Add region tag to linear solver samples

ortools/linear_solver/samples/SimpleLpProgram.cs

@@ -20,29 +20,46 @@ public class SimpleLpProgram
 {
   static void Main()
   {
+    // [START solver]
     // Create the linear solver with the GLOP backend.
     Solver solver = Solver.CreateSolver("SimpleLpProgram", "GLOP_LINEAR_PROGRAMMING");
+    // [END solver]
 
+    // [START variables]
     // Create the variables x and y.
     Variable x = solver.MakeNumVar(0.0, 1.0, "x");
     Variable y = solver.MakeNumVar(0.0, 2.0, "y");
 
+    Console.WriteLine("Number of variables = " + solver.NumVariables());
+    // [END variables]
+
+    // [START constraints]
     // Create a linear constraint, 0 <= x + y <= 2.
     Constraint ct = solver.MakeConstraint(0.0, 2.0, "ct");
     ct.SetCoefficient(x, 1);
     ct.SetCoefficient(y, 1);
 
+    Console.WriteLine("Number of constraints = " + solver.NumConstraints());
+    // [END constraints]
+
+    // [START objective]
     // Create the objective function, 3 * x + y.
     Objective objective = solver.Objective();
     objective.SetCoefficient(x, 3);
     objective.SetCoefficient(y, 1);
     objective.SetMaximization();
+    // [END objective]
 
-    // Call the solver and display the results.
+    // [START solve]
     solver.Solve();
+    // [END solve]
+
+    // [START print_solution]
     Console.WriteLine("Solution:");
+    Console.WriteLine("Objective value = " + solver.Objective().Value());
     Console.WriteLine("x = " + x.SolutionValue());
     Console.WriteLine("y = " + y.SolutionValue());
+    // [END print_solution]
   }
 }
 // [END program]

ortools/linear_solver/samples/SimpleLpProgram.java

@@ -13,10 +13,12 @@
 
 // Minimal example to call the GLOP solver.
 // [START program]
+// [START import]
 import com.google.ortools.linearsolver.MPConstraint;
 import com.google.ortools.linearsolver.MPObjective;
 import com.google.ortools.linearsolver.MPSolver;
 import com.google.ortools.linearsolver.MPVariable;
+// [END import]
 
 /** Minimal Linear Programming example to showcase calling the solver.*/
 public class SimpleLpProgram {
@@ -25,30 +27,47 @@ public class SimpleLpProgram {
   }
 
   public static void main(String[] args) throws Exception {
+    // [START solver]
     // Create the linear solver with the GLOP backend.
-    MPSolver solver =
-        new MPSolver("SimpleLpProgram", MPSolver.OptimizationProblemType.GLOP_LINEAR_PROGRAMMING);
+    MPSolver solver = new MPSolver(
+        "SimpleLpProgram", MPSolver.OptimizationProblemType.GLOP_LINEAR_PROGRAMMING);
+    // [END solver]
 
+    // [START variables]
     // Create the variables x and y.
     MPVariable x = solver.makeNumVar(0.0, 1.0, "x");
     MPVariable y = solver.makeNumVar(0.0, 2.0, "y");
 
+    System.out.println("Number of variables = " + solver.numVariables());
+    // [END variables]
+
+    // [START constraints]
     // Create a linear constraint, 0 <= x + y <= 2.
     MPConstraint ct = solver.makeConstraint(0.0, 2.0, "ct");
     ct.setCoefficient(x, 1);
     ct.setCoefficient(y, 1);
 
+    System.out.println("Number of constraints = " + solver.numConstraints());
+    // [END constraints]
+
+    // [START objective]
     // Create the objective function, 3 * x + y.
     MPObjective objective = solver.objective();
     objective.setCoefficient(x, 3);
     objective.setCoefficient(y, 1);
     objective.setMaximization();
+    // [END objective]
 
-    // Call the solver and display the results.
+    // [START solve]
     solver.solve();
+    // [END solve]
+
+    // [START print_solution]
     System.out.println("Solution:");
+    System.out.println("Objective value = " + objective.value());
     System.out.println("x = " + x.solutionValue());
     System.out.println("y = " + y.solutionValue());
+    // [END print_solution]
   }
 }
 // [END program]

ortools/linear_solver/samples/simple_lp_program.cc

@@ -13,34 +13,52 @@
 
 // Minimal example to call the GLOP solver.
 // [START program]
-#include <iostream>
+// [START import]
 #include "ortools/linear_solver/linear_solver.h"
+// [END import]
 
 namespace operations_research {
 void run() {
+  // [START solver]
   // Create the linear solver with the GLOP backend.
   MPSolver solver("simple_lp_program", MPSolver::GLOP_LINEAR_PROGRAMMING);
+  // [END solver]
 
+  // [START variables]
   // Create the variables x and y.
   MPVariable* const x = solver.MakeNumVar(0.0, 1, "x");
   MPVariable* const y = solver.MakeNumVar(0.0, 2, "y");
 
+  LOG(INFO) << "Number of variables = " << solver.NumVariables();
+  // [END variables]
+
+  // [START constraints]
   // Create a linear constraint, 0 <= x + y <= 2.
   MPConstraint* const ct = solver.MakeRowConstraint(0.0, 2.0, "ct");
   ct->SetCoefficient(x, 1);
   ct->SetCoefficient(y, 1);
 
+  LOG(INFO) << "Number of constraints = " << solver.NumConstraints();
+  // [END constraints]
+
+  // [START objective]
   // Create the objective function, 3 * x + y.
   MPObjective* const objective = solver.MutableObjective();
   objective->SetCoefficient(x, 3);
   objective->SetCoefficient(y, 1);
   objective->SetMaximization();
+  // [END objective]
 
-  // Call the solver and display the results.
+  // [START solve]
   solver.Solve();
+  // [END solve]
+
+  // [START print_solution]
   std::cout << "Solution:" << std::endl;
-  std::cout << "x = " << x->solution_value() << std::endl;
-  std::cout << "y = " << y->solution_value() << std::endl;
+  LOG(INFO) << "Objective value = " << objective->Value();
+  LOG(INFO) << "x = " << x->solution_value();
+  LOG(INFO) << "y = " << y->solution_value();
+  // [END print_solution]
 }
 }  // namespace operations_research
 

ortools/linear_solver/samples/simple_lp_program.py

@@ -11,39 +11,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Minimal example to call the GLOP solver."""
-
 # [START program]
+# [START import]
 from __future__ import print_function
-
 from ortools.linear_solver import pywraplp
+# [END import]
 
 
 def main():
+    # [START solver]
     # Create the linear solver with the GLOP backend.
     solver = pywraplp.Solver('simple_lp_program',
                              pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)
+    # [END solver]
 
+    # [START variables]
     # Create the variables x and y.
     x = solver.NumVar(0, 1, 'x')
     y = solver.NumVar(0, 2, 'y')
 
+    print(('Number of variables = %d' % solver.NumVariables()))
+    # [END variables]
+
+    # [START constraints]
     # Create a linear constraint, 0 <= x + y <= 2.
     ct = solver.Constraint(0, 2, 'ct')
     ct.SetCoefficient(x, 1)
     ct.SetCoefficient(y, 1)
 
+    print(('Number of constraints = ', solver.NumConstraints()))
+    # [END constraints]
+
+    # [START objective]
     # Create the objective function, 3 * x + y.
     objective = solver.Objective()
     objective.SetCoefficient(x, 3)
     objective.SetCoefficient(y, 1)
     objective.SetMaximization()
+    # [END objective]
 
-    # Call the solver and display the results.
+    # [START solve]
     solver.Solve()
+    # [END solve]
+
+    # [START print_solution]
     print('Solution:')
     print('Objective value = ', objective.Value())
     print('x = ', x.solution_value())
     print('y = ', y.solution_value())
+    # [END print_solution]
 
 
 if __name__ == '__main__':

ortools/linear_solver/samples/simple_mip_program.cc

@@ -66,9 +66,9 @@ void simple_mip_program() {
 
   // [START print_solution]
   LOG(INFO) << "Solution:";
+  LOG(INFO) << "Objective value = " << objective->Value();
   LOG(INFO) << "x = " << x->solution_value();
   LOG(INFO) << "y = " << y->solution_value();
-  LOG(INFO) << "Optimal objective value = " << objective->Value();
   // [END print_solution]
 
   // [START advanced]