public Fraction SolveWithArtificialBasic()
{
// Получаем начальную декомпозицию для искусственного базиса
var dec = _augmentedConstraintList.DecompositionForArtificialBasic();
// Вычисляем целевую функцию по декомпозиции для искусственного базиса
var coeffs = new Fraction[dec.FreeVariables.Length + 1];
for (var i = 0; i < coeffs.Length; i++)
{
Fraction sum = 0;
for (var j = 0; j < dec.BasicVariables.Length; j++)
sum += dec.Coefficients[j, i];
coeffs[i] = -sum;
}
coeffs[coeffs.Length - 1] *= -1; // ! Потом будет поменян знак, т.к. в симлекс таблице мы записываем обратное значение
var objFunc = new ObjectiveFunction(coeffs);
// Приводим составленую симплекс таблицу к нужному виду
var artBasic = new SimplexTable(dec, objFunc, _loggerForArtBasic, _userChoiceForArtBasic, _isDecimalFractions)
.ToArtificialBasic();
_objectiveFunction.Substitution(artBasic);
return new SimplexTable(artBasic, _objectiveFunction, _logger, _userChoice, _isDecimalFractions)
.Calculate();
}
public void ObjectiveFunction_Substitution_Successful(
ObjectiveFunction objectiveFunction, Decomposition decomposition, ObjectiveFunction expected)
{
objectiveFunction.Substitution(decomposition);
CollectionAssert.AreEqual(expected.ToArray(), objectiveFunction.ToArray());
}
public void SimplexTable_Solve_Successful(
Decomposition decomposition, ObjectiveFunction objectiveFunction, Fraction expected)
{
var dut = new SimplexTable(decomposition, objectiveFunction, _logger).Calculate();
Assert.AreEqual(expected, dut);
}
public void SimplexMethod_Solve_Successful(
ObjectiveFunction objectiveFunction, Matrix matrix, int[] cornerPoint, Fraction expected)
{
var simplexMethodSolver = new SimplexMethodSolver(objectiveFunction, matrix, cornerPoint, _logger);
var actual = simplexMethodSolver.Solve();
Assert.AreEqual(expected, actual);
}
/// <summary>
/// Find vector x that minimizes the function f(x) using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm.
/// For more options and diagnostics consider to use <see cref="BfgsMinimizer"/> directly.
/// An alternative routine using conjugate gradients (CG) is available in <see cref="ConjugateGradientMinimizer"/>.
/// </summary>
public static Vector <double> OfFunctionGradient(Func <Vector <double>, double> function, Func <Vector <double>, Vector <double> > gradient, Vector <double> initialGuess, double gradientTolerance = 1e-5, double parameterTolerance = 1e-5, double functionProgressTolerance = 1e-5, int maxIterations = 1000)
{
var objective = ObjectiveFunction.Gradient(function, gradient);
var algorithm = new BfgsMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations);
var result = algorithm.FindMinimum(objective, initialGuess);
return(result.MinimizingPoint);
}
public PSO(ObjectiveFunction _objf, Response _res, Variable _var)
{
this.objf = _objf;
this.response = _res;
this.variables = _var;
Settings.aaa();
}
public override Harmony <StopTimeInfo> GenerateRandomHarmony()
{
var randomArguments = GetRandomArguments();
var objectiveValue = ObjectiveFunction.GetObjectiveValue(randomArguments);
return(new Harmony <StopTimeInfo>(objectiveValue, randomArguments));
}
/// <summary>
/// Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
/// For more options and diagnostics consider to use <see cref="BfgsBMinimizer"/> directly.
/// </summary>
public static Vector <double> OfFunctionGradientConstrained(Func <Vector <double>, Tuple <double, Vector <double> > > functionGradient, Vector <double> lowerBound, Vector <double> upperBound, Vector <double> initialGuess, double gradientTolerance = 1e-5, double parameterTolerance = 1e-5, double functionProgressTolerance = 1e-5, int maxIterations = 1000)
{
var objective = ObjectiveFunction.Gradient(functionGradient);
var algorithm = new BfgsBMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations);
var result = algorithm.FindMinimum(objective, lowerBound, upperBound, initialGuess);
return(result.MinimizingPoint);
}
public void FindMinimum_Rosenbrock_Overton()
{
var obj = ObjectiveFunction.Gradient(RosenbrockFunction.Value, RosenbrockFunction.Gradient);
var solver = new LimitedMemoryBfgsMinimizer(1e-5, 1e-5, 1e-5, 5, 100);
var result = solver.FindMinimum(obj, new DenseVector(new[] { -0.9, -0.5 }));
Assert.That(Math.Abs(result.MinimizingPoint[0] - RosenbrockFunction.Minimum[0]), Is.LessThan(1e-3));
Assert.That(Math.Abs(result.MinimizingPoint[1] - RosenbrockFunction.Minimum[1]), Is.LessThan(1e-3));
}
public void FindMinimum_BigRosenbrock_Hard()
{
var obj = ObjectiveFunction.Gradient(BigRosenbrockFunction.Value, BigRosenbrockFunction.Gradient);
var solver = new LimitedMemoryBfgsMinimizer(1e-5, 1e-5, 1e-5, 5, 1000);
var result = solver.FindMinimum(obj, new DenseVector(new[] { -1.2 * 100.0, 1.0 * 100.0 }));
Assert.That(Math.Abs(result.MinimizingPoint[0] - BigRosenbrockFunction.Minimum[0]), Is.LessThan(1e-3));
Assert.That(Math.Abs(result.MinimizingPoint[1] - BigRosenbrockFunction.Minimum[1]), Is.LessThan(1e-3));
}
public void Test_ExpansionWorks()
{
var algorithm = new GoldenSectionMinimizer(1e-5, 1000);
var f1 = new Func <double, double>(x => (x - 3) * (x - 3));
var obj = ObjectiveFunction.ScalarValue(f1);
var r1 = algorithm.FindMinimum(obj, -5, 5);
Assert.That(Math.Abs(r1.MinimizingPoint - 3.0), Is.LessThan(1e-4));
}
public void FindMinimum_Rosenbrock_Hard()
{
var obj = ObjectiveFunction.GradientHessian(point => Tuple.Create(RosenbrockFunction.Value(point), RosenbrockFunction.Gradient(point), RosenbrockFunction.Hessian(point)));
var solver = new NewtonMinimizer(1e-5, 1000);
var result = solver.FindMinimum(obj, new DenseVector(new[] { -1.2, 1.0 }));
Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
}
public SimplexTable(Decomposition decomposition, ObjectiveFunction objectiveFunction,
ILogger logger = null, UserChoice userChoice = null, bool isDecimalFractions = false)
{
_decomposition = decomposition;
_logger = logger;
_userChoice = userChoice;
_isDecimalFractions = isDecimalFractions;
_shortObjectiveFunction = ConvertToShortObjectiveFunction(decomposition, objectiveFunction);
}
public void FindMinimum_Rosenbrock_Hard()
{
var obj = ObjectiveFunction.Gradient(RosenbrockFunction.Value, RosenbrockFunction.Gradient);
var solver = new ConjugateGradientMinimizer(1e-5, 1000);
var result = solver.FindMinimum(obj, new DenseVector(new[] { -1.2, 1.0 }));
Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
}
public void FindMinimum_Rosenbrock_Easy()
{
var obj = ObjectiveFunction.GradientHessian(RosenbrockFunction.Value, RosenbrockFunction.Gradient, RosenbrockFunction.Hessian);
var solver = new NewtonMinimizer(1e-5, 1000);
var result = solver.FindMinimum(obj, new DenseVector(new[] { 1.2, 1.2 }));
Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(1e-3));
Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(1e-3));
}
/// <summary>
/// Find vector x that minimizes the function f(x), constrained within bounds, using the Broyden–Fletcher–Goldfarb–Shanno Bounded (BFGS-B) algorithm.
/// The missing gradient is evaluated numerically (forward difference).
/// For more options and diagnostics consider to use <see cref="BfgsBMinimizer"/> directly.
/// </summary>
public static Vector <double> OfFunctionConstrained(Func <Vector <double>, double> function, Vector <double> lowerBound, Vector <double> upperBound, Vector <double> initialGuess, double gradientTolerance = 1e-5, double parameterTolerance = 1e-5, double functionProgressTolerance = 1e-5, int maxIterations = 1000)
{
var objective = ObjectiveFunction.Value(function);
var objectiveWithGradient = new Optimization.ObjectiveFunctions.ForwardDifferenceGradientObjectiveFunction(objective, lowerBound, upperBound);
var algorithm = new BfgsBMinimizer(gradientTolerance, parameterTolerance, functionProgressTolerance, maxIterations);
var result = algorithm.FindMinimum(objectiveWithGradient, lowerBound, upperBound, initialGuess);
return(result.MinimizingPoint);
}
/*
* Input:
* Data file name
* Output:
* none
*
* Parse input data and loops over scans, performing scan matching
*/
public static void RunScanMatch(String inpF)
{
//Call Init
Init(inpF);
//Initialise Optimisation routine
var obj = ObjectiveFunction.GradientHessian(Cost);
var solver = new ConjugateGradientMinimizer(1e0, 30); //(1e-5, 100, false);
Vector <double> x_init = Vector <double> .Build.DenseOfArray(new double[] { 0.0, 0.0, 0.0 });
Vector <double> Xopt = Vector <double> .Build.DenseOfArray(new double[] { 0.0, 0.0, 0.0 });
//Initialise reference point cloud, expressed in map coordinates. rPNn
var rBNn = Vector <double> .Build.Dense(2);
rBNn[0] = Pose[0][0];
rBNn[1] = Pose[0][1];
var Rnb = SO2.EulerRotation(Pose[0][2]);
rEBb = GetPointCloudFromRange(Range[0]);
var rPNn_new = Matrix <double> .Build.DenseOfMatrix(rEBb);
for (int j = 0; j < rPNn_new.ColumnCount; j++)
{
rPNn_new.SetColumn(j, rBNn.Add(Rnb.Multiply(rEBb.Column(j))));
}
rPNn = rPNn_new;
//Loop through data, setting up and running optimisation routine each time.
for (int i = 1; i < Range.Count(); i++)
{
//Initialise independent point cloud, expressed in body coordinates.rPBb
rEBb = GetPointCloudFromRange(Range[i]);
//Set up initial conditions
x_init.SetValues(Pose[i]);
//Solve
var result = solver.FindMinimum(obj, x_init);
Xopt = result.MinimizingPoint;
rBNn = Vector <double> .Build.Dense(2);
rBNn[0] = Xopt[0];
rBNn[1] = Xopt[1];
Rnb = SO2.EulerRotation(Xopt[2]);
//Append to PointCloud
rPNn_new = Matrix <double> .Build.DenseOfMatrix(rEBb);
for (int j = 0; j < rPNn_new.ColumnCount; j++)
{
rPNn_new.SetColumn(j, rBNn.Add(Rnb.Multiply(rEBb.Column(j))));
}
rPNn = rPNn.Append(rPNn_new);
}
}
public void StandarizeConstraintAndFOTest()
{
PrimalSimplexService simplexService = new PrimalSimplexService(new VectorOperations(new VectorHelper()), new VectorHelper());
Constraint c1 = new Constraint(new Dictionary <string, double> {
{ "x1", 2 },
{ "x2", 2 },
{ "x3", 3 }
}, Simplex.Entities.EComparator.GreaterEqualThan, 15);
c1.Name = "r1";
Constraint c2 = new Constraint(new Dictionary <string, double> {
{ "x1", 2 },
{ "x2", 3 },
{ "x3", 1 }
}, Simplex.Entities.EComparator.LessEqualThan, 12);
c2.Name = "r2";
ObjectiveFunction fo = new ObjectiveFunction(new Dictionary <string, double>
{
{ "x1", 3 },
{ "x2", 2 },
{ "x3", 4 }
}, false);
fo.Name = "FO";
List <Constraint> constraints = new List <Constraint> {
c1, c2
};
List <Constraint> result = simplexService.StandarizeConstraint(constraints, out List <string> header).ToList();
fo = simplexService.StandarizeObjectiveFunction(fo, header);
Assert.Equal(0, result.Where(c => c.Name.Equals("r1")).FirstOrDefault().VectorBody["S1"]);
Assert.Equal(-1, result.Where(c => c.Name.Equals("r1")).FirstOrDefault().VectorBody["e1"]);
Assert.Equal(1, result.Where(c => c.Name.Equals("r1")).FirstOrDefault().VectorBody["A1"]);
Assert.Equal(1, result.Where(c => c.Name.Equals("r2")).FirstOrDefault().VectorBody["S1"]);
Assert.Equal(0, result.Where(c => c.Name.Equals("r2")).FirstOrDefault().VectorBody["e1"]);
Assert.Equal(0, result.Where(c => c.Name.Equals("r2")).FirstOrDefault().VectorBody["A1"]);
Assert.Equal(-3, fo.VectorBody["x1"]);
Assert.Equal(-2, fo.VectorBody["x2"]);
Assert.Equal(-4, fo.VectorBody["x3"]);
Assert.Equal(0, fo.VectorBody["S1"]);
Assert.Equal(0, fo.VectorBody["e1"]);
Assert.Equal(1, fo.VectorBody["A1"]);
Assert.True(header.Contains("S1") &&
header.Contains("e1") &&
header.Contains("A1"));
}
//added a listbox return to retrieve result during runtime at different stages (before optimisation/ after)
private ListBox ShowEvaluations()
{
ObjectiveFunction obj = new ObjectiveFunction();
List <Individual> inds = _ga.EvaluatePopulation(obj.Evaluate, _dbh);
listBox3.Items.Clear();
listBox4.Items.Clear();
dataGridView2.Rows.Clear();
dataGridView2.ColumnCount = SysConfig.chromeLength;
for (var i = 0; i < inds.Count; i++)
{
listBox3.Items.Add(inds[i].ObjectiveValue.ToString());
listBox4.Items.Add("Tour: " + inds[i].TourViolation.ToString() + " Continent: " + inds[i].ContinentViolation.ToString());
var row = new DataGridViewRow();
for (int j = 0; j < inds[i].Cities.Length; j++)
{
row.Cells.Add(new DataGridViewTextBoxCell()
{
Value = Convert.ToInt16(inds[i].Cities[j])
});
}
dataGridView2.Rows.Add(row);
}
var best = _ga.GetFittestIndividual();
listBox5.Items.Clear();
string cities = "";
foreach (var c in best.TravelOrder)
{
cities += c + ", ";
}
listBox5.Items.Add(cities);
listBox5.Items.Add("Fitness: " + best.ObjectiveValue);
listBox5.Items.Add("Tour Violation: " + best.TourViolation);
listBox5.Items.Add("Continent Violation: " + best.ContinentViolation);
listBox5.Items.Add("Num cities: " + best.CountriesVisited);
bool[] visited = new bool[SysConfig.chromeLength];
foreach (var i in inds)
{
for (var c = 0; c < i.Cities.Length; c++)
{
if (i.Cities[c])
{
visited[c] = true;
}
}
}
int citiesMapped = visited.Where(x => x == true).Count();
listBox8.Items.Clear();
listBox8.Items.Add(citiesMapped);
return(listBox5);
}
/// <summary>
/// Construtor padrão
/// </summary>
/// <param name="po">Funcao objetiva</param>
/// <param name="restrictions">Lista de restricoes</param>
public Simplex(ObjectiveFunction po, List <Restriction> restrictions)
{
objectiveFunction = po;
restrictionsList = restrictions;
table = new Tuple <double, double> [restrictions.Count( ) + 1, po.Z.Count( ) + 1];
columnPositions = new string[po.Z.Count( ) + 1];
linePositions = new string[restrictions.Count( ) + 1];
}
public BAforCOP(int numberOfPreys, double[] upBound, double[] lowBound, OptimizationType type, ObjectiveFunction objFun)
{
this.numberOfPreys = numberOfPreys;
lowerBound = lowBound;
upperBound = upBound;
optimizationTpe = type; //完全讓constructor決定就好
objFunction = objFun;
soFarTheBestSolution = new double[numberOfPreys];
}
public PSOforCOP(int numberOfVariables, double[] upBound, double[] lowBound, ObjectiveFunction objFun)
{
this.numberOfVariables = numberOfVariables;
LowerBound = lowBound;
UpperBound = upBound;
//this.optimizationTpe = type; //完全讓constructor決定就好
objFunction = objFun;
SoFarTheBestSolution = new double[numberOfVariables];
}
// try this for multiparameter optimization: https://numerics.mathdotnet.com/api/MathNet.Numerics.Optimization.TrustRegion/index.htm
// Golden Section Minimizer
public static Value Argmin(Value function, Value lowerBound, Value upperBound, Value tolerance, Netlist netlist, Style style, int s)
{
if (!(lowerBound is NumberValue) || !(upperBound is NumberValue))
{
throw new Error("argmin: expecting numbers for lower and upper bounds");
}
double lower = (lowerBound as NumberValue).value;
double upper = (upperBound as NumberValue).value;
if (lower > upper)
{
throw new Error("argmin: lower bound greater than upper bound");
}
if (!(function is FunctionValue))
{
throw new Error("argmin: expecting a function as first argument");
}
FunctionValue closure = function as FunctionValue;
if (closure.parameters.parameters.Count != 1)
{
throw new Error("argmin: initial values and function parameters have different lengths");
}
IScalarObjectiveFunction objectiveFunction = ObjectiveFunction.ScalarValue(
(double parameter) => {
List <Value> arguments = new List <Value>(); arguments.Add(new NumberValue(parameter));
bool autoContinue = netlist.autoContinue; netlist.autoContinue = true;
Value result = closure.ApplyReject(arguments, netlist, style, s);
if (result == null)
{
throw new Error("Objective function returned null");
}
netlist.autoContinue = autoContinue;
if (!(result is NumberValue))
{
throw new Error("Objective function must return a number, not: " + result.Format(style));
}
KGui.gui.GuiOutputAppendText("argmin: parameter=" + Style.FormatSequence(arguments, ", ", x => x.Format(style)) + " => cost=" + result.Format(style) + Environment.NewLine);
return((result as NumberValue).value);
});
try {
ScalarMinimizationResult result = GoldenSectionMinimizer.Minimum(objectiveFunction, lower, upper);
if (result.ReasonForExit == ExitCondition.Converged || result.ReasonForExit == ExitCondition.BoundTolerance)
{
KGui.gui.GuiOutputAppendText("argmin: converged with parameter=" + result.MinimizingPoint + " and reason '" + result.ReasonForExit + "'" + Environment.NewLine);
return(new NumberValue(result.MinimizingPoint));
}
else
{
throw new Error("reason '" + result.ReasonForExit.ToString() + "'");
}
} catch (Exception e) { throw new Error("argmin ended: " + ((e.InnerException == null) ? e.Message : e.InnerException.Message)); } // somehow we need to recatch the inner exception coming from CostAndGradient
}
public void PollutionWithWeights()
{
var obj = ObjectiveFunction.NonlinearModel(PollutionModel, PollutionX, PollutionY, PollutionW, accuracyOrder: 6);
var solver = new LevenbergMarquardtMinimizer();
var result = solver.FindMinimum(obj, PollutionStart);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(PollutionBest[i], result.MinimizingPoint[i], 4);
}
}
public MinimizationResult Train()
{
Vector <double> theta = Vector <double> .Build.Dense(X.ColumnCount);
LinearRegression lr = new LinearRegression(this.X, this.y, this.Lambda);
var obj = ObjectiveFunction.Gradient(lr.Cost, lr.Gradient);
var solver = new BfgsMinimizer(1e-5, 1e-5, 1e-5, 200);
MinimizationResult result = solver.FindMinimum(obj, theta);
return(result);
}
public void Thurber_LBfgs_Dif()
{
var obj = ObjectiveFunction.NonlinearFunction(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
var solver = new LimitedMemoryBfgsMinimizer(1e-10, 1e-10, 1e-10, 1000);
var result = solver.FindMinimum(obj, ThurberStart);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 6);
}
}
public void BoxBod_Newton_Der()
{
var obj = ObjectiveFunction.NonlinearFunction(BoxBodModel, BoxBodPrime, BoxBodX, BoxBodY);
var solver = new NewtonMinimizer(1e-10, 100);
var result = solver.FindMinimum(obj, BoxBodStart2);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(BoxBodPbest[i], result.MinimizingPoint[i], 6);
}
}
public void Rat43_LBfgs_Dif()
{
var obj = ObjectiveFunction.NonlinearFunction(Rat43Model, Rat43X, Rat43Y, accuracyOrder: 6);
var solver = new LimitedMemoryBfgsMinimizer(1e-10, 1e-10, 1e-10, 1000);
var result = solver.FindMinimum(obj, Rat43Start2);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(Rat43Pbest[i], result.MinimizingPoint[i], 2);
}
}
public void Rosenbrock_Bfgs_Dif()
{
var obj = ObjectiveFunction.NonlinearFunction(RosenbrockModel, RosenbrockX, RosenbrockY, accuracyOrder: 6);
var solver = new BfgsMinimizer(1e-8, 1e-8, 1e-8, 1000);
var result = solver.FindMinimum(obj, RosenbrockStart1);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(RosenbrockPbest[i], result.MinimizingPoint[i], 2);
}
}
public bool ComprobarSiFinalizaSimplex(ObjectiveFunction fo)
{
bool siFinaliza = false;
if (fo != null)
{
siFinaliza = !fo.CuerpoNum.Any(n => n < 0) && fo.TerminoIndependiente > 0;
}
return(siFinaliza);
}
public void NMS_FindMinimum_Rosenbrock_Easy()
{
var obj = ObjectiveFunction.Value(RosenbrockFunction.Value);
var solver = new NelderMeadSimplex(Tolerance * 0.1, maximumIterations: 1000);
var initialGuess = new DenseVector(new[] { 1.2, 1.2 });
var result = solver.FindMinimum(obj, initialGuess);
Assert.That(Math.Abs(result.MinimizingPoint[0] - 1.0), Is.LessThan(Tolerance));
Assert.That(Math.Abs(result.MinimizingPoint[1] - 1.0), Is.LessThan(Tolerance));
}
public void Thurber_LM_Dif()
{
var obj = ObjectiveFunction.NonlinearModel(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
var solver = new LevenbergMarquardtMinimizer();
var result = solver.FindMinimum(obj, ThurberStart);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 6);
AssertHelpers.AlmostEqualRelative(ThurberPstd[i], result.StandardErrors[i], 6);
}
}
public void BoxBod_TRNCG_Dif()
{
var obj = ObjectiveFunction.NonlinearModel(BoxBodModel, BoxBodX, BoxBodY, accuracyOrder: 6);
var solver = new TrustRegionNewtonCGMinimizer();
var result = solver.FindMinimum(obj, BoxBodStart2);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(BoxBodPbest[i], result.MinimizingPoint[i], 3);
AssertHelpers.AlmostEqualRelative(BoxBodPstd[i], result.StandardErrors[i], 3);
}
}
public void Rat43_TRDL_Dif()
{
var obj = ObjectiveFunction.NonlinearModel(Rat43Model, Rat43X, Rat43Y, accuracyOrder: 6);
var solver = new TrustRegionDogLegMinimizer();
var result = solver.FindMinimum(obj, Rat43Start2);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(Rat43Pbest[i], result.MinimizingPoint[i], 2);
AssertHelpers.AlmostEqualRelative(Rat43Pstd[i], result.StandardErrors[i], 2);
}
}
public void Thurber_TRNCG_Dif()
{
var obj = ObjectiveFunction.NonlinearModel(ThurberModel, ThurberX, ThurberY, accuracyOrder: 6);
var solver = new TrustRegionNewtonCGMinimizer();
var result = solver.FindMinimum(obj, ThurberStart, scales: ThurberScales);
for (int i = 0; i < result.MinimizingPoint.Count; i++)
{
AssertHelpers.AlmostEqualRelative(ThurberPbest[i], result.MinimizingPoint[i], 3);
AssertHelpers.AlmostEqualRelative(ThurberPstd[i], result.StandardErrors[i], 3);
}
}
public void Test1()
{
var dec = new Decomposition
{
BasicVariables = new[] {5, 6, 7},
FreeVariables = new[] {0, 1, 2, 3, 4},
Coefficients = new Fraction[,] {{-2, 4, 1, -1, 0, 3}, {4, -3, -1, 1, 1, 6}, {1, 4, 1, 0, 1, 15}}
};
var objFunc = new ObjectiveFunction(new Fraction[] {-3, -5, -1, 0, -2, 24});
var dut = new SimplexTable(dec, objFunc, new Logger());
var artBasic = dut.ToArtificialBasic();
}
public SimplexMethodSolver(ObjectiveFunction objectiveFunction, Matrix augmentedConstraintList,
IEnumerable<int> cornerPoint = null,
ILogger logger = null, ILogger loggerForArtBasic = null,
UserChoice userChoice = null, UserChoice userChoiceForArtBasic = null,
bool isDecimalFractions = false)
{
_objectiveFunction = objectiveFunction;
_augmentedConstraintList = augmentedConstraintList;
_logger = logger;
_loggerForArtBasic = loggerForArtBasic;
_userChoice = userChoice;
_userChoiceForArtBasic = userChoiceForArtBasic;
_isDecimalFractions = isDecimalFractions;
_cornerPoint = cornerPoint as IReadOnlyList<int>;
}
/// <summary>
/// Выделение из полной целевой функции нужных коэффициентов по декомпозиции
/// </summary>
private static ObjectiveFunction ConvertToShortObjectiveFunction(Decomposition decomposition, ObjectiveFunction objectiveFunction)
{
var shortObjectiveFunction = new List<Fraction>();
for (var i = 0; i < objectiveFunction.Count(); i++)
{
if (decomposition.FreeVariables.Contains(i))
shortObjectiveFunction.Add(objectiveFunction[i]);
}
shortObjectiveFunction.Add(objectiveFunction.Last());
return new ObjectiveFunction(shortObjectiveFunction);
}
public void SimplexMethod_Solve_Exception(
ObjectiveFunction objectiveFunction, Matrix matrix, int[] cornerPoint)
{
var simplexMethodSolver = new SimplexMethodSolver(objectiveFunction, matrix, cornerPoint, _logger);
Assert.Throws(typeof(Exception), () => simplexMethodSolver.Solve());
}
//===========================================================================//
// GFunctionWithShifts //
//===========================================================================//
public GFunctionWithShifts(CmsCoupon coupon, Handle<Quote> meanReversion) {
meanReversion_ = meanReversion;
calibratedShift_ = 0.03;
tmpRs_ = 10000000.0;
accuracy_ = 1.0e-14;
SwapIndex swapIndex = coupon.swapIndex();
VanillaSwap swap = swapIndex.underlyingSwap(coupon.fixingDate());
swapRateValue_ = swap.fairRate();
objectiveFunction_ = new ObjectiveFunction(this, swapRateValue_);
Schedule schedule = swap.fixedSchedule();
Handle<YieldTermStructure> rateCurve = swapIndex.forwardingTermStructure();
DayCounter dc = swapIndex.dayCounter();
swapStartTime_ = dc.yearFraction(rateCurve.link.referenceDate(), schedule.startDate());
discountAtStart_ = rateCurve.link.discount(schedule.startDate());
double paymentTime = dc.yearFraction(rateCurve.link.referenceDate(), coupon.date());
shapedPaymentTime_ = shapeOfShift(paymentTime);
List<CashFlow> fixedLeg = new List<CashFlow>(swap.fixedLeg());
int n = fixedLeg.Count;
shapedSwapPaymentTimes_ = new List<double>();
swapPaymentDiscounts_ = new List<double>();
accruals_ = new List<double>();
for (int i = 0; i < n; ++i) {
Coupon coupon1 = fixedLeg[i] as Coupon;
accruals_.Add(coupon1.accrualPeriod());
Date paymentDate = new Date(coupon1.date().serialNumber());
double swapPaymentTime = dc.yearFraction(rateCurve.link.referenceDate(), paymentDate);
shapedSwapPaymentTimes_.Add(shapeOfShift(swapPaymentTime));
swapPaymentDiscounts_.Add(rateCurve.link.discount(paymentDate));
}
discountRatio_ = swapPaymentDiscounts_.Last() / discountAtStart_;
}
public CMAESData(string distribution, string dimension, ObjectiveFunction objFun, bool dependentModel, DirectoryInfo data)
: base(distribution, dimension, DataSet.train, false, objFun == ObjectiveFunction.MinimumRho, data)
{
AlreadySavedPID = Generation;
FileInfo =
new FileInfo(String.Format(@"{0}\CMAES\weights\full.{1}.{2}.{3}.weights.{4}.csv", data.FullName,
Distribution, Dimension, objFun, dependentModel ? "timedependent" : "timeindependent"));
FileInfoResults =
new FileInfo(
String.Format(@"{0}\CMAES\results\output.{1}.{2}.{3}.weights.{4}.csv", data.FullName, Distribution,
Dimension, objFun, dependentModel ? "timedependent" : "timeindependent"));
N = NUM_FEATURES;
if (dependentModel)
N *= NumDimension;
StopEval = 50000; // 1e3*N^2;
if (FileInfoResults.Exists & !FileInfo.Exists)
{
ReadFileInfoResults();
AlreadySavedPID = Generation = _output.Count > 0 ? _output[_output.Count - 1].Generation : 0;
CountEval = StopEval; // use last results as finished run
if (OptimistationComplete)
Write();
}
if (FileInfo.Exists)
{
//throw new WarningException(String.Format("Optimistation already completed, see results in {0}", FileInfo.Name));
CountEval = StopEval;
return;
}
//Get the method information using the method info class
switch (objFun)
{
case ObjectiveFunction.MinimumMakespan:
_objFun = MinimumMakespan;
break;
case ObjectiveFunction.MinimumRho:
_optMakespans = OptimumArray();
_objFun = MinimumRho;
break;
}
#region -------------------- Initialization --------------------------------
xmean = LinearAlgebra.RandomValues(N); // objective variables initial point
sigma = 0.5;
_stopFitness = 1e-10;
#region Strategy parameter setting: Selection
lambda = 4 + (int) Math.Floor(3*Math.Log(N));
// ReSharper disable once LocalVariableHidesMember
double mu = lambda/2.0;
this.mu = (int) Math.Floor(mu);
_population = new Offspring[lambda];
weights = new double[this.mu];
for (int i = 0; i < this.mu; i++)
weights[i] = Math.Log(mu + 0.5) - Math.Log(i + 1);
// normalize recombination weights array
double tmpSum = weights.Sum();
for (int i = 0; i < weights.Length; i++)
weights[i] /= tmpSum;
mueff = Math.Pow(weights.Sum(), 2)/weights.Sum(w => Math.Pow(w, 2));
#endregion
#region Strategy parameter setting: Adaptation
cc = (4 + mueff/N)/(N + 4 + 2*mueff/N);
cs = (mueff + 2)/(N + mueff + 5);
c1 = 2/(Math.Pow(N + 1.3, 2) + mueff);
cmu = Math.Min(1 - c1, 2*(mueff - 2 + 1/mueff)/(Math.Pow(N + 2, 2) + mueff));
damps = 1 + 2*Math.Max(0, Math.Sqrt((mueff - 1)/(N + 1)) - 1) + cs;
#endregion
#region Initialize dynamic (internal) strategy parameters and constants
pc = LinearAlgebra.Zeros(N);
ps = LinearAlgebra.Zeros(N);
B = LinearAlgebra.Eye(N);
D = LinearAlgebra.Ones(N);
// C = B * diag(D.^2) * B';
C = LinearAlgebra.Multiply(B, LinearAlgebra.Diag(LinearAlgebra.Power(D, 2)), B, true);
invsqrtC = LinearAlgebra.InvertSqrtMatrix(B, D);
chiN = Math.Sqrt(N)*(1 - 1/(4.0*N) + 1/(21*Math.Pow(N, 2)));
#endregion
#endregion
}
public void SimplexTable_Solve_Unsolvable(Decomposition decomposition, ObjectiveFunction objectiveFunction)
{
Assert.Throws(typeof(Exception), () => new SimplexTable(decomposition, objectiveFunction, _logger).Calculate());
}
