math_opt: Add docs snippets

Author: Mizux

ortools/math_opt/docs/snippets/getting_started.cc

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+// Copyright 2010-2022 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.
+
+// Minimal example for Getting Started.
+// [START imports]
+#include <iostream>
+#include <ostream>
+
+#include "absl/log/check.h"
+#include "absl/status/statusor.h"
+#include "ortools/base/init_google.h"
+#include "ortools/math_opt/cpp/math_opt.h"
+// [END imports]
+
+int main(int argc, char** argv) {
+  InitGoogle(argv[0], &argc, &argv, true);
+
+  // [START build_model]
+  // Build the model.
+  namespace math_opt = ::operations_research::math_opt;
+  math_opt::Model lp_model("getting_started_lp");
+  const math_opt::Variable x = lp_model.AddContinuousVariable(-1.0, 1.5, "x");
+  const math_opt::Variable y = lp_model.AddContinuousVariable(0.0, 1.0, "y");
+  lp_model.AddLinearConstraint(x + y <= 1.5, "c");
+  lp_model.Maximize(x + 2 * y);
+  // [END build_model]
+
+  // [START solve_args]
+  // Set parameters, e.g. turn on logging.
+  math_opt::SolveArguments args;
+  args.parameters.enable_output = true;
+  // [END solve_args]
+
+  // [START solve]
+  // Solve and ensure an optimal solution was found with no errors.
+  const absl::StatusOr<math_opt::SolveResult> result =
+      math_opt::Solve(lp_model, math_opt::SolverType::kGlop, args);
+  CHECK_OK(result.status());
+  CHECK_OK(result->termination.EnsureIsOptimal());
+  // [END solve]
+
+  // [START print_result]
+  // Print some information from the result.
+  std::cout << "MathOpt solve succeeded" << std::endl;
+  std::cout << "Objective value: " << result->objective_value() << std::endl;
+  std::cout << "x: " << result->variable_values().at(x) << std::endl;
+  std::cout << "y: " << result->variable_values().at(y) << std::endl;
+  // [END print_result]
+
+  return 0;
+}

ortools/math_opt/docs/snippets/getting_started_py.py

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+# Copyright 2010-2022 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.
+
+"""Minimal example for Getting Started page."""
+
+from typing import Sequence
+
+from absl import app
+
+# [START imports]
+from ortools.math_opt.python import mathopt
+# [END imports]
+
+
+def main(argv: Sequence[str]) -> None:
+    del argv  # Unused.
+
+    # [START build_model]
+    # Build the model.
+    model = mathopt.Model(name="getting_started_lp")
+    x = model.add_variable(lb=-1.0, ub=1.5, name="x")
+    y = model.add_variable(lb=0.0, ub=1.0, name="y")
+    model.add_linear_constraint(x + y <= 1.5)
+    model.maximize(x + 2 * y)
+    # [END build_model]
+
+    # [START solve_args]
+    # Set parameters, e.g. turn on logging.
+    params = mathopt.SolveParameters(enable_output=True)
+    # [END solve_args]
+
+    # [START solve]
+    # Solve and ensure an optimal solution was found with no errors.
+    # (mathopt.solve may raise a RuntimeError on invalid input or internal solver
+    # errors.)
+    result = mathopt.solve(model, mathopt.SolverType.GLOP, params=params)
+    if result.termination.reason != mathopt.TerminationReason.OPTIMAL:
+        raise RuntimeError(f"model failed to solve: {result.termination}")
+    # [END solve]
+
+    # [START print_results]
+    # Print some information from the result.
+    print("MathOpt solve succeeded")
+    print("Objective value:", result.objective_value())
+    print("x:", result.variable_values()[x])
+    print("y:", result.variable_values()[y])
+    # [END print_results]
+
+
+if __name__ == "__main__":
+    app.run(main)