#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Drawing;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.PluginInfrastructure;
using HeuristicLab.Problems.Instances;

namespace HeuristicLab.Problems.QuadraticAssignment {
  [Item("Quadratic Assignment Problem", "The Quadratic Assignment Problem (QAP) can be described as the problem of assigning N facilities to N fixed locations such that there is exactly one facility in each location and that the sum of the distances multiplied by the connection strength between the facilities becomes minimal.")]
  [Creatable("Problems")]
  [StorableClass]
  public sealed class QuadraticAssignmentProblem : SingleObjectiveHeuristicOptimizationProblem<IQAPEvaluator, IPermutationCreator>, IStorableContent,
    IProblemInstanceConsumer<QAPData>,
    IProblemInstanceConsumer<TSPData> {
    public string Filename { get; set; }

    public static new Image StaticItemImage {
      get { return HeuristicLab.Common.Resources.VSImageLibrary.Type; }
    }

    #region Parameter Properties
    public IValueParameter<ItemSet<Permutation>> BestKnownSolutionsParameter {
      get { return (IValueParameter<ItemSet<Permutation>>)Parameters["BestKnownSolutions"]; }
    }
    public IValueParameter<Permutation> BestKnownSolutionParameter {
      get { return (IValueParameter<Permutation>)Parameters["BestKnownSolution"]; }
    }
    public IValueParameter<DoubleMatrix> WeightsParameter {
      get { return (IValueParameter<DoubleMatrix>)Parameters["Weights"]; }
    }
    public IValueParameter<DoubleMatrix> DistancesParameter {
      get { return (IValueParameter<DoubleMatrix>)Parameters["Distances"]; }
    }
    public IValueParameter<DoubleValue> LowerBoundParameter {
      get { return (IValueParameter<DoubleValue>)Parameters["LowerBound"]; }
    }
    public IValueParameter<DoubleValue> AverageQualityParameter {
      get { return (IValueParameter<DoubleValue>)Parameters["AverageQuality"]; }
    }
    #endregion

    #region Properties
    public ItemSet<Permutation> BestKnownSolutions {
      get { return BestKnownSolutionsParameter.Value; }
      set { BestKnownSolutionsParameter.Value = value; }
    }
    public Permutation BestKnownSolution {
      get { return BestKnownSolutionParameter.Value; }
      set { BestKnownSolutionParameter.Value = value; }
    }
    public DoubleMatrix Weights {
      get { return WeightsParameter.Value; }
      set { WeightsParameter.Value = value; }
    }
    public DoubleMatrix Distances {
      get { return DistancesParameter.Value; }
      set { DistancesParameter.Value = value; }
    }
    public DoubleValue LowerBound {
      get { return LowerBoundParameter.Value; }
      set { LowerBoundParameter.Value = value; }
    }
    public DoubleValue AverageQuality {
      get { return AverageQualityParameter.Value; }
      set { AverageQualityParameter.Value = value; }
    }

    private BestQAPSolutionAnalyzer BestQAPSolutionAnalyzer {
      get { return Operators.OfType<BestQAPSolutionAnalyzer>().FirstOrDefault(); }
    }

    private QAPAlleleFrequencyAnalyzer QAPAlleleFrequencyAnalyzer {
      get { return Operators.OfType<QAPAlleleFrequencyAnalyzer>().FirstOrDefault(); }
    }

    private QAPPopulationDiversityAnalyzer QAPPopulationDiversityAnalyzer {
      get { return Operators.OfType<QAPPopulationDiversityAnalyzer>().FirstOrDefault(); }
    }
    #endregion

    [StorableConstructor]
    private QuadraticAssignmentProblem(bool deserializing) : base(deserializing) { }
    private QuadraticAssignmentProblem(QuadraticAssignmentProblem original, Cloner cloner)
      : base(original, cloner) {
      RegisterEventHandlers();
    }
    public QuadraticAssignmentProblem()
      : base(new QAPEvaluator(), new RandomPermutationCreator()) {
      Parameters.Add(new OptionalValueParameter<ItemSet<Permutation>>("BestKnownSolutions", "The list of best known solutions which is updated whenever a new better solution is found or may be the optimal solution if it is known beforehand.", null));
      Parameters.Add(new OptionalValueParameter<Permutation>("BestKnownSolution", "The best known solution which is updated whenever a new better solution is found or may be the optimal solution if it is known beforehand.", null));
      Parameters.Add(new ValueParameter<DoubleMatrix>("Weights", "The strength of the connection between the facilities.", new DoubleMatrix(5, 5)));
      Parameters.Add(new ValueParameter<DoubleMatrix>("Distances", "The distance matrix which can either be specified directly without the coordinates, or can be calculated automatically from the coordinates.", new DoubleMatrix(5, 5)));
      Parameters.Add(new OptionalValueParameter<DoubleValue>("LowerBound", "The Gilmore-Lawler lower bound to the solution quality."));
      Parameters.Add(new OptionalValueParameter<DoubleValue>("AverageQuality", "The expected quality of a random solution."));

      Maximization.Value = false;
      MaximizationParameter.Hidden = true;

      WeightsParameter.GetsCollected = false;
      Weights = new DoubleMatrix(new double[,] {
        { 0, 1, 0, 0, 1 },
        { 1, 0, 1, 0, 0 },
        { 0, 1, 0, 1, 0 },
        { 0, 0, 1, 0, 1 },
        { 1, 0, 0, 1, 0 }
      });

      DistancesParameter.GetsCollected = false;
      Distances = new DoubleMatrix(new double[,] {
        {   0, 360, 582, 582, 360 },
        { 360,   0, 360, 582, 582 },
        { 582, 360,   0, 360, 582 },
        { 582, 582, 360,   0, 360 },
        { 360, 582, 582, 360,   0 }
      });

      SolutionCreator.PermutationParameter.ActualName = "Assignment";
      ParameterizeSolutionCreator();
      ParameterizeEvaluator();

      InitializeOperators();
      RegisterEventHandlers();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new QuadraticAssignmentProblem(this, cloner);
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      // BackwardsCompatibility3.3
      #region Backwards compatible code, remove with 3.4
      if (!Parameters.ContainsKey("BestKnownSolutions")) {
        Parameters.Add(new OptionalValueParameter<ItemSet<Permutation>>("BestKnownSolutions", "The list of best known solutions which is updated whenever a new better solution is found or may be the optimal solution if it is known beforehand.", null));
      } else if (Parameters["BestKnownSolutions"].GetType().Equals(typeof(OptionalValueParameter<ItemList<Permutation>>))) {
        ItemList<Permutation> list = ((OptionalValueParameter<ItemList<Permutation>>)Parameters["BestKnownSolutions"]).Value;
        Parameters.Remove("BestKnownSolutions");
        Parameters.Add(new OptionalValueParameter<ItemSet<Permutation>>("BestKnownSolutions", "The list of best known solutions which is updated whenever a new better solution is found or may be the optimal solution if it is known beforehand.", (list != null ? new ItemSet<Permutation>(list) : null)));
      }
      if (Parameters.ContainsKey("DistanceMatrix")) {
        DoubleMatrix d = ((ValueParameter<DoubleMatrix>)Parameters["DistanceMatrix"]).Value;
        Parameters.Remove("DistanceMatrix");
        Parameters.Add(new ValueParameter<DoubleMatrix>("Distances", "The distance matrix which can either be specified directly without the coordinates, or can be calculated automatically from the coordinates.", d));
      }
      if (!Parameters.ContainsKey("LowerBound")) {
        Parameters.Add(new OptionalValueParameter<DoubleValue>("LowerBound", "The Gilmore-Lawler lower bound to the solution quality."));
        LowerBound = new DoubleValue(GilmoreLawlerBoundCalculator.CalculateLowerBound(Weights, Distances));
      }
      if (!Parameters.ContainsKey("AverageQuality")) {
        Parameters.Add(new OptionalValueParameter<DoubleValue>("AverageQuality", "The expected quality of a random solution."));
        AverageQuality = new DoubleValue(ComputeAverageQuality());
      }
      #endregion
      RegisterEventHandlers();
    }

    #region Events
    protected override void OnSolutionCreatorChanged() {
      SolutionCreator.PermutationParameter.ActualNameChanged += new EventHandler(SolutionCreator_PermutationParameter_ActualNameChanged);
      ParameterizeSolutionCreator();
      ParameterizeEvaluator();
      ParameterizeAnalyzers();
      ParameterizeOperators();
      base.OnSolutionCreatorChanged();
    }
    protected override void OnEvaluatorChanged() {
      Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
      ParameterizeEvaluator();
      ParameterizeAnalyzers();
      ParameterizeOperators();
      base.OnEvaluatorChanged();
    }

    private void SolutionCreator_PermutationParameter_ActualNameChanged(object sender, EventArgs e) {
      ParameterizeEvaluator();
      ParameterizeAnalyzers();
      ParameterizeOperators();
    }
    private void Evaluator_QualityParameter_ActualNameChanged(object sender, EventArgs e) {
      ParameterizeAnalyzers();
      ParameterizeOperators();
    }
    private void WeightsParameter_ValueChanged(object sender, EventArgs e) {
      Weights.RowsChanged += new EventHandler(Weights_RowsChanged);
      Weights.ColumnsChanged += new EventHandler(Weights_ColumnsChanged);
      Weights.ToStringChanged += new EventHandler(Weights_ToStringChanged);
      ParameterizeSolutionCreator();
      ParameterizeEvaluator();
      ParameterizeOperators();
      AdjustDistanceMatrix();
    }
    private void Weights_RowsChanged(object sender, EventArgs e) {
      if (Weights.Rows != Weights.Columns)
        ((IStringConvertibleMatrix)Weights).Columns = Weights.Rows;
      else {
        ParameterizeSolutionCreator();
        ParameterizeEvaluator();
        ParameterizeOperators();
        AdjustDistanceMatrix();
      }
    }
    private void Weights_ColumnsChanged(object sender, EventArgs e) {
      if (Weights.Rows != Weights.Columns)
        ((IStringConvertibleMatrix)Weights).Rows = Weights.Columns;
      else {
        ParameterizeSolutionCreator();
        ParameterizeEvaluator();
        ParameterizeOperators();
        AdjustDistanceMatrix();
      }
    }
    private void Weights_ToStringChanged(object sender, EventArgs e) {
      UpdateParameterValues();
    }
    private void DistancesParameter_ValueChanged(object sender, EventArgs e) {
      Distances.RowsChanged += new EventHandler(Distances_RowsChanged);
      Distances.ColumnsChanged += new EventHandler(Distances_ColumnsChanged);
      Distances.ToStringChanged += new EventHandler(Distances_ToStringChanged);
      ParameterizeSolutionCreator();
      ParameterizeEvaluator();
      ParameterizeOperators();
      AdjustWeightsMatrix();
    }
    private void Distances_RowsChanged(object sender, EventArgs e) {
      if (Distances.Rows != Distances.Columns)
        ((IStringConvertibleMatrix)Distances).Columns = Distances.Rows;
      else {
        ParameterizeSolutionCreator();
        ParameterizeEvaluator();
        ParameterizeOperators();
        AdjustWeightsMatrix();
      }
    }
    private void Distances_ColumnsChanged(object sender, EventArgs e) {
      if (Distances.Rows != Distances.Columns)
        ((IStringConvertibleMatrix)Distances).Rows = Distances.Columns;
      else {
        ParameterizeSolutionCreator();
        ParameterizeEvaluator();
        ParameterizeOperators();
        AdjustWeightsMatrix();
      }
    }
    private void Distances_ToStringChanged(object sender, EventArgs e) {
      UpdateParameterValues();
    }
    #endregion

    private void RegisterEventHandlers() {
      SolutionCreator.PermutationParameter.ActualNameChanged += new EventHandler(SolutionCreator_PermutationParameter_ActualNameChanged);
      Evaluator.QualityParameter.ActualNameChanged += new EventHandler(Evaluator_QualityParameter_ActualNameChanged);
      WeightsParameter.ValueChanged += new EventHandler(WeightsParameter_ValueChanged);
      Weights.RowsChanged += new EventHandler(Weights_RowsChanged);
      Weights.ColumnsChanged += new EventHandler(Weights_ColumnsChanged);
      Weights.ToStringChanged += new EventHandler(Weights_ToStringChanged);
      DistancesParameter.ValueChanged += new EventHandler(DistancesParameter_ValueChanged);
      Distances.RowsChanged += new EventHandler(Distances_RowsChanged);
      Distances.ColumnsChanged += new EventHandler(Distances_ColumnsChanged);
      Distances.ToStringChanged += new EventHandler(Distances_ToStringChanged);
    }

    #region Helpers
    private void InitializeOperators() {
      var defaultOperators = new HashSet<IPermutationOperator>(new IPermutationOperator[] {
        new PartiallyMatchedCrossover(),
        new Swap2Manipulator(),
        new ExhaustiveSwap2MoveGenerator()
      });
      Operators.AddRange(defaultOperators);
      Operators.AddRange(ApplicationManager.Manager.GetInstances<IPermutationOperator>().Except(defaultOperators, new TypeEqualityComparer<IPermutationOperator>()));
      Operators.RemoveAll(x => x is ISingleObjectiveMoveEvaluator);
      Operators.AddRange(ApplicationManager.Manager.GetInstances<IQAPMoveEvaluator>());
      Operators.Add(new BestQAPSolutionAnalyzer());
      Operators.Add(new QAPAlleleFrequencyAnalyzer());
      Operators.Add(new QAPPopulationDiversityAnalyzer());
      Operators.Add(new QAPExhaustiveSwap2LocalImprovement());
      Operators.Add(new QAPSimilarityCalculator());
      ParameterizeAnalyzers();
      ParameterizeOperators();
    }
    private void ParameterizeSolutionCreator() {
      if (SolutionCreator != null) {
        SolutionCreator.PermutationTypeParameter.Value = new PermutationType(PermutationTypes.Absolute);
        SolutionCreator.LengthParameter.Value = new IntValue(Weights.Rows);
      }
    }
    private void ParameterizeEvaluator() {
      if (Evaluator != null) {
        Evaluator.PermutationParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
        Evaluator.DistancesParameter.ActualName = DistancesParameter.Name;
        Evaluator.WeightsParameter.ActualName = WeightsParameter.Name;
      }
    }
    private void ParameterizeAnalyzers() {
      if (BestQAPSolutionAnalyzer != null) {
        BestQAPSolutionAnalyzer.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
        BestQAPSolutionAnalyzer.DistancesParameter.ActualName = DistancesParameter.Name;
        BestQAPSolutionAnalyzer.WeightsParameter.ActualName = WeightsParameter.Name;
        BestQAPSolutionAnalyzer.PermutationParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
        BestQAPSolutionAnalyzer.ResultsParameter.ActualName = "Results";
        BestQAPSolutionAnalyzer.BestKnownQualityParameter.ActualName = BestKnownQualityParameter.Name;
        BestQAPSolutionAnalyzer.BestKnownSolutionsParameter.ActualName = BestKnownSolutionsParameter.Name;
        BestQAPSolutionAnalyzer.MaximizationParameter.ActualName = MaximizationParameter.Name;
      }
      if (QAPAlleleFrequencyAnalyzer != null) {
        QAPAlleleFrequencyAnalyzer.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
        QAPAlleleFrequencyAnalyzer.BestKnownSolutionParameter.ActualName = BestKnownSolutionParameter.Name;
        QAPAlleleFrequencyAnalyzer.DistancesParameter.ActualName = DistancesParameter.Name;
        QAPAlleleFrequencyAnalyzer.MaximizationParameter.ActualName = MaximizationParameter.Name;
        QAPAlleleFrequencyAnalyzer.ResultsParameter.ActualName = "Results";
        QAPAlleleFrequencyAnalyzer.SolutionParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
        QAPAlleleFrequencyAnalyzer.WeightsParameter.ActualName = WeightsParameter.Name;
      }
      if (QAPPopulationDiversityAnalyzer != null) {
        QAPPopulationDiversityAnalyzer.MaximizationParameter.ActualName = MaximizationParameter.Name;
        QAPPopulationDiversityAnalyzer.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
        QAPPopulationDiversityAnalyzer.ResultsParameter.ActualName = "Results";
        QAPPopulationDiversityAnalyzer.SolutionParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
      }
    }
    private void ParameterizeOperators() {
      foreach (IPermutationCrossover op in Operators.OfType<IPermutationCrossover>()) {
        op.ParentsParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
        op.ChildParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
      }
      foreach (IPermutationManipulator op in Operators.OfType<IPermutationManipulator>()) {
        op.PermutationParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
      }
      foreach (IPermutationMoveOperator op in Operators.OfType<IPermutationMoveOperator>()) {
        op.PermutationParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
      }
      if (Operators.OfType<IMoveGenerator>().Any()) {
        if (Operators.OfType<IMoveGenerator>().OfType<IPermutationInversionMoveOperator>().Any()) {
          string inversionMove = Operators.OfType<IMoveGenerator>().OfType<IPermutationInversionMoveOperator>().First().InversionMoveParameter.ActualName;
          foreach (IPermutationInversionMoveOperator op in Operators.OfType<IPermutationInversionMoveOperator>())
            op.InversionMoveParameter.ActualName = inversionMove;
        }
        if (Operators.OfType<IMoveGenerator>().OfType<IPermutationTranslocationMoveOperator>().Any()) {
          string translocationMove = Operators.OfType<IMoveGenerator>().OfType<IPermutationTranslocationMoveOperator>().First().TranslocationMoveParameter.ActualName;
          foreach (IPermutationTranslocationMoveOperator op in Operators.OfType<IPermutationTranslocationMoveOperator>())
            op.TranslocationMoveParameter.ActualName = translocationMove;
        }
        if (Operators.OfType<IMoveGenerator>().OfType<IPermutationSwap2MoveOperator>().Any()) {
          string swapMove = Operators.OfType<IMoveGenerator>().OfType<IPermutationSwap2MoveOperator>().First().Swap2MoveParameter.ActualName;
          foreach (IPermutationSwap2MoveOperator op in Operators.OfType<IPermutationSwap2MoveOperator>()) {
            op.Swap2MoveParameter.ActualName = swapMove;
          }
        }
      }
      foreach (var op in Operators.OfType<IPermutationMultiNeighborhoodShakingOperator>())
        op.PermutationParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;

      QAPExhaustiveSwap2LocalImprovement localOpt = Operators.OfType<QAPExhaustiveSwap2LocalImprovement>().SingleOrDefault();
      if (localOpt != null) {
        localOpt.AssignmentParameter.ActualName = SolutionCreator.PermutationParameter.ActualName;
        localOpt.DistancesParameter.ActualName = DistancesParameter.Name;
        localOpt.MaximizationParameter.ActualName = MaximizationParameter.Name;
        localOpt.QualityParameter.ActualName = Evaluator.QualityParameter.ActualName;
        localOpt.WeightsParameter.ActualName = WeightsParameter.Name;
      }

      QAPSimilarityCalculator similarityCalculator = Operators.OfType<QAPSimilarityCalculator>().SingleOrDefault();
      if (similarityCalculator != null) {
        similarityCalculator.SolutionVariableName = SolutionCreator.PermutationParameter.ActualName;
        similarityCalculator.QualityVariableName = Evaluator.QualityParameter.ActualName;
      }
    }

    private void AdjustDistanceMatrix() {
      if (Distances.Rows != Weights.Rows || Distances.Columns != Weights.Columns) {
        ((IStringConvertibleMatrix)Distances).Rows = Weights.Rows;
      }
    }

    private void AdjustWeightsMatrix() {
      if (Weights.Rows != Distances.Rows || Weights.Columns != Distances.Columns) {
        ((IStringConvertibleMatrix)Weights).Rows = Distances.Rows;
      }
    }

    private void UpdateParameterValues() {
      Permutation lbSolution;
      // calculate the optimum of a LAP relaxation and use it as lower bound of our QAP
      LowerBound = new DoubleValue(GilmoreLawlerBoundCalculator.CalculateLowerBound(Weights, Distances, out lbSolution));
      // evalute the LAP optimal solution as if it was a QAP solution
      var lbSolutionQuality = QAPEvaluator.Apply(lbSolution, Weights, Distances);
      // in case both qualities are the same it means that the LAP optimum is also a QAP optimum
      if (LowerBound.Value.IsAlmost(lbSolutionQuality)) {
        BestKnownSolution = lbSolution;
        BestKnownQuality = new DoubleValue(LowerBound.Value);
      }
      AverageQuality = new DoubleValue(ComputeAverageQuality());
    }

    private double ComputeAverageQuality() {
      double rt = 0, rd = 0, wt = 0, wd = 0;
      int n = Weights.Rows;
      for (int i = 0; i < n; i++)
        for (int j = 0; j < n; j++) {
          if (i == j) {
            rd += Distances[i, i];
            wd += Weights[i, i];
          } else {
            rt += Distances[i, j];
            wt += Weights[i, j];
          }
        }

      return rt * wt / (n * (n - 1)) + rd * wd / n;
    }
    #endregion

    public void Load(QAPData data) {
      var weights = new DoubleMatrix(data.Weights);
      var distances = new DoubleMatrix(data.Distances);
      Name = data.Name;
      Description = data.Description;
      Load(weights, distances);
      if (data.BestKnownQuality.HasValue) BestKnownQuality = new DoubleValue(data.BestKnownQuality.Value);
      EvaluateAndLoadAssignment(data.BestKnownAssignment);
      OnReset();
    }

    public void Load(TSPData data) {
      if (data.Dimension > 1000)
        throw new System.IO.InvalidDataException("Instances with more than 1000 customers are not supported by the QAP.");
      var weights = new DoubleMatrix(data.Dimension, data.Dimension);
      for (int i = 0; i < data.Dimension; i++)
        weights[i, (i + 1) % data.Dimension] = 1;
      var distances = new DoubleMatrix(data.GetDistanceMatrix());
      Name = data.Name;
      Description = data.Description;
      Load(weights, distances);
      if (data.BestKnownQuality.HasValue) BestKnownQuality = new DoubleValue(data.BestKnownQuality.Value);
      EvaluateAndLoadAssignment(data.BestKnownTour);
      OnReset();
    }

    public void Load(DoubleMatrix weights, DoubleMatrix distances) {
      if (weights == null || weights.Rows == 0)
        throw new System.IO.InvalidDataException("The given instance does not contain weights!");
      if (weights.Rows != weights.Columns)
        throw new System.IO.InvalidDataException("The weights matrix is not a square matrix!");
      if (distances == null || distances.Rows == 0)
        throw new System.IO.InvalidDataException("The given instance does not contain distances!");
      if (distances.Rows != distances.Columns)
        throw new System.IO.InvalidDataException("The distances matrix is not a square matrix!");
      if (weights.Rows != distances.Columns)
        throw new System.IO.InvalidDataException("The weights matrix and the distance matrix are not of equal size!");

      Weights = weights;
      Distances = distances;

      BestKnownQuality = null;
      BestKnownSolution = null;
      BestKnownSolutions = null;
      UpdateParameterValues();
    }

    public void EvaluateAndLoadAssignment(int[] assignment) {
      if (assignment == null || assignment.Length == 0) return;
      var vector = new Permutation(PermutationTypes.Absolute, assignment);
      var result = QAPEvaluator.Apply(vector, Weights, Distances);
      BestKnownQuality = new DoubleValue(result);
      BestKnownSolution = vector;
      BestKnownSolutions = new ItemSet<Permutation>();
      BestKnownSolutions.Add((Permutation)vector.Clone());
    }
  }
}