if (inverted_bounds) {
const double ub = variables.upper_bounds(i);
if (gscip_->VarType(v) == GScipVarType::kBinary && ub != 0.0 &&
ub != 1.0) {
// gSCIP (and SCIP actually) upgrades the variable type to kBinary if
// the bounds passed to AddVariable() are both in {0.0, 1.0}. Changing
// the bounds then raises an assertion in SCIP if the bounds is not in
// {0.0, 1.0}.
RETURN_IF_ERROR(gscip_->SetVarType(v, GScipVarType::kInteger));
}
RETURN_IF_ERROR(gscip_->SetUb(v, variables.upper_bounds(i)));
}
gtl::InsertOrDie(&variables_, id, v);
}
return absl::OkStatus();
}
absl::StatusOr<bool> GScipSolver::UpdateVariables(
const VariableUpdatesProto& variable_updates) {
for (const auto [id, is_integer] : MakeView(variable_updates.integers())) {
// We intentionally update vartype first to ensure the checks below against
// binary variables are against the up-to-date model.
SCIP_VAR* const var = variables_.at(id);
if (gscip_->VarType(var) == GScipVarType::kBinary) {
// We reject bound updates on binary variables as they can lead to
// crashes, or unexpected round-trip values if the vartype changes.
return false;
}
RETURN_IF_ERROR(
gscip_->SetVarType(var, GScipVarTypeFromIsInteger(is_integer)));
}
for (const auto [id, lb] : MakeView(variable_updates.lower_bounds())) {
SCIP_VAR* const var = variables_.at(id);
if (gscip_->VarType(var) == GScipVarType::kBinary) {
// We reject bound updates on binary variables as they can lead to
// crashes, or unexpected round-trip values if the vartype changes.
return false;
}
RETURN_IF_ERROR(gscip_->SetLb(var, lb));
}
for (const auto [id, ub] : MakeView(variable_updates.upper_bounds())) {
SCIP_VAR* const var = variables_.at(id);
if (gscip_->VarType(var) == GScipVarType::kBinary) {
// We reject bound updates on binary variables as they can lead to
// crashes, or unexpected round-trip values if the vartype changes.
return false;
}
RETURN_IF_ERROR(gscip_->SetUb(var, ub));
}
return true;
}
// SCIP does not natively support quadratic objectives, so we formulate them
// using quadratic constraints. We use a epi-/hypo-graph formulation depending
// on the objective sense:
// min x'Qx <--> min y s.t. y >= x'Qx
// max x'Qx <--> max y s.t. y <= x'Qx
absl::Status GScipSolver::AddQuadraticObjectiveTerms(
const SparseDoubleMatrixProto& new_qp_terms, const bool maximize) {
const int num_qp_terms = new_qp_terms.row_ids_size();
if (num_qp_terms == 0) {
return absl::OkStatus();
}
ASSIGN_OR_RETURN(
SCIP_VAR* const qp_auxiliary_variable,
gscip_->AddVariable(-kInf, kInf, 1.0, GScipVarType::kContinuous));
GScipQuadraticRange range{
.lower_bound = maximize ? 0.0 : -kInf,
.linear_variables = {qp_auxiliary_variable},
.linear_coefficients = {-1.0},
.upper_bound = maximize ? kInf : 0.0,
};
range.quadratic_variables1.reserve(num_qp_terms);
range.quadratic_variables2.reserve(num_qp_terms);
range.quadratic_coefficients.reserve(num_qp_terms);
for (int i = 0; i < num_qp_terms; ++i) {
range.quadratic_variables1.push_back(
variables_.at(new_qp_terms.row_ids(i)));
range.quadratic_variables2.push_back(
variables_.at(new_qp_terms.column_ids(i)));
range.quadratic_coefficients.push_back(new_qp_terms.coefficients(i));
}
RETURN_IF_ERROR(gscip_->AddQuadraticConstraint(range).status());
has_quadratic_objective_ = true;
return absl::OkStatus();
}
absl::Status GScipSolver::AddLinearConstraints(
const LinearConstraintsProto& linear_constraints,
const SparseDoubleMatrixProto& linear_constraint_matrix) {
for (LinearConstraintIterator lin_con_it(&linear_constraints,
&linear_constraint_matrix);
!lin_con_it.IsDone(); lin_con_it.Next()) {
const LinearConstraintView current = lin_con_it.Current();
GScipLinearRange range;
range.lower_bound = current.lower_bound;
range.upper_bound = current.upper_bound;
range.coefficients = std::vector<double>(current.coefficients.begin(),
current.coefficients.end());
range.variables.reserve(current.variable_ids.size());
for (const int64_t var_id : current.variable_ids) {
range.variables.push_back(variables_.at(var_id));
}
ASSIGN_OR_RETURN(
SCIP_CONS* const scip_con,
gscip_->AddLinearConstraint(range, std::string(current.name)));
gtl::InsertOrDie(&linear_constraints_, current.linear_constraint_id,
scip_con);
}
return absl::OkStatus();
}
absl::Status GScipSolver::UpdateLinearConstraints(
const LinearConstraintUpdatesProto linear_constraint_updates,
const SparseDoubleMatrixProto& linear_constraint_matrix,
const std::optional<int64_t> first_new_var_id,
const std::optional<int64_t> first_new_cstr_id) {
for (const auto [id, lb] :
MakeView(linear_constraint_updates.lower_bounds())) {
RETURN_IF_ERROR(
gscip_->SetLinearConstraintLb(linear_constraints_.at(id), lb));
}
for (const auto [id, ub] :
MakeView(linear_constraint_updates.upper_bounds())) {
RETURN_IF_ERROR(
gscip_->SetLinearConstraintUb(linear_constraints_.at(id), ub));
}
for (const auto& [lin_con_id, var_coeffs] : SparseSubmatrixByRows(
linear_constraint_matrix, /*start_row_id=*/0,
/*end_row_id=*/first_new_cstr_id, /*start_col_id=*/0,
/*end_col_id=*/first_new_var_id)) {
for (const auto& [var_id, value] : var_coeffs) {
RETURN_IF_ERROR(gscip_->SetLinearConstraintCoef(
linear_constraints_.at(lin_con_id), variables_.at(var_id), value));
}
}
return absl::OkStatus();
}
absl::Status GScipSolver::AddQuadraticConstraints(
const google::protobuf::Map<int64_t, QuadraticConstraintProto>&
quadratic_constraints) {
for (const auto& [id, constraint] : quadratic_constraints) {
GScipQuadraticRange range{
.lower_bound = constraint.lower_bound(),
.upper_bound = constraint.upper_bound(),
};
{
const int num_linear_terms = constraint.linear_terms().ids_size();
range.linear_variables.reserve(num_linear_terms);
range.linear_coefficients.reserve(num_linear_terms);
for (const auto [var_id, coeff] : MakeView(constraint.linear_terms())) {
range.linear_variables.push_back(variables_.at(var_id));
range.linear_coefficients.push_back(coeff);
}
}
{
const SparseDoubleMatrixProto& quad_terms = constraint.quadratic_terms();
const int num_quad_terms = constraint.quadratic_terms().row_ids_size();
range.quadratic_variables1.reserve(num_quad_terms);
range.quadratic_variables2.reserve(num_quad_terms);
range.quadratic_coefficients.reserve(num_quad_terms);
for (int i = 0; i < num_quad_terms; ++i) {
range.quadratic_variables1.push_back(
variables_.at(quad_terms.row_ids(i)));
range.quadratic_variables2.push_back(
variables_.at(quad_terms.column_ids(i)));
range.quadratic_coefficients.push_back(quad_terms.coefficients(i));
}
}
ASSIGN_OR_RETURN(SCIP_CONS* const scip_con,
gscip_->AddQuadraticConstraint(range, constraint.name()));
gtl::InsertOrDie(&quadratic_constraints_, id, scip_con);
}
return absl::OkStatus();
}
absl::Status GScipSolver::AddIndicatorConstraints(
const google::protobuf::Map<int64_t, IndicatorConstraintProto>&
indicator_constraints) {
for (const auto& [id, constraint] : indicator_constraints) {
if (!constraint.has_indicator_id()) {
gtl::InsertOrDie(&indicator_constraints_, id, std::nullopt);
continue;
}
SCIP_VAR* const indicator_var = variables_.at(constraint.indicator_id());
// TODO(b/254860940): Properly handle the auxiliary variable that gSCIP may
// add if `activate_on_zero()` is true and the indicator constraint is
// deleted.
GScipIndicatorConstraint data{
.indicator_variable = indicator_var,
.negate_indicator = constraint.activate_on_zero(),
};
const double lb = constraint.lower_bound();
const double ub = constraint.upper_bound();
if (lb > -kInf && ub < kInf) {
return util::InvalidArgumentErrorBuilder()
<< "gSCIP does not support indicator constraints with ranged "
"implied constraints; bounds are: "
<< lb << " <= ... <= " << ub;
}
// SCIP only supports implied constraints of the form ax <= b, so we must
// formulate any constraints of the form cx >= d as -cx <= -d.
double scaling;
if (ub < kInf) {
data.upper_bound = ub;
scaling = 1.0;
} else {
data.upper_bound = -lb;
scaling = -1.0;
}
{
const int num_terms = constraint.expression().ids_size();
data.variables.reserve(num_terms);
data.coefficients.reserve(num_terms);
for (const auto [var_id, coeff] : MakeView(constraint.expression())) {
data.variables.push_back(variables_.at(var_id));
data.coefficients.push_back(scaling * coeff);
}
}
ASSIGN_OR_RETURN(SCIP_CONS* const scip_con,
gscip_->AddIndicatorConstraint(data, constraint.name()));
gtl::InsertOrDie(&indicator_constraints_, id,
std::make_pair(scip_con, constraint.indicator_id()));
}
return absl::OkStatus();
}
absl::StatusOr<std::pair<SCIP_VAR*, SCIP_CONS*>>
GScipSolver::AddSlackVariableEqualToExpression(
const LinearExpressionProto& expression) {
ASSIGN_OR_RETURN(
SCIP_VAR * aux_var,
gscip_->AddVariable(-kInf, kInf, 0.0, GScipVarType::kContinuous));
GScipLinearRange range{
.lower_bound = -expression.offset(),
.upper_bound = -expression.offset(),
};
range.variables.push_back(aux_var);
range.coefficients.push_back(-1.0);
for (int i = 0; i < expression.ids_size(); ++i) {
range.variables.push_back(variables_.at(expression.ids(i)));
range.coefficients.push_back(expression.coefficients(i));
}
ASSIGN_OR_RETURN(SCIP_CONS * aux_constr, gscip_->AddLinearConstraint(range));
return std::make_pair(aux_var, aux_constr);
}
absl::Status GScipSolver::AuxiliaryStructureHandler::DeleteStructure(
GScip& gscip) {
for (SCIP_CONS* const constraint : constraints) {
RETURN_IF_ERROR(gscip.DeleteConstraint(constraint));
}
for (SCIP_VAR* const variable : variables) {
RETURN_IF_ERROR(gscip.DeleteVariable(variable));
}
variables.clear();
constraints.clear();
return absl::OkStatus();
}
absl::StatusOr<std::pair<GScipSOSData, GScipSolver::AuxiliaryStructureHandler>>
GScipSolver::ProcessSosProto(const SosConstraintProto& sos_constraint) {
GScipSOSData data;
AuxiliaryStructureHandler handler;
absl::flat_hash_set<int64_t> reused_variables;
for (const LinearExpressionProto& expr : sos_constraint.expressions()) {
// If the expression is equivalent to 1 * some_variable, there is no need to
// add a slack variable. But, SCIP crashes in debug mode if you repeat a
// variable twice within an SOS constraint, so we track the ones we reuse.
if (expr.ids_size() == 1 && expr.coefficients(0) == 1.0 &&
expr.offset() == 0.0 && !reused_variables.contains(expr.ids(0))) {
data.variables.push_back(variables_.at(expr.ids(0)));
reused_variables.insert(expr.ids(0));
} else {
ASSIGN_OR_RETURN((const auto [slack_var, slack_constr]),
AddSlackVariableEqualToExpression(expr));
handler.variables.push_back(slack_var);
handler.constraints.push_back(slack_constr);
data.variables.push_back(slack_var);
}
}
for (const double weight : sos_constraint.weights()) {
data.weights.push_back(weight);
}
return std::make_pair(data, handler);
}
absl::Status GScipSolver::AddSos1Constraints(
const google::protobuf::Map<int64_t, SosConstraintProto>&
sos1_constraints) {
for (const auto& [id, constraint] : sos1_constraints) {
ASSIGN_OR_RETURN((auto [sos_data, slack_handler]),
ProcessSosProto(constraint));
ASSIGN_OR_RETURN(
SCIP_CONS* const scip_con,
gscip_->AddSOS1Constraint(std::move(sos_data), constraint.name()));
gtl::InsertOrDie(&sos1_constraints_, id,
std::make_pair(scip_con, std::move(slack_handler)));
}
return absl::OkStatus();
}
absl::Status GScipSolver::AddSos2Constraints(
const google::protobuf::Map<int64_t, SosConstraintProto>&
sos2_constraints) {
for (const auto& [id, constraint] : sos2_constraints) {
ASSIGN_OR_RETURN((auto [sos_data, slack_handler]),
ProcessSosProto(constraint));
ASSIGN_OR_RETURN(SCIP_CONS* const scip_con,
gscip_->AddSOS2Constraint(sos_data, constraint.name()));
gtl::InsertOrDie(&sos2_constraints_, id,
std::make_pair(scip_con, std::move(slack_handler)));
}
return absl::OkStatus();
}
GScipParameters::MetaParamValue ConvertMathOptEmphasis(EmphasisProto emphasis) {
switch (emphasis) {
case EMPHASIS_OFF:
return GScipParameters::OFF;
case EMPHASIS_LOW:
return GScipParameters::FAST;
case EMPHASIS_MEDIUM:
case EMPHASIS_UNSPECIFIED:
return GScipParameters::DEFAULT_META_PARAM_VALUE;
case EMPHASIS_HIGH:
case EMPHASIS_VERY_HIGH:
return GScipParameters::AGGRESSIVE;
default:
LOG(FATAL) << "Unsupported MathOpt Emphasis value: "
<< ProtoEnumToString(emphasis)
<< " unknown, error setting gSCIP parameters";
}
}
absl::StatusOr<GScipParameters> GScipSolver::MergeParameters(
const SolveParametersProto& solve_parameters) {
// First build the result by translating common parameters to a
// GScipParameters, and then merging with user provided gscip_parameters.
// This results in user provided solver specific parameters overwriting
// common parameters should there be any conflict.
GScipParameters result;
std::vector<std::string> warnings;
// By default SCIP catches Ctrl-C but we don't want this behavior when the
// users uses SCIP through MathOpt.
GScipSetCatchCtrlC(false, &result);
if (solve_parameters.has_time_limit()) {
GScipSetTimeLimit(
util_time::DecodeGoogleApiProto(solve_parameters.time_limit()).value(),
&result);
}
if (solve_parameters.has_iteration_limit()) {
warnings.push_back("parameter iteration_limit not supported for gSCIP.");
}
if (solve_parameters.has_node_limit()) {
(*result.mutable_long_params())["limits/totalnodes"] =
solve_parameters.node_limit();
}
if (solve_parameters.has_cutoff_limit()) {
result.set_objective_limit(solve_parameters.cutoff_limit());
}
if (solve_parameters.has_objective_limit()) {
warnings.push_back("parameter objective_limit not supported for gSCIP.");
}
if (solve_parameters.has_best_bound_limit()) {
warnings.push_back("parameter best_bound_limit not supported for gSCIP.");
}
if (solve_parameters.has_solution_limit()) {
(*result.mutable_int_params())["limits/solutions"] =
solve_parameters.solution_limit();
}
// GScip has also GScipSetOutputEnabled() but this changes the log
// level. Setting `silence_output` sets the `quiet` field on the default
// message handler of SCIP which removes the output. Here it is important to
// use this rather than changing the log level so that if the user provides
// a MessageCallback function they do get some messages even when
// `enable_output` is false.
result.set_silence_output(!solve_parameters.enable_output());
if (solve_parameters.has_threads()) {
GScipSetMaxNumThreads(solve_parameters.threads(), &result);
}
if (solve_parameters.has_random_seed()) {
GScipSetRandomSeed(&result, solve_parameters.random_seed());
}
if (solve_parameters.has_absolute_gap_tolerance()) {
(*result.mutable_real_params())["limits/absgap"] =
solve_parameters.absolute_gap_tolerance();
}
if (solve_parameters.has_relative_gap_tolerance()) {
(*result.mutable_real_params())["limits/gap"] =
solve_parameters.relative_gap_tolerance();
}
if (solve_parameters.has_solution_pool_size()) {
result.set_num_solutions(solve_parameters.solution_pool_size());
// We must set limits/maxsol (the internal solution pool) and
// limits/maxorigsol (the number solutions to attempt to transform back to
// the user.
//
// NOTE: As of SCIP 8, limits/maxsol defaults to 100.
(*result.mutable_int_params())["limits/maxsol"] =
std::max(100, solve_parameters.solution_pool_size());
(*result.mutable_int_params())["limits/maxorigsol"] =
solve_parameters.solution_pool_size();
}
if (solve_parameters.lp_algorithm() != LP_ALGORITHM_UNSPECIFIED) {
char alg = 's';
switch (solve_parameters.lp_algorithm()) {
case LP_ALGORITHM_PRIMAL_SIMPLEX:
alg = 'p';
break;
case LP_ALGORITHM_DUAL_SIMPLEX:
alg = 'd';
break;
case LP_ALGORITHM_BARRIER:
// As SCIP is configured in ortools, this is an error, since we are not
// connected to any LP solver that runs barrier.
warnings.push_back(
"parameter lp_algorithm with value BARRIER is not supported for "
"gSCIP in ortools.");
alg = 'c';
break;
case LP_ALGORITHM_FIRST_ORDER:
warnings.push_back(
"parameter lp_algorithm with value FIRST_ORDER is not supported.");
break;
default:
LOG(FATAL) << "LPAlgorithm: "
<< ProtoEnumToString(solve_parameters.lp_algorithm())
<< " unknown, error setting gSCIP parameters";
}
(*result.mutable_char_params())["lp/initalgorithm"] = alg;
}
if (solve_parameters.presolve() != EMPHASIS_UNSPECIFIED) {
result.set_presolve(ConvertMathOptEmphasis(solve_parameters.presolve()));
}
if (solve_parameters.cuts() != EMPHASIS_UNSPECIFIED) {
result.set_separating(ConvertMathOptEmphasis(solve_parameters.cuts()));
}
if (solve_parameters.heuristics() != EMPHASIS_UNSPECIFIED) {
result.set_heuristics(
ConvertMathOptEmphasis(solve_parameters.heuristics()));
}
if (solve_parameters.scaling() != EMPHASIS_UNSPECIFIED) {
int scaling_value;
switch (solve_parameters.scaling()) {
case EMPHASIS_OFF:
scaling_value = 0;
break;
case EMPHASIS_LOW:
case EMPHASIS_MEDIUM:
scaling_value = 1;
break;
case EMPHASIS_HIGH:
case EMPHASIS_VERY_HIGH:
scaling_value = 2;
break;
default:
LOG(FATAL) << "Scaling emphasis: "
<< ProtoEnumToString(solve_parameters.scaling())
<< " unknown, error setting gSCIP parameters";
}
(*result.mutable_int_params())["lp/scaling"] = scaling_value;
}
result.MergeFrom(solve_parameters.gscip());
if (!warnings.empty()) {
return absl::InvalidArgumentError(absl::StrJoin(warnings, "; "));
}
return result;
}
namespace {
std::string JoinDetails(absl::string_view gscip_detail,
absl::string_view math_opt_detail) {
if (gscip_detail.empty()) {
return std::string(math_opt_detail);
}
if (math_opt_detail.empty()) {
return std::string(gscip_detail);
}
return absl::StrCat(gscip_detail, "; ", math_opt_detail);
}
double GetBestPrimalObjective(
const double gscip_best_objective,
const google::protobuf::RepeatedPtrField<SolutionProto>& solutions,
const bool is_maximize) {
double result = gscip_best_objective;
for (const auto& solution : solutions) {
if (solution.has_primal_solution() &&
solution.primal_solution().feasibility_status() ==
SOLUTION_STATUS_FEASIBLE) {
result =
is_maximize
? std::max(result, solution.primal_solution().objective_value())
: std::min(result, solution.primal_solution().objective_value());
}
}
return result;
}
absl::StatusOr<TerminationProto> ConvertTerminationReason(
const GScipResult& gscip_result, const bool is_maximize,
const google::protobuf::RepeatedPtrField<SolutionProto>& solutions,
const bool had_cutoff) {
const bool has_feasible_solution = !solutions.empty();
const GScipOutput::Status gscip_status = gscip_result.gscip_output.status();
absl::string_view gscip_status_detail =
gscip_result.gscip_output.status_detail();
const GScipSolvingStats& gscip_stats = gscip_result.gscip_output.stats();
// SCIP may return primal solutions that are slightly better than
// gscip_stats.best_objective().
const double best_primal_objective = GetBestPrimalObjective(
gscip_stats.best_objective(), solutions, is_maximize);
const std::optional<double> optional_finite_primal_objective =
has_feasible_solution ? std::make_optional(best_primal_objective)
: std::nullopt;
// For SCIP, the only indicator for the existence of a dual feasible solution
// is a finite dual bound.
const std::optional<double> optional_dual_objective =
std::isfinite(gscip_stats.best_bound())
? std::make_optional(gscip_stats.best_bound())
: std::nullopt;
switch (gscip_status) {
case GScipOutput::USER_INTERRUPT:
return LimitTerminationProto(
is_maximize, LIMIT_INTERRUPTED, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: USER_INTERRUPT"));
case GScipOutput::NODE_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_NODE, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: NODE_LIMIT"));
case GScipOutput::TOTAL_NODE_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_NODE, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: TOTAL_NODE_LIMIT"));
case GScipOutput::STALL_NODE_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_SLOW_PROGRESS, optional_finite_primal_objective,
optional_dual_objective, gscip_status_detail);
case GScipOutput::TIME_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_TIME, optional_finite_primal_objective,
optional_dual_objective, gscip_status_detail);
case GScipOutput::MEM_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_MEMORY, optional_finite_primal_objective,
optional_dual_objective, gscip_status_detail);
case GScipOutput::SOL_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_SOLUTION, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: SOL_LIMIT"));
case GScipOutput::BEST_SOL_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_SOLUTION, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: BEST_SOL_LIMIT"));
case GScipOutput::RESTART_LIMIT:
return LimitTerminationProto(
is_maximize, LIMIT_OTHER, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: RESTART_LIMIT"));
case GScipOutput::OPTIMAL:
return OptimalTerminationProto(
/*finite_primal_objective=*/best_primal_objective,
/*dual_objective=*/gscip_stats.best_bound(),
JoinDetails(gscip_status_detail, "underlying gSCIP status: OPTIMAL"));
case GScipOutput::GAP_LIMIT:
return OptimalTerminationProto(
/*finite_primal_objective=*/best_primal_objective,
/*dual_objective=*/gscip_stats.best_bound(),
JoinDetails(gscip_status_detail,
"underlying gSCIP status: GAP_LIMIT"));
case GScipOutput::INFEASIBLE:
if (had_cutoff) {
return CutoffTerminationProto(is_maximize, gscip_status_detail);
} else {
// By convention infeasible MIPs are always dual feasible.
const FeasibilityStatusProto dual_feasibility_status =
FEASIBILITY_STATUS_FEASIBLE;
return InfeasibleTerminationProto(is_maximize, dual_feasibility_status,
gscip_status_detail);
}
case GScipOutput::UNBOUNDED: {
if (has_feasible_solution) {
return UnboundedTerminationProto(
is_maximize,
JoinDetails(gscip_status_detail,
"underlying gSCIP status was UNBOUNDED, both primal "
"ray and feasible solution are present"));
} else {
return InfeasibleOrUnboundedTerminationProto(
is_maximize,
/*dual_feasibility_status=*/FEASIBILITY_STATUS_INFEASIBLE,
JoinDetails(
gscip_status_detail,
"underlying gSCIP status was UNBOUNDED, but only primal ray "
"was given, no feasible solution was found"));
}
}
case GScipOutput::INF_OR_UNBD:
return InfeasibleOrUnboundedTerminationProto(
is_maximize,
/*dual_feasibility_status=*/FEASIBILITY_STATUS_UNDETERMINED,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: INF_OR_UNBD"));
case GScipOutput::TERMINATE:
return LimitTerminationProto(
is_maximize, LIMIT_INTERRUPTED, optional_finite_primal_objective,
optional_dual_objective,
JoinDetails(gscip_status_detail,
"underlying gSCIP status: TERMINATE"));
case GScipOutput::INVALID_SOLVER_PARAMETERS:
return absl::InvalidArgumentError(gscip_status_detail);
case GScipOutput::UNKNOWN:
return absl::InternalError(JoinDetails(
gscip_status_detail, "Unexpected GScipOutput.status: UNKNOWN"));
default:
return absl::InternalError(JoinDetails(
gscip_status_detail, absl::StrCat("Missing GScipOutput.status case: ",
ProtoEnumToString(gscip_status))));
}
}
} // namespace
absl::StatusOr<SolveResultProto> GScipSolver::CreateSolveResultProto(
GScipResult gscip_result, const ModelSolveParametersProto& model_parameters,
const std::optional<double> cutoff) {
SolveResultProto solve_result;
const bool is_maximize = gscip_->ObjectiveIsMaximize();
// When an objective limit is set, SCIP returns the solutions worse than the
// limit, we need to filter these out manually.
const auto meets_cutoff = [cutoff, is_maximize](const double obj_value) {
if (!cutoff.has_value()) {
return true;
}
if (is_maximize) {
return obj_value >= *cutoff;
} else {
return obj_value <= *cutoff;
}
};
CHECK_EQ(gscip_result.solutions.size(), gscip_result.objective_values.size());
for (int i = 0; i < gscip_result.solutions.size(); ++i) {
// GScip ensures the solutions are returned best objective first.
if (!meets_cutoff(gscip_result.objective_values[i])) {
break;
}
SolutionProto* const solution = solve_result.add_solutions();
PrimalSolutionProto* const primal_solution =
solution->mutable_primal_solution();
primal_solution->set_objective_value(gscip_result.objective_values[i]);
primal_solution->set_feasibility_status(SOLUTION_STATUS_FEASIBLE);
*primal_solution->mutable_variable_values() =
FillSparseDoubleVector(variables_, gscip_result.solutions[i],
model_parameters.variable_values_filter());
}
if (!gscip_result.primal_ray.empty()) {
*solve_result.add_primal_rays()->mutable_variable_values() =
FillSparseDoubleVector(variables_, gscip_result.primal_ray,
model_parameters.variable_values_filter());
}
ASSIGN_OR_RETURN(*solve_result.mutable_termination(),
ConvertTerminationReason(gscip_result, is_maximize,
solve_result.solutions(),
/*had_cutoff=*/cutoff.has_value()));
SolveStatsProto* const common_stats = solve_result.mutable_solve_stats();
const GScipSolvingStats& gscip_stats = gscip_result.gscip_output.stats();
common_stats->set_node_count(gscip_stats.node_count());
common_stats->set_simplex_iterations(gscip_stats.primal_simplex_iterations() +
gscip_stats.dual_simplex_iterations());
common_stats->set_barrier_iterations(gscip_stats.barrier_iterations());
*solve_result.mutable_gscip_output() = std::move(gscip_result.gscip_output);
return solve_result;
}
GScipSolver::GScipSolver(std::unique_ptr<GScip> gscip)
: gscip_(std::move(ABSL_DIE_IF_NULL(gscip))) {}
absl::StatusOr<std::unique_ptr<SolverInterface>> GScipSolver::New(
const ModelProto& model, const InitArgs&) {
RETURN_IF_ERROR(ModelIsSupported(model, kGscipSupportedStructures, "SCIP"));
ASSIGN_OR_RETURN(std::unique_ptr<GScip> gscip, GScip::Create(model.name()));
RETURN_IF_ERROR(gscip->SetMaximize(model.objective().maximize()));
RETURN_IF_ERROR(gscip->SetObjectiveOffset(model.objective().offset()));
auto solver = absl::WrapUnique(new GScipSolver(std::move(gscip)));
RETURN_IF_ERROR(solver->constraint_handler_.Register(solver->gscip_.get()));
RETURN_IF_ERROR(solver->AddVariables(
model.variables(),
SparseDoubleVectorAsMap(model.objective().linear_coefficients())));
RETURN_IF_ERROR(solver->AddQuadraticObjectiveTerms(
model.objective().quadratic_coefficients(),
model.objective().maximize()));
RETURN_IF_ERROR(solver->AddLinearConstraints(