protected ContinuousSolution[] Diversify(List <ContinuousSolution> diverse_set)
{
ContinuousSolution[] ref_set = new ContinuousSolution[mReferenceSetSize];
for (int i = 0; i < mNumElite; ++i)
{
ref_set[i] = diverse_set[i];
}
int remainder_set_size = mDiverseSetSize - mNumElite;
ContinuousSolution[] remainder_set = new ContinuousSolution[remainder_set_size];
for (int i = mNumElite; i < mDiverseSetSize; ++i)
{
remainder_set[i - mNumElite] = diverse_set[i];
}
Dictionary <ContinuousSolution, double> remainder_distance_set = new Dictionary <ContinuousSolution, double>();
for (int i = 0; i < remainder_set_size; ++i)
{
double distance = 0;
for (int j = 0; j < mNumElite; ++j)
{
distance += remainder_set[i].GetDistance2(ref_set[j]);
}
remainder_distance_set[remainder_set[i]] = distance;
}
remainder_set = remainder_set.OrderBy(s => remainder_distance_set[s]).ToArray();
for (int i = mNumElite; i < mReferenceSetSize; ++i)
{
ref_set[i] = remainder_set[i - mNumElite];
}
return(ref_set);
}
public void CalculateCentroid(List <ContinuousSolution> solutions, ContinuousSolution centroid)
{
int solution_count = solutions.Count - 1;
int dimension = solutions[0].Values.Length;
double[] x_centroid = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
x_centroid[d] = 0;
}
for (int i = 0; i < solution_count; ++i)
{
ContinuousSolution s = solutions[i];
double[] x = s.Values;
for (int d = 0; d < dimension; ++d)
{
x_centroid[d] += x[d];
}
}
for (int d = 0; d < dimension; ++d)
{
x_centroid[d] /= solution_count;
}
centroid.Values = x_centroid;
}
public override ContinuousSolution Minimize(CostEvaluationMethod evaluate, GradientEvaluationMethod calc_grad, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
if (mLocalSearch == null)
{
throw new ArgumentNullException();
}
mLocalSearch.LowerBounds = mLowerBounds;
mLocalSearch.UpperBounds = mUpperBounds;
int problem_size = mDimension;
ContinuousSolution best_solution = new ContinuousSolution();
while (!should_terminate(improvement, iteration))
{
double[] x = GreedyConstructRandomSolution(evaluate, constraints, problem_size);
ContinuousSolution x_pi_refined = mLocalSearch.Minimize(x, evaluate, calc_grad, mLocalSearchShouldTerminate, constraints);
if (best_solution.TryUpdateSolution(x_pi_refined.Values, x_pi_refined.Cost, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
public static double[] GetMutationStrategy(this ContinuousSolution s)
{
if (s.HasAttribute("MutationStrategy"))
{
return(s["MutationStrategy"] as double[]);
}
return(null);
}
public static int GetWins(this ContinuousSolution s)
{
if (s.HasAttribute("Wins"))
{
return((int)s["Wins"]);
}
return(0);
}
protected ContinuousSolution Mutate(ContinuousSolution solution, double[] lower_bounds, double[] upper_bounds, object constraints)
{
ContinuousSolution child = MutateVector(solution, lower_bounds, upper_bounds, constraints);
double[] child_strategy = MutateStrategy(solution.GetMutationStrategy());
child.SetMutationStrategy(child_strategy);
return(child);
}
public ContinuousSolution[] Recombine(ContinuousSolution[] subset, object constraints)
{
ContinuousSolution a = subset[0];
ContinuousSolution b = subset[1];
ContinuousSolution d = (a - b) / 2;
double[] lower_bounds = null;
double[] upper_bounds = null;
if (mLowerBounds == null || mUpperBounds == null)
{
if (constraints != null && constraints is Tuple <double[], double[]> )
{
Tuple <double[], double[]> bounds = constraints as Tuple <double[], double[]>;
lower_bounds = bounds.Item1;
upper_bounds = bounds.Item2;
}
else
{
throw new InvalidCastException();
}
}
else
{
lower_bounds = mLowerBounds;
upper_bounds = mUpperBounds;
}
if (lower_bounds.Length < d.Length)
{
throw new IndexOutOfRangeException();
}
if (upper_bounds.Length < d.Length)
{
throw new IndexOutOfRangeException();
}
ContinuousSolution[] children = new ContinuousSolution[subset.Length];
for (int i = 0; i < subset.Length; ++i)
{
double[] x = new double[d.Length];
for (int j = 0; j < d.Length; ++j)
{
int direction = RandomEngine.NextDouble() < 0.5 ? 1 : -1;
double r = RandomEngine.NextDouble();
x[j] = subset[i][j] + d[j] * direction * r;
x[j] = System.Math.Max(lower_bounds[j], x[j]);
x[j] = System.Math.Min(upper_bounds[j], x[j]);
}
children[i] = new ContinuousSolution(x, double.MaxValue);
}
return(children);
}
public static bool IsNew(this ContinuousSolution s)
{
if (s.HasAttribute("IsNew"))
{
return(Convert.ToInt32(s["IsNew"]) == 1);
}
else
{
return(false);
}
}
public override void Train(List <T> records)
{
HashSet <string> class_labels = new HashSet <string>();
foreach (T rec in records)
{
class_labels.Add(rec.Label);
}
mClassFieldLabels = class_labels.ToList();
mTheta.Clear();
int sample_count = records.Count;
if (sample_count == 0)
{
throw new ArgumentException("sample_count==0");
}
int dimension = records[0].Dimension;
double[,] X = new double[sample_count, dimension];
int[] Y = new int[sample_count];
for (int i = 0; i < sample_count; ++i)
{
T rec = records[i];
for (int d = 0; d < dimension; ++d)
{
X[i, d] = rec[d];
}
}
double[] theta_0 = new double[dimension];
foreach (string class_label in mClassFieldLabels)
{
for (int i = 0; i < sample_count; ++i)
{
T rec = records[i];
Y[i] = rec.Label == class_label ? 1 : 0;
}
for (int d = 0; d < dimension; ++d)
{
theta_0[d] = 0;
}
LinearSVMCostFunction f = new LinearSVMCostFunction(X, Y, dimension, sample_count);
f.C = mC;
ContinuousSolution solution = mLocalSearcher.Minimize(theta_0, f, mMaxSolverIteration);
mTheta[class_label] = solution.Values;
}
}
protected virtual ContinuousSolution MutateVector(ContinuousSolution solution, double[] lower_bounds, double[] upper_bounds, object constraints)
{
double[] x = new double[mDimension];
double[] strategy = solution.GetMutationStrategy();
for (int i = 0; i < mDimension; ++i)
{
x[i] = solution[i] + RandomEngine.Gauss(0, strategy[i]);
x[i] = System.Math.Max(lower_bounds[i], x[i]);
x[i] = System.Math.Min(upper_bounds[i], x[i]);
}
return(new ContinuousSolution(x, double.MaxValue));
}
protected virtual ContinuousSolution Recombine(ContinuousSolution[] pop, double[] lower_bounds, double[] upper_bounds, object constraints)
{
ContinuousSolution[] parents = new ContinuousSolution[mSelectedParentCount_rho];
for (int i = 0; i < mSelectedParentCount_rho; ++i)
{
parents[i] = BinaryTournamentSelection(pop);
}
HashSet <int> intersection_points = new HashSet <int>();
for (int i = 0; i < mSelectedParentCount_rho - 1; ++i)
{
int index = RandomEngine.NextInt(mDimension);
while (intersection_points.Contains(index))
{
index = RandomEngine.NextInt(mDimension);
}
intersection_points.Add(index);
}
int[] intersect_pts = intersection_points.OrderBy(s => s).ToArray();
int start_pt = 0;
double[] x = new double[mDimension];
double[] child_strategy = new double[mDimension];
double[] parent_strategy = null;
for (int i = 0; i < intersect_pts.Length; ++i)
{
int end_pt = intersect_pts[i];
parent_strategy = parents[i].GetMutationStrategy();
for (int j = start_pt; j < end_pt; ++j)
{
x[j] = parents[i][j];
child_strategy[j] = parent_strategy[j];
}
}
parent_strategy = parents[mSelectedParentCount_rho - 1].GetMutationStrategy();
for (int i = start_pt; i < mDimension; ++i)
{
x[i] = parents[mSelectedParentCount_rho - 1][i];
child_strategy[i] = parent_strategy[i];
}
ContinuousSolution child = new ContinuousSolution(x, double.MaxValue);
child.SetMutationStrategy(child_strategy);
return(child);
}
public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
DifferentialEvolution s = new DifferentialEvolution(f);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public override void Train(List <T> records)
{
HashSet <string> class_labels = new HashSet <string>();
foreach (T rec in records)
{
class_labels.Add(rec.Label);
}
mClassFieldLabels = class_labels.ToList();
mTheta.Clear();
mKernelCentroids.Clear();
int m = records.Count;
if (m == 0)
{
throw new ArgumentException("sample count==0");
}
int dimension = records[0].Dimension;
int[] Y = new int[m];
for (int i = 0; i < m; ++i)
{
T rec = records[i];
mKernelCentroids.Add((double[])rec.Data.Clone());
}
double[] theta_0 = new double[m + 1];
foreach (string class_label in mClassFieldLabels)
{
for (int i = 0; i < m; ++i)
{
T rec = records[i];
Y[i] = rec.Label == class_label ? 1 : 0;
}
for (int d = 0; d < dimension; ++d)
{
theta_0[d] = 0;
}
KernelSVMCostFunction <T> f = new KernelSVMCostFunction <T>(records, Y, m, mKernel);
f.C = mC;
ContinuousSolution solution = mLocalSearcher.Minimize(theta_0, f, mMaxSolverIteration);
mTheta[class_label] = solution.Values;
}
}
public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
int popSize = 100; // population Size
EvolutionaryProgramming s = new EvolutionaryProgramming(f, popSize);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_grad, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
double fx_0 = evaluate(x_0, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x_0, fx_0);
int dimension = x_0.Length;
while (!should_terminate(improvement, iteration))
{
double[] best_x_in_neighborhood = null;
double best_x_in_neighborhood_fx = double.MaxValue;
int move_id = -1;
for (int i = 0; i < dimension; ++i)
{
if (!IsMoveTabu(i))
{
double[] x_pi = GetNeighbor(best_solution.Values, i, constraints);
double fx_pi = evaluate(x_pi, mLowerBounds, mUpperBounds, constraints);
if (fx_pi < best_x_in_neighborhood_fx)
{
best_x_in_neighborhood = x_pi;
best_x_in_neighborhood_fx = fx_pi;
move_id = i;
}
}
}
if (best_x_in_neighborhood != null)
{
if (best_solution.TryUpdateSolution(best_x_in_neighborhood, best_x_in_neighborhood_fx, out improvement))
{
TabuMove(move_id);
OnSolutionUpdated(best_solution, iteration);
}
}
LowerTabuList();
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
public void Reflect(List <ContinuousSolution> solutions, ContinuousSolution centroid, double alpha, out double[] x_r)
{
int dimension = centroid.Values.Length;
double[] x_centroid = centroid.Values;
int solution_count = solutions.Count - 1;
ContinuousSolution last_solution = solutions[solution_count];
double[] x_last = last_solution.Values;
x_r = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
x_r[d] = x_centroid[d] + alpha * (x_centroid[d] - x_last[d]);
}
}
public void Expand(List <ContinuousSolution> solutions, ContinuousSolution centroid, double gamma, out double[] x_e)
{
int dimension = centroid.Values.Length;
double[] x_centroid = centroid.Values;
int solution_count = solutions.Count - 1;
ContinuousSolution last_solution = solutions[solution_count];
double[] x_last = last_solution.Values;
x_e = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
x_e[d] = x_centroid[d] + gamma * (x_centroid[d] - x_last[d]);
}
}
public static void RunMain(string[] args)
{
CostFunction_RosenbrockSaddle f = new CostFunction_RosenbrockSaddle();
int maxIterations = 200;
int mu = 30;
int lambda = 20;
EvolutionStrategy s = new EvolutionStrategy(f, mu, lambda);
s.SolutionUpdated += (best_solution, step) =>
{
Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};
ContinuousSolution finalSolution = s.Minimize(f, maxIterations);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_grad, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
if (mNeighborhoods.Count == 0)
{
throw new ArgumentNullException();
}
mNeighborhoods.LowerBounds = mLowerBounds;
mNeighborhoods.UpperBounds = mUpperBounds;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x, fx);
int neighborhood_count = mNeighborhoods.Count;
ContinuousSolution current_best = null;
while (!should_terminate(improvement, iteration))
{
for (int l = 0; l < neighborhood_count; ++l)
{
SingleTrajectoryContinuousSolver local_search = mNeighborhoods.GetLocalSearchAt(l);
SingleTrajectoryContinuousSolver.TerminationEvaluationMethod termination_condition = mNeighborhoods.GetTerminationConditionAt(l);
current_best = local_search.Minimize(x, evaluate, calc_grad, termination_condition, constraints);
x = current_best.Values;
fx = current_best.Cost;
if (best_solution.TryUpdateSolution(x, fx, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(best_solution, iteration);
iteration++;
}
}
return(best_solution);
}
protected bool ExploreSubsets(ref ContinuousSolution[] ref_set, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_grad, object constraints)
{
bool is_improved = false;
List <ContinuousSolution[]> subsets = SelectSubsets(ref_set);
for (int i = 0; i < ref_set.Length; ++i)
{
ref_set[i].SetIsNew(false);
}
for (int i = 0; i < subsets.Count; ++i)
{
ContinuousSolution[] subset = subsets[i];
ContinuousSolution[] candidate_solutions = Recombine(subset, constraints);
ContinuousSolution[] improvements = new ContinuousSolution[candidate_solutions.Length];
for (int j = 0; j < improvements.Length; ++j)
{
//double fx = evaluate(candidate_solutions[j].Values, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution s = mLocalSearch.Minimize(candidate_solutions[j].Values, evaluate, calc_grad, mLocalSearchTerminationCondition, constraints);
//double improvement = fx - s.Cost;
improvements[j] = s;
}
for (int j = 0; j < improvements.Length; ++j)
{
if (ref_set.ContainsItem(improvements[j]))
{
improvements[j].SetIsNew(false);
}
else
{
improvements[j].SetIsNew(true);
ref_set = ref_set.OrderBy(s => s.Cost).ToArray();
if (ref_set[mReferenceSetSize - 1].Cost > improvements[j].Cost)
{
ref_set[mReferenceSetSize - 1] = improvements[j];
is_improved = true;
}
}
}
}
return(is_improved);
}
public virtual double[] Solve()
{
int n = A[0].Length;
Random random = new Random();
double[] x_0 = new double[n];
for (int i = 0; i < n; ++i)
{
x_0[i] = random.NextDouble();
}
ContinuousSolution s = solver.Minimize(x_0, EvaluateCost, EvaluateGradient, ShouldTerminate);
mX = s.Values;
UpdateStatistics();
return(X);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
double fx_0 = evaluate(x_0, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x_0, fx_0);
if (mSearchSpaceSize == -1)
{
mSearchSpaceSize = x_0.Length;
}
while (!should_terminate(improvement, iteration))
{
double[] best_x_in_neighborhood = null;
double best_x_in_neighborhood_fx = double.MaxValue;
for (int i = 0; i < mSearchSpaceSize; ++i)
{
double[] x_pi = CreateRandomSolution(mSolutionGenerator, i, constraints);
double fx_pi = evaluate(x_pi, mLowerBounds, mUpperBounds, constraints);
if (fx_pi < best_x_in_neighborhood_fx)
{
best_x_in_neighborhood = x_pi;
best_x_in_neighborhood_fx = fx_pi;
}
}
if (best_x_in_neighborhood != null)
{
if (best_solution.TryUpdateSolution(best_x_in_neighborhood, best_x_in_neighborhood_fx, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
}
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
public void Reduce(List <ContinuousSolution> solutions, double sigma)
{
int solution_count = solutions.Count;
ContinuousSolution best_solution = solutions[0];
double[] x_0 = best_solution.Values;
int dimension = x_0.Length;
for (int i = 1; i < solution_count; ++i)
{
ContinuousSolution s = solutions[i];
double[] x = s.Values;
for (int d = 0; d < dimension; ++d)
{
x[d] = x_0[d] + sigma * (x[d] - x_0[d]);
}
s.Values = x;
}
}
protected ContinuousSolution[] SelectParents(ContinuousSolution[] pop, ContinuousSolution p0)
{
ContinuousSolution p1, p2, p3;
do
{
p1 = pop[RandomEngine.NextInt(pop.Length)];
} while (p1 == p0);
do
{
p2 = pop[RandomEngine.NextInt(pop.Length)];
} while (p2 == p1 || p2 == p0);
do
{
p3 = pop[RandomEngine.NextInt(pop.Length)];
} while (p3 == p2 || p3 == p1 || p3 == p0);
return(new ContinuousSolution[] { p1, p2, p3 });
}
protected List <ContinuousSolution[]> SelectSubsets(ContinuousSolution[] ref_set)
{
List <ContinuousSolution> additions = new List <ContinuousSolution>();
List <ContinuousSolution> remainders = new List <ContinuousSolution>();
for (int i = 0; i < mReferenceSetSize; ++i)
{
if (ref_set[i].IsNew())
{
additions.Add(ref_set[i]);
}
else
{
remainders.Add(ref_set[i]);
}
}
if (remainders.Count == 0)
{
foreach (ContinuousSolution s in additions)
{
remainders.Add(s);
}
}
List <ContinuousSolution[]> subsets = new List <ContinuousSolution[]>();
for (int i = 0; i < additions.Count; ++i)
{
ContinuousSolution addition = additions[i];
for (int j = 0; j < remainders.Count; ++j)
{
ContinuousSolution remainder = remainders[j];
if (addition != remainder)
{
subsets.Add(new ContinuousSolution[] { remainder, addition });
}
}
}
return(subsets);
}
public override ContinuousSolution Minimize(CostEvaluationMethod evaluate, GradientEvaluationMethod calc_grad, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
mLocalSearch.LowerBounds = mLowerBounds;
mLocalSearch.UpperBounds = mUpperBounds;
List <ContinuousSolution> diverse_set = ConstructDiverseSet(evaluate, calc_grad, constraints);
diverse_set = diverse_set.OrderBy(s => s.Cost).ToList();
ContinuousSolution[] ref_set = Diversify(diverse_set);
foreach (ContinuousSolution s in ref_set)
{
s.SetIsNew(true);
}
ContinuousSolution best_solution = ref_set[0].Clone() as ContinuousSolution;
while (!should_terminate(improvement, iteration))
{
bool was_changed = ExploreSubsets(ref ref_set, evaluate, calc_grad, constraints);
if (!was_changed)
{
break;
}
if (best_solution.TryUpdateSolution(ref_set[0].Values, ref_set[0].Cost, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
if (mLocalSearch == null)
{
throw new ArgumentNullException();
}
mLocalSearch.LowerBounds = mLowerBounds;
mLocalSearch.UpperBounds = mUpperBounds;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x, fx);
while (!should_terminate(improvement, iteration))
{
int r_index = (int)(RandomEngine.NextDouble() * x.Length);
double[] x_pi = GetNeighbor(x, r_index, constraints);
double fx_pi = evaluate(x_pi, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution x_pi_refined = mLocalSearch.Minimize(x_pi, evaluate, calc_gradient, mLocalSearchTerminationCondition, constraints);
if (best_solution.TryUpdateSolution(x_pi_refined.Values, x_pi_refined.Cost, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
x = best_solution.Values;
fx = best_solution.Cost;
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
public void Minimize(double x_0, CostFunction f, int max_iterations)
{
double x = x_0;
double fx = f.Evaluate(x);
double newtonX = 0;
double denominator = 0;
ContinuousSolution best_solution = new ContinuousSolution(new double[] { x }, fx);
double?improvement = null;
for (int k = 0; k < max_iterations; ++k)
{
f.CalcGradient(x, out denominator);
if (Math.Abs(denominator) < ZERO)
{
break;
}
newtonX = x - fx / denominator;
if (Math.Abs(newtonX - x) < SIGMA)
{
break;
}
x = newtonX;
fx = f.Evaluate(x);
if (best_solution.TryUpdateSolution(new double[] { x }, fx, out improvement))
{
OnSolutionUpdated(best_solution, k);
}
OnStepped(new ContinuousSolution(new double[] { x }, fx), k);
}
}
protected ContinuousSolution Reproduce(ContinuousSolution[] pop, ContinuousSolution p0, double[] lower_bounds, double[] upper_bounds)
{
ContinuousSolution[] parents = SelectParents(pop, p0);
ContinuousSolution p1 = parents[0];
ContinuousSolution p2 = parents[1];
ContinuousSolution p3 = parents[2];
int cut_point = RandomEngine.NextInt(mDimension);
ContinuousSolution child = new ContinuousSolution(mDimension);
for (int i = 0; i < mDimension; ++i)
{
child[i] = p0[i];
if (i == cut_point || RandomEngine.NextDouble() < mCrossoverRate)
{
child[i] = p1[i] + mWeightingFactor * (p2[i] - p3[i]);
child[i] = System.Math.Max(lower_bounds[i], child[i]);
child[i] = System.Math.Min(upper_bounds[i], child[i]);
}
}
return(child);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
int dimension = x_0.Length;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
double[] Vfx = new double[dimension];
ContinuousSolution best_solution = new ContinuousSolution(x, fx);
double?improvement = null;
int iteration = 0;
while (!should_terminate(improvement, iteration))
{
calc_gradient(x, Vfx, mLowerBounds, mUpperBounds, constraints);
for (int d = 0; d < dimension; ++d)
{
x[d] = x[d] - mAlpha * Vfx[d];
}
fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
if (best_solution.TryUpdateSolution(x, fx, out improvement))
{
//Console.WriteLine("called");
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(new ContinuousSolution(x, fx), iteration);
iteration++;
}
return(best_solution);
}