public IteratedLocalSearch(double[] masks, SingleTrajectoryContinuousSolver local_search, TerminationEvaluationMethod local_search_termination_condition, CreateRandomNeighborhoodMethod generator = null)
{
mLocalSearch = local_search;
mLocalSearchTerminationCondition = local_search_termination_condition;
mMasks = (double[])masks.Clone();
mNeighborSolutionProvider = generator;
if (mNeighborSolutionProvider == null)
{
mNeighborSolutionProvider = (x, index, constraints) =>
{
double[] lower_bounds = null;
double[] upper_bounds = null;
if (mLowerBounds == null || mUpperBounds == null)
{
Tuple <double[], double[]> bounds = null;
if (constraints is Tuple <double[], double[]> )
{
bounds = constraints as Tuple <double[], double[]>;
}
else
{
throw new InvalidCastException();
}
lower_bounds = bounds.Item1;
upper_bounds = bounds.Item2;
}
else
{
lower_bounds = mLowerBounds;
upper_bounds = mUpperBounds;
}
if (lower_bounds.Length < x.Length)
{
throw new ArgumentOutOfRangeException();
}
if (upper_bounds.Length < x.Length)
{
throw new ArgumentOutOfRangeException();
}
double[] x_p = (double[])x.Clone();
for (int i = 0; i < mMasks.Length; ++i)
{
int mindex = (index + i) % x_p.Length;
double new_val = mMasks[i] > 0 ? x[(index + i)] + RandomEngine.Gauss(0, mMasks[i]) : x[(index + i) % x.Length];
new_val = System.Math.Max(lower_bounds[mindex], new_val);
new_val = System.Math.Min(upper_bounds[mindex], new_val);
x_p[mindex] = new_val;
}
return(x_p);
};
}
}
public ScatterSearch(GenerateSolutionMethod solution_generator, SingleTrajectoryContinuousSolver local_search, TerminationEvaluationMethod local_search_termination_condition)
{
mSolutionGenerator = solution_generator;
mLocalSearch = local_search;
mLocalSearchTerminationCondition = local_search_termination_condition;
if (mSolutionGenerator == null || mLocalSearch == null || mLocalSearchTerminationCondition == null)
{
throw new NullReferenceException();
}
}
public GRASP(int[] masks, int dimension, TerminationEvaluationMethod local_search_should_terminate, GreedyConstructSolutionMethod solution_constructor)
{
mDimension = dimension;
mLocalSearchShouldTerminate = local_search_should_terminate;
mMasks = (int[])masks.Clone();
mGreedySolutionConstructor = solution_constructor;
if (mGreedySolutionConstructor == null)
{
mGreedySolutionConstructor = (evaluate, constraints, problem_size) =>
{
double[] solution = new double[problem_size];
for (int i = 0; i < solution.Length; ++i)
{
solution[i] = -1;
}
for (int i = 0; i < problem_size; ++i)
{
int best_feature_value = -1;
double min_cost = double.MaxValue;
for (int bit_value = 0; bit_value < 2; ++bit_value)
{
solution[i] = bit_value;
double cost = evaluate(solution, mLowerBounds, mUpperBounds, constraints); //evaluate partial solution
if (min_cost > cost)
{
min_cost = cost;
best_feature_value = bit_value;
}
}
}
return(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;
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 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);
}
public override ContinuousSolution Minimize(CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
ContinuousSolution[] pop = new ContinuousSolution[mPopSize];
ContinuousSolution[] children = new ContinuousSolution[mPopSize];
double[] lower_bounds = null;
double[] upper_bounds = null;
if (mLowerBounds == null || mUpperBounds == null)
{
if (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 < mDimension)
{
throw new IndexOutOfRangeException();
}
if (upper_bounds.Length < mDimension)
{
throw new IndexOutOfRangeException();
}
double[] best_x0 = null;
double best_fx0 = double.MaxValue;
for (int i = 0; i < mPopSize; ++i)
{
double[] x = mSolutionGenerator(mDimension, constraints);
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution s = new ContinuousSolution(x, fx);
pop[i] = s;
if (fx < best_fx0)
{
best_fx0 = fx;
best_x0 = x;
}
}
pop = pop.OrderBy(s => s.Cost).ToArray();
ContinuousSolution best_solution = new ContinuousSolution(best_x0, best_fx0);
while (!should_terminate(improvement, iteration))
{
for (int i = 0; i < mPopSize; ++i)
{
ContinuousSolution child = Reproduce(pop, pop[i], lower_bounds, upper_bounds);
child.Cost = evaluate(child.Values, mLowerBounds, mUpperBounds, constraints);
children[i] = child;
}
for (int i = 0; i < mPopSize; ++i)
{
if (pop[i].Cost > children[i].Cost)
{
pop[i] = children[i];
}
}
pop = pop.OrderBy(s => s.Cost).ToArray();
if (best_solution.TryUpdateSolution(pop[0].Values, pop[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 override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x, fx);
double current_step_size = 0;
double step_size_large = 0;
int no_change_count = 0;
while (!should_terminate(improvement, iteration))
{
double[] x1 = TakeStep(mSolutionGenerator, x, current_step_size, constraints);
double fx1 = evaluate(x1, mLowerBounds, mUpperBounds, constraints);
step_size_large = CalcStepSizeLarge(current_step_size, iteration);
double[] x2 = TakeStep(mSolutionGenerator, x, current_step_size, constraints);
double fx2 = evaluate(x1, mLowerBounds, mUpperBounds, constraints);
if (fx1 < fx || fx2 < fx)
{
if (fx1 < fx2)
{
x = x1;
fx = fx1;
current_step_size = step_size_large;
}
else
{
x = x2;
fx = fx2;
}
no_change_count = 0;
if (best_solution.TryUpdateSolution(x, fx, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
}
else
{
no_change_count++;
if (no_change_count > mMaxNoChangeIterationCount)
{
no_change_count = 0;
current_step_size = current_step_size / mStepSizeFactor_Small;
}
}
OnStepped(best_solution, iteration);
iteration++;
}
return(best_solution);
}
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 override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
ContinuousSolution best_solution = new ContinuousSolution();
int dimension = x_0.Length;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
double?improvement = null;
int iteration = 0;
while (!should_terminate(improvement, iteration))
{
double[] fx_candidate = new double[dimension];
double[] h = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
x[d] += mDeltaX;
fx_candidate[d] = evaluate(x, mLowerBounds, mUpperBounds, constraints);
h[d] = fx - fx_candidate[d];
x[d] -= mDeltaX;
}
int max_d = -1;
double h_max = 0;
for (int d = 0; d < dimension; ++d)
{
if (h_max < h[d])
{
h_max = h[d];
max_d = d;
}
}
if (max_d != -1)
{
fx = fx_candidate[max_d];
x[max_d] += mDeltaX;
}
else
{
if (mDeltaX > ZERO)
{
mDeltaX *= (-BETA);
}
else
{
break;
}
}
if (best_solution.TryUpdateSolution(x, fx, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(new ContinuousSolution(x, fx), iteration);
iteration++;
}
return(best_solution);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
throw new NotImplementedException();
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
ContinuousSolution best_solution = new ContinuousSolution();
int dimension = x_0.Length;
int vertex_count = dimension + 1;
double[] x = (double[])x_0.Clone();
List <ContinuousSolution> solutions = new List <ContinuousSolution>();
for (int i = 0; i < vertex_count; ++i)
{
double[] temp = (double[])x_0.Clone();
if (i == vertex_count - 1)
{
temp[0] -= 1.0;
}
else
{
temp[i] += 1.0;
}
solutions.Add(new ContinuousSolution(temp, evaluate(temp, mLowerBounds, mUpperBounds, constraints)));
}
ContinuousSolution centroid = new ContinuousSolution();
double?improvement = null;
int iteration = 0;
while (!should_terminate(improvement, iteration))
{
Sort(solutions);
CalculateCentroid(solutions, centroid);
double[] x_r;
Reflect(solutions, centroid, mAlpha, out x_r);
double fx_r = evaluate(x_r, mLowerBounds, mUpperBounds, constraints);
if (fx_r >= solutions[0].Cost && fx_r < solutions[solutions.Count - 2].Cost)
{
ContinuousSolution last_solution = solutions[solutions.Count - 1];
last_solution.Values = x_r;
last_solution.Cost = fx_r;
}
else if (fx_r < solutions[0].Cost)
{
double[] x_e;
Expand(solutions, centroid, mGamma, out x_e);
double fx_e = evaluate(x_e, mLowerBounds, mUpperBounds, constraints);
if (fx_e < fx_r)
{
ContinuousSolution last_solution = solutions[solutions.Count - 1];
last_solution.Values = x_e;
last_solution.Cost = fx_e;
}
else
{
ContinuousSolution last_solution = solutions[solutions.Count - 1];
last_solution.Values = x_r;
last_solution.Cost = fx_r;
}
}
else
{
double[] x_c;
Contract(solutions, centroid, mRho, out x_c);
double fx_c = evaluate(x_c, mLowerBounds, mUpperBounds, constraints);
if (fx_c < solutions[solutions.Count - 1].Cost)
{
ContinuousSolution last_solution = solutions[solutions.Count - 1];
last_solution.Values = x_c;
last_solution.Cost = fx_c;
}
else
{
Reduce(solutions, mSigma);
int solution_count = solutions.Count;
for (int i = 1; i < solution_count; ++i)
{
ContinuousSolution s = solutions[i];
s.Cost = evaluate(s.Values, mLowerBounds, mUpperBounds, constraints);
}
}
}
if (best_solution.TryUpdateSolution(solutions[0].Values, solutions[0].Cost, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(solutions[0], iteration);
iteration++;
}
return(best_solution);
}
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(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 override ContinuousSolution Minimize(CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
double?improvement = null;
int iteration = 0;
ContinuousSolution[] pop = new ContinuousSolution[mPopSize];
double[] lower_bounds = null;
double[] upper_bounds = null;
if (mLowerBounds == null || mUpperBounds == null)
{
if (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 < mDimension)
{
throw new IndexOutOfRangeException();
}
if (upper_bounds.Length < mDimension)
{
throw new IndexOutOfRangeException();
}
double[,] init_strategy_bounds = new double[mDimension, 2];
for (int j = 0; j < mDimension; ++j)
{
init_strategy_bounds[j, 0] = 0;
init_strategy_bounds[j, 1] = (upper_bounds[j] - lower_bounds[j]) * 0.05;
}
ContinuousSolution best_solution = null;
for (int i = 0; i < mPopSize; ++i)
{
double[] x = mSolutionGenerator(mDimension, constraints);
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution s = new ContinuousSolution(x, fx);
double[] strategy = new double[mDimension];
for (int j = 0; j < mDimension; ++j)
{
strategy[j] = init_strategy_bounds[j, 0] + (init_strategy_bounds[j, 1] - init_strategy_bounds[j, 0]) * RandomEngine.NextDouble();
}
s.SetMutationStrategy(strategy);
pop[i] = s;
}
pop = pop.OrderBy(s => s.Cost).ToArray();
best_solution = pop[0].Clone() as ContinuousSolution;
ContinuousSolution[] children = new ContinuousSolution[mPopSize];
ContinuousSolution[] generation = new ContinuousSolution[mPopSize * 2];
while (!should_terminate(improvement, iteration))
{
for (int i = 0; i < mPopSize; ++i)
{
children[i] = Mutate(pop[i], lower_bounds, upper_bounds, constraints);
children[i].Cost = evaluate(children[i].Values, mLowerBounds, mUpperBounds, constraints);
}
children = children.OrderBy(s => s.Cost).ToArray();
if (best_solution.TryUpdateSolution(children[0].Values, children[0].Cost, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
for (int i = 0; i < mPopSize; ++i)
{
generation[i] = pop[i];
}
for (int i = 0; i < mPopSize; ++i)
{
generation[i + mPopSize] = children[i];
}
for (int i = 0; i < generation.Length; ++i)
{
int wins = 0;
ContinuousSolution si = generation[i];
for (int j = 0; j < mBoutSize; ++j)
{
ContinuousSolution sj = generation[RandomEngine.NextInt(generation.Length)];
if (si.Cost < sj.Cost)
{
wins++;
}
}
si.SetWins(wins);
}
generation = generation.OrderByDescending(s => s.GetWins()).ToArray();
for (int i = 0; i < mPopSize; ++i)
{
pop[i] = generation[i];
}
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)
{
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);
}
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[] x_prev = (double[])x.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
ContinuousSolution best_solution = new ContinuousSolution(x, fx);
double[] Vfx = new double[dimension];
double[] Vfx_prev = new double[dimension];
double[] y = new double[dimension];
double[] p = new double[dimension];
double[] s = new double[dimension];
double[] x_out;
double fx_out = 0;
double alpha = 1;
calc_gradient(x, Vfx, mLowerBounds, mUpperBounds, constraints);
Matrix <double> B_matrix = SparseMatrix.Identity(dimension);
ContinuousSolution solution = new ContinuousSolution(x, fx);
int iteration = 0;
double?improvement = null;
while (!should_terminate(improvement, iteration))
{
Matrix <double> Vfx_matrix = new SparseMatrix(dimension, 1, 0);
for (int d = 0; d < dimension; ++d)
{
Vfx_matrix[d, 0] = -Vfx[d];
}
Matrix <double> p_matrix = B_matrix.Inverse().Multiply(Vfx_matrix);
for (int d = 0; d < dimension; ++d)
{
p[d] = p_matrix[d, 0];
}
fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
//Console.WriteLine("before");
SingleTrajectoryContinuousSolver.LineSearch(x, fx, p, out x_out, out fx_out, out alpha, evaluate, calc_gradient, mLowerBounds, mUpperBounds, constraints);
//Console.WriteLine("after");
if (best_solution.TryUpdateSolution(x, fx, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
for (int d = 0; d < dimension; ++d)
{
s[d] = alpha * p[d];
}
Matrix <double> s_matrix = new SparseMatrix(dimension, 1, s);
Matrix <double> s_matrix_t = s_matrix.Transpose();
for (int d = 0; d < dimension; ++d)
{
x_prev[d] = x[d];
}
for (int d = 0; d < dimension; ++d)
{
Vfx_prev[d] = Vfx[d];
}
for (int d = 0; d < dimension; ++d)
{
x[d] += alpha * p[d];
}
calc_gradient(x, Vfx, mLowerBounds, mUpperBounds, constraints);
for (int d = 0; d < dimension; ++d)
{
y[d] = Vfx[d] - Vfx_prev[d];
}
Matrix <double> y_matrix = new SparseMatrix(dimension, 1, y);
Matrix <double> y_matrix_t = y_matrix.Transpose();
Matrix <double> yts_matrix = y_matrix_t.Multiply(s_matrix);
double yts = yts_matrix[0, 0];
Matrix <double> sBs_matrix = s_matrix_t.Multiply(B_matrix).Multiply(s_matrix);
double sBs = sBs_matrix[0, 0];
B_matrix = B_matrix + y_matrix.Multiply(y_matrix_t).Divide(yts) - B_matrix.Multiply(s_matrix).Multiply(s_matrix_t).Multiply(B_matrix).Divide(sBs);
OnStepped(new ContinuousSolution(x, fx), iteration);
iteration++;
}
return(best_solution);
}
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
ContinuousSolution best_solution = new ContinuousSolution();
int dimension = x_0.Length;
double[] x = (double[])x_0.Clone();
double fx = evaluate(x, mLowerBounds, mUpperBounds, constraints);
double[] Vfx = new double[dimension];
calc_gradient(x, Vfx, mLowerBounds, mUpperBounds, constraints);
double[] deltaX = new double[dimension];
double[] deltaX_prev = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
deltaX[d] = -Vfx[d];
}
double alpha;
double[] x_next;
double fx_next;
LineSearch(x, fx, deltaX, out x_next, out fx_next, out alpha, evaluate, calc_gradient, mLowerBounds, mUpperBounds, constraints);
double beta = 0;
double[] s = new double[dimension];
for (int d = 0; d < dimension; ++d)
{
s[d] = deltaX[d];
}
double?improvement = null;
int iteration = 0;
while (!should_terminate(improvement, iteration))
{
for (int d = 0; d < dimension; ++d)
{
deltaX_prev[d] = deltaX[d];
x[d] = x_next[d];
}
calc_gradient(x, Vfx, mLowerBounds, mUpperBounds, constraints);
for (int d = 0; d < dimension; ++d)
{
deltaX[d] = -Vfx[d];
}
beta = ComputeBeta(deltaX, deltaX_prev, s);
for (int d = 0; d < dimension; ++d)
{
s[d] = deltaX[d] + beta * s[d];
}
LineSearch(x, fx, s, out x_next, out fx_next, out alpha, evaluate, calc_gradient, mLowerBounds, mUpperBounds, constraints);
if (best_solution.TryUpdateSolution(x_next, fx_next, out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
OnStepped(new ContinuousSolution(x_next, fx_next), iteration);
iteration++;
}
return(best_solution);
}
public abstract ContinuousSolution Minimize(CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null);
public override ContinuousSolution Minimize(double[] x_0, CostEvaluationMethod evaluate, GradientEvaluationMethod calc_gradient, TerminationEvaluationMethod should_terminate, object constraints = null)
{
int dimension = x_0.Length;
double[][] h = new double[dimension][];
double[] lambda = new double[dimension];
for (int i = 0; i < dimension; ++i)
{
h[i] = new double[dimension];
for (int j = 0; j < dimension; ++j)
{
h[i][j] = i == j ? 1.0 : 0.0;
}
}
double[][] x = new double[dimension + 1][];
double[] fx = new double[dimension + 1];
for (int i = 0; i <= dimension; ++i)
{
x[i] = new double[dimension];
}
for (int d = 0; d < dimension; ++d)
{
x[0][d] = x_0[d];
fx[0] = evaluate(x[0], mLowerBounds, mUpperBounds, constraints);
}
ContinuousSolution best_solution = new ContinuousSolution(x[0], fx[0]);
double?improvement = null;
int iteration = 0;
while (!should_terminate(improvement, iteration))
{
for (int i = 1; i <= dimension; ++i)
{
LineSearch(x[i - 1], fx[i - 1], h[i - 1], out x[i], out fx[i], out lambda[i - 1], evaluate, calc_gradient, mLowerBounds, mUpperBounds, constraints);
for (int j = 0; j < dimension - 1; ++j)
{
for (int d = 0; d < dimension; ++d)
{
h[j][d] = h[j + 1][d];
}
}
for (int d = 0; d < dimension; ++d)
{
h[dimension - 1][d] = x[dimension][d] - x[0][d];
}
LineSearch(x[0], fx[0], h[dimension - 1], out x[0], out fx[0], out lambda[dimension - 1], evaluate, calc_gradient, mLowerBounds, mUpperBounds, constraints);
if (best_solution.TryUpdateSolution(x[0], fx[0], out improvement))
{
OnSolutionUpdated(best_solution, iteration);
}
}
OnStepped(new ContinuousSolution(x[0], fx[0]), iteration);
iteration++;
}
return(best_solution);
}
