Changeset 16172 for branches/2931_OR-Tools_LP_MIP/HeuristicLab.MathematicalOptimization/3.3/LinearProgramming/Templates/CompiledLinearProgrammingProblemDefinition.cs

Timestamp:

09/21/18 09:50:12 (6 years ago)

Author:

ddorfmei

Message:

#2931:

• created LinearProgrammingAlgorithm
  • created definitions for all LP/MIP solvers supported by OR-Tools
• created LinearProgrammingProblem
  • created classes required for scripting: LinearProgrammingProblemDefinition, LinearProgrammingProblemDefinitionScript, CompiledLinearProgrammingProblemDefinition
  • created views: LinearProgrammingProblemView, LinearProgrammingProblemDefinitionScriptView
• updated OR-Tools version in ExtLibs to 6.9

Location:

branches/2931_OR-Tools_LP_MIP/HeuristicLab.MathematicalOptimization/3.3/LinearProgramming/Templates

Files:

• . (copied) (copied from branches/2931_OR-Tools_LP_MIP/HeuristicLab.Problems.Programmable/3.3/Templates)
• CompiledLinearProgrammingProblemDefinition.cs (copied) (copied from branches/2931_OR-Tools_LP_MIP/HeuristicLab.Problems.Programmable/3.3/Templates/CompiledSingleObjectiveProblemDefinition.cs) (2 diffs)

branches/2931_OR-Tools_LP_MIP/HeuristicLab.MathematicalOptimization/3.3/LinearProgramming/Templates/CompiledLinearProgrammingProblemDefinition.cs

@@ -1 @@
-using System;
-using System.Linq;
-using System.Collections.Generic;
-using HeuristicLab.Common;
-using HeuristicLab.Core;
+using Google.OrTools.LinearSolver;
 using HeuristicLab.Data;
-using HeuristicLab.Encodings.BinaryVectorEncoding;
-using HeuristicLab.Encodings.IntegerVectorEncoding;
-using HeuristicLab.Encodings.RealVectorEncoding;
-using HeuristicLab.Encodings.PermutationEncoding;
-using HeuristicLab.Encodings.LinearLinkageEncoding;
-using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
+using HeuristicLab.MathematicalOptimization.LinearProgramming.Problems;
 using HeuristicLab.Optimization;
 using HeuristicLab.Problems.Programmable;
 
-namespace HeuristicLab.Problems.Programmable {
-  public class CompiledSingleObjectiveProblemDefinition : CompiledProblemDefinition, ISingleObjectiveProblemDefinition {
-    public bool Maximization { get { return false; } }
+namespace HeuristicLab.MathematicalOptimization.LinearProgramming {
+  public class CompiledLinearProgrammingProblemDefinition : CompiledProblemDefinition, ILinearProgrammingProblemDefinition {
+
+    private Variable x;
+    private Variable y;
 
     public override void Initialize() {
       // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
-      // Define the solution encoding which can also consist of multiple vectors, examples below
-      //Encoding = new BinaryVectorEncoding("b", length: 5);
-      //Encoding = new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 2);
-      //Encoding = new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0);
-      //Encoding = new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute);
-      //Encoding = new LinearLinkageEncoding("l", length: 5);
-      //Encoding = new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12);
-      // The encoding can also be a combination
-      //Encoding = new MultiEncoding()
-      //.Add(new BinaryVectorEncoding("b", length: 5))
-      //.Add(new IntegerVectorEncoding("i", length: 5, min: 2, max: 14, step: 4))
-      //.Add(new RealVectorEncoding("r", length: 5, min: -1.0, max: 1.0))
-      //.Add(new PermutationEncoding("p", length: 5, type: PermutationTypes.Absolute))
-      //.Add(new LinearLinkageEncoding("l", length: 5))
-      //.Add(new SymbolicExpressionTreeEncoding("s", new SimpleSymbolicExpressionGrammar(), 50, 12))
-      ;
       // Add additional initialization code e.g. private variables that you need for evaluating
     }
 
-    public double Evaluate(Individual individual, IRandom random) {
+    public void BuildModel(Solver solver) {
       // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
-      var quality = 0.0;
-      //quality = individual.RealVector("r").Sum(x => x * x);
-      return quality;
+      // Example model taken from https://developers.google.com/optimization/mip/integer_opt 
+      // Define the decision variables
+      x = solver.MakeIntVar(0, 3.5, "x");
+      y = solver.MakeIntVar(0, double.PositiveInfinity, "y");
+      // Define the constraints
+      solver.Add(x + 7 * y <= 17.5);
+      // Define the objective
+      solver.Maximize(x + 10 * y);
     }
 
-    public void Analyze(Individual[] individuals, double[] qualities, ResultCollection results, IRandom random) {
+    public void Analyze(Solver solver, ResultCollection results) {
       // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
-      // Write or update results given the range of vectors and resulting qualities
-      // Uncomment the following lines if you want to retrieve the best individual
-
-      //var orderedIndividuals = individuals.Zip(qualities, (i, q) => new { Individual = i, Quality = q }).OrderBy(z => z.Quality);
-      //var best = Maximization ? orderedIndividuals.Last().Individual : orderedIndividuals.First().Individual;
-
-      //if (!results.ContainsKey("Best Solution")) {
-      //  results.Add(new Result("Best Solution", typeof(RealVector)));
-      //}
-      //results["Best Solution"].Value = (IItem)best.RealVector("r").Clone();
-    }
-
-    public IEnumerable<Individual> GetNeighbors(Individual individual, IRandom random) {
-      // Use vars.yourVariable to access variables in the variable store i.e. yourVariable
-      // Create new vectors, based on the given one that represent small changes
-      // This method is only called from move-based algorithms (Local Search, Simulated Annealing, etc.)
-      while (true) {
-        // Algorithm will draw only a finite amount of samples
-        // Change to a for-loop to return a concrete amount of neighbors
-        var neighbor = individual.Copy();
-        // For instance, perform a single bit-flip in a binary parameter
-        //var bIndex = random.Next(neighbor.BinaryVector("b").Length);
-        //neighbor.BinaryVector("b")[bIndex] = !neighbor.BinaryVector("b")[bIndex];
-        yield return neighbor;
-      }
+      // Write or update results given the solution variables of the decision variables
+      results.AddOrUpdateResult("x", new DoubleValue(x.SolutionValue()));
+      results.AddOrUpdateResult("y", new DoubleValue(y.SolutionValue()));
+      // The decision variables can also be retrieved from the solver
+      //results.AddOrUpdateResult("x", new DoubleValue(solver.LookupVariableOrNull("x").SolutionValue()));
+      //results.AddOrUpdateResult("y", new DoubleValue(solver.LookupVariableOrNull("y").SolutionValue()));
     }
 
@@ -78 +41 @@
   }
 }
-