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set_cover.proto
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set_cover.proto
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// 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.
syntax = "proto3";
package operations_research;
import "ortools/util/int128.proto";
option java_package = "com.google.ortools.algorithms";
option java_multiple_files = true;
message SetCoverProto {
message Subset {
// The cost for using the given subset.
optional double cost = 1;
// The list of elements in the subset.
repeated int32 element = 2 [packed = true];
}
// The list of subsets in the model.
repeated Subset subset = 1;
// A user-defined name for the model.
optional string name = 2;
// An automatically fingerprint for the model. TODO(user): Implement.
optional Int128 fingerprint = 3;
}
message SetCoverSolutionResponse {
// Result of the optimization.
enum Status {
// Undefined.
UNDEFINED = 0;
// The solver found the proven optimal solution.
OPTIMAL = 1;
// The solver had enough time to find some solution that satisfied all
// constraints, but it did not reach the optimal.
FEASIBLE = 2;
// The model does not have any solution.
INFEASIBLE = 3;
// The model is invalid.
INVALID = 4;
}
// For future use. TODO(user): Implement.
optional Status status = 1;
// The number of subsets that are selected in the solution. This is used
// to decompress their indices below.
optional int32 num_subsets = 2;
// The list of the subsets selected in the solution.
repeated int32 subset = 3 [packed = true];
// The cost of the solution, as computed by the algorithm.
optional double cost = 4;
// A lower bound of the solution, as computed by the algorithm.
optional double cost_lower_bound = 5;
// An automatically fingerprint for the solution. TODO(user): Implement.
optional Int128 fingerprint = 6;
// A reference to the model the solution applies to. TODO(user): Implement.
optional Int128 model_fingerprint = 7;
}