/// <summary>
/// Attempts a calibration through <see cref="PelsserCappletOptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
MatrixMarketData normalVol = null;
if (data.Count > 1)
{
normalVol = (MatrixMarketData)data[1];
}
EstimationResult result;
if ((dataset.ZRMarket == null) || (dataset.CapVolatility == null))
{
result = new EstimationResult();
result.ErrorMessage = "Not enough data to calibrate.\n" +
"The estimator needs a ZRMarket and a CapVolatility " +
"defined inside InterestRateMarketData";
return(result);
}
// Backup the dates
DateTime effectiveDate = DateTime.Now.Date;
DateTime valuationDate = DateTime.Now.Date;
if (Document.ActiveDocument != null)
{
effectiveDate = Document.ActiveDocument.ContractDate;
valuationDate = Document.ActiveDocument.SimulationStartDate;
}
// Creates the Context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
Function zr = new PFunction(null);
zr.VarName = "zr";
// Load the zr.
double[,] zrvalue = (double[, ])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
prj.Symbols.Add(zr);
var bm = BlackModelFactory(zr);
if (bm is BachelierNormalModel)
{
(bm as BachelierNormalModel).Tenor = dataset.CapTenor;
}
double deltak = dataset.CapTenor;
Matrix capVol = normalVol != null ? normalVol.Values:dataset.CapVolatility;
Vector capMat = normalVol != null ? normalVol.RowValues: dataset.CapMaturity;
Vector capK = normalVol != null ? normalVol.ColumnValues: dataset.CapRate;
var preferences = settings as Fairmat.Calibration.CapVolatilityFiltering;
// Matrix calculated with black.
var blackCaps = new Matrix(capMat.Length, capK.Length);
for (int m = 0; m < capMat.Length; m++)
{
for (int s = 0; s < capK.Length; s++)
{
bool skip = false;
if (preferences != null)
{
if (capK[s] < preferences.MinCapRate || capK[s] > preferences.MaxCapRate ||
capMat[m] < preferences.MinCapMaturity || capMat[m] > preferences.MaxCapMaturity)
{
skip = true;
}
}
if (capVol[m, s] == 0 || skip)
{
blackCaps[m, s] = 0;
}
else
{
blackCaps[m, s] = bm.Cap(capK[s], capVol[m, s], deltak, capMat[m]);
}
}
}
if (blackCaps.IsNaN())
{
Console.WriteLine("Black caps matrix has non real values:");
Console.WriteLine(blackCaps);
throw new Exception("Cannot calculate Black caps");
}
// Maturity goes from 0 to the last item with step deltaK.
Vector maturity = new Vector((int)(1.0 + capMat[capMat.Length - 1] / deltak));
for (int l = 0; l < maturity.Length; l++)
{
maturity[l] = deltak * l;
}
Vector fwd = new Vector(maturity.Length - 1);
for (int i = 0; i < fwd.Length; i++)
{
fwd[i] = bm.Fk(maturity[i + 1], deltak);
}
// Creates a default Pelsser model.
Pelsser.SquaredGaussianModel model = new Pelsser.SquaredGaussianModel();
model.a1 = (ModelParameter)0.014;
model.sigma1 = (ModelParameter)0.001;
model.zr = (ModelParameter)"@zr";
StochasticProcessExtendible iex = new StochasticProcessExtendible(prj, model);
prj.Processes.AddProcess(iex);
prj.Parse();
DateTime t0 = DateTime.Now;
Caplet cp = new Caplet();
PelsserCappletOptimizationProblem problem = new PelsserCappletOptimizationProblem(prj, cp, maturity, fwd, capK, deltak, capMat, blackCaps);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 35;
o.TargetCost = 0.0025;
o.MaxIter = 10;
o.Verbosity = Math.Max(1, Engine.Verbose);
o.controller = controller;
// Parallel evaluation is not supported for this calibration.
o.Parallel = false;
o.Debug = true;
SolutionInfo solution = null;
Vector x0 = (Vector) new double[] { 0.1, 0.1 };
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
o.epsilon = 10e-7;
o.h = 10e-7;
o.MaxIter = 1000;
o.Debug = true;
o.Verbosity = Math.Max(1, Engine.Verbose);
if (solution != null)
{
solution = solver2.Minimize(problem, o, solution.x);
}
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
Console.WriteLine(solution);
string[] names = new string[] { "alpha1", "sigma1" };
result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
result.Objects = new object[1];
result.Objects[0] = solution.obj;
//result.Fit = solution.obj;//Uncomment in 1.6
// Restore the dates
if (Document.ActiveDocument != null)
{
Document.ActiveDocument.ContractDate = effectiveDate;
Document.ActiveDocument.SimulationStartDate = valuationDate;
}
return(result);
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(SteepestDescent obj) {
return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr;
}
static void Main(string[] args)
{
double oneOver2Pi = 1.0 / (1.0 * Math.Sqrt(2 * Math.PI));
Console.WriteLine(NormalCDFInverse(0.99));
Console.WriteLine(inv_cdf(0.99));
Console.WriteLine(InverseCDF.QNorm(0.99, 0, 1, true, false));
Console.WriteLine(NormalCDFInverse(0.5));
Console.WriteLine(inv_cdf(0.5));
Console.WriteLine(InverseCDF.QNorm(0.5, 0, 1, true, false));
Console.WriteLine(NormalCDFInverse(0.31));
Console.WriteLine(inv_cdf(0.31));
Console.WriteLine(InverseCDF.QNorm(0.31, 0, 1, true, false));
//x4−8x2 + 5
Func <double[], double> testFunc1 = (x) =>
{
return(Math.Pow(x[0], 4) - 8 * Math.Pow(x[0], 2) + 5);
};
Func <double[], double>[] dtestFunc1 = new Func <double[], double> [1];
dtestFunc1[0] = (x) =>
{
return(4 * Math.Pow(x[0], 3) - 16 * x[0]);
};
Func <double[], double> testConstr = (x) =>
{
return(5 - Math.Exp(x[0]) + 2.0 * Math.Pow(x[0] - 1, 2));
};
Func <double[], double> testv = (x) =>
{
double a = x[0];
return(0.0);
};
//Func<Variables, double> testFunc = (x) => {
// return Math.Pow(x.Vars[0], 4) - 3 * Math.Pow(x.Vars[0], 3) + 2;
//};
//Func<Variables, double> dTestfunc = (x) =>
//{
// return 4 * Math.Pow(x.Vars[0], 3) - 9 * Math.Pow(x.Vars[0], 2);
//};
//x4−3x3 + 2
//TestFunc();
Func <double[], double> bananaFunc = (x) =>
{
return(Math.Pow(1 - x[0], 2) + 100 * Math.Pow(x[1] - x[0] * x[0], 2));
};
Func <double[], double> powell = (x) =>
{
return(Math.Pow(x[0] + 10 * x[1], 2) +
5 * Math.Pow(x[2] - x[3], 2) +
Math.Pow(x[1] + 2 * x[2], 4) +
10 * Math.Pow(x[0] - x[3], 4));
};
OptVector[] ttt = new OptVector[3];
ttt[0] = new OptVector(new double[3] {
1, 2, 3
});
ttt[1] = new OptVector(new double[3] {
4, 5, 6
});
ttt[2] = new OptVector(new double[3] {
7, 8, 9
});
var tr = OptVector.Transpose(ttt);
//TestsSQP.Test0();
TestsSQP.Test1();
TestsSQP.Test2();
TestsSQP.Test3();
////TestsSQP.Test4();
////TestsSQP.Test5();
TestsSQP.Test6();
TestsSQP.Test7();
TestsSQP.Test8();
TestsSQP.Test9();
TestsSQP.Test10();
TestsSQP.Test11();
TestsSQP.Test12();
TestsSQP.Test13();
TestsSQP.Test14();
TestsSQP.Test15();
TestsSQP.Test16();
TestsSQP.Test17();
TestsSQP.Test18();
TestsSQP.Test19();
TestsSQP.Test20();
TestsSQP.Test21();
TestsSQP.Test22();
TestsSQP.Test23();
TestsSQP.Test24();
TestsSQP.Test25();
TestsSQP.Test26();
TestsSQP.Test27();
TestsSQP.Test28();
//TestCGMethod();
BFGS bfsg = new BFGS();
var result0 = bfsg.Solve(powell, new double[] { 3.0, -1.0, 0.0, 1.0 }, 10000);
Console.WriteLine("Min " + powell(result0));
NLCG gradient = new NLCG();
var result = gradient.Solve(powell, new double[] { 3.0, -1.0, 0.0, 1.0 }, 4000);
Console.WriteLine("Min " + powell(result));
SteepestDescent gradientSteep = new SteepestDescent();
var result1 = gradientSteep.Solve(powell, new double[] { 3.0, -1.0, 0.0, 1.0 }, 10000);
Console.WriteLine("Min " + powell(result1));
//Variables result = SteepestDescent(testFunc, dTestFunc, 2, 20);
for (int i = 0; i < result.Length; i++)
{
Console.WriteLine("result " + result[i]);
}
//Console.WriteLine("ver " + testFunc(result));
Console.ReadLine();
}
/// <summary>
/// Attempts a calibration through <see cref="SwaptionHW1OptimizationProblem"/>
/// using swaption matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
MatrixMarketData normalVol = null;
if (data.Count > 1)
{
normalVol = (MatrixMarketData)data[1];
}
PFunction zr = new PFunction(null);
// Loads the zero rate.
double[,] zrvalue = (double[, ])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
double deltak = dataset.SwaptionTenor;
Console.WriteLine("Swaption Tenor\t" + dataset.SwaptionTenor);
var swaptionsFiltering = settings as SwaptionsFiltering;
if (swaptionsFiltering == null)
{
swaptionsFiltering = new SwaptionsFiltering();//creates a default
}
//F stands for Full matrix
var optionMaturityF = normalVol != null ? normalVol.RowValues: dataset.OptionMaturity;
var swapDurationF = normalVol != null ? normalVol.ColumnValues: dataset.SwapDuration;
var swaptionsVolatilityF = normalVol != null ? normalVol.Values: dataset.SwaptionsVolatility;
int maturitiesCount = optionMaturityF.Count(x => x >= swaptionsFiltering.MinSwaptionMaturity && x <= swaptionsFiltering.MaxSwaptionMaturity);
int durationsCount = swapDurationF.Count(x => x >= swaptionsFiltering.MinSwapDuration && x <= swaptionsFiltering.MaxSwapDuration);
Console.WriteLine(string.Format("Calibrating on {0} swaptions prices [#maturiries x #durations]=[{1} x {2}]", maturitiesCount * durationsCount, maturitiesCount, durationsCount));
if (maturitiesCount * durationsCount == 0)
{
return(new EstimationResult("No swaptions satisfying criteria found, please relax filters"));
}
//reduced version
var swaptionsVolatility = new Matrix(maturitiesCount, durationsCount); // dataset.SwaptionsVolatility;
var optionMaturity = new Vector(maturitiesCount); // dataset.OptionMaturity;
var swapDuration = new Vector(durationsCount); // dataset.SwapDuration;
//Build filtered matrix and vectors
int fm = 0;
for (int m = 0; m < optionMaturityF.Length; m++)
{
int fd = 0;
if (optionMaturityF[m] >= swaptionsFiltering.MinSwaptionMaturity && optionMaturityF[m] <= swaptionsFiltering.MaxSwaptionMaturity)
{
for (int d = 0; d < swapDurationF.Length; d++)
{
if (swapDurationF[d] >= swaptionsFiltering.MinSwapDuration && swapDurationF[d] <= swaptionsFiltering.MaxSwapDuration)
{
swaptionsVolatility[fm, fd] = swaptionsVolatilityF[m, d];
swapDuration[fd] = swapDurationF[d];
fd++;
}
}
optionMaturity[fm] = optionMaturityF[m];
fm++;
}
}
var swbm = new SwaptionsBlackModel(zr, BlackModelFactory(zr));
Matrix fsr;
var blackSwaptionPrice = 1000.0 * swbm.SwaptionsSurfBM(optionMaturity, swapDuration, swaptionsVolatility, deltak, out fsr);
Console.WriteLine("Maturities\t" + optionMaturity);
Console.WriteLine("swapDuration\t" + swapDuration);
Console.WriteLine("SwaptionHWEstimator: Black model prices");
Console.WriteLine(blackSwaptionPrice);
SwaptionHW1 swhw1 = new SwaptionHW1(zr);
SwaptionHW1OptimizationProblem problem = new SwaptionHW1OptimizationProblem(swhw1, blackSwaptionPrice, optionMaturity, swapDuration, deltak);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 5;
o.Verbosity = 1;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.1, 0.1 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 1000;
// We can permit this, given it is fast.
o.accourate_numerical_derivatives = true;
if (solution != null)
{
solution = solver2.Minimize(problem, o, solution.x);
}
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
Console.WriteLine("SwaptionHWEstimator: hw model prices and error");
problem.Obj(solution.x, true);
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
double obj = problem.Obj(solution.x);
return(result);
}
/// <summary>
/// Attempts a calibration through <see cref="CapsHW1OptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
PFunction zr = new PFunction(null);
zr.VarName = "zr";
var preferences = settings as Fairmat.Calibration.CapVolatilityFiltering;
// Loads ZR
double[,] zrvalue = (double[,])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
BlackModel bm = new BlackModel(zr);
double deltak = dataset.CapTenor;
if (dataset.CapVolatility == null)
return new EstimationResult("Cap not available at requested date");
Matrix capVolatility = dataset.CapVolatility;
Vector capMaturity = dataset.CapMaturity;
Vector capRate = dataset.CapRate;
double a = 0.1;
double sigma = 0.1;
// Matrix calculated with Black.
Matrix blackCaps = new Matrix(capMaturity.Length, capRate.Length);
Matrix logic = new Matrix(capMaturity.Length, capRate.Length);
for (int m = 0; m < capMaturity.Length; m++)
{
for (int s = 0; s < capRate.Length; s++)
{
blackCaps[m, s] = bm.Cap(capRate[s], capVolatility[m, s], deltak, capMaturity[m]);
if (double.IsNaN(blackCaps[m, s]))
{
bm.Cap(capRate[s], capVolatility[m, s], deltak, capMaturity[m]);
throw new Exception("Malformed black caps");
}
if (blackCaps[m, s] == 0.0)
{
logic[m, s] = 0.0;
}
else
{
logic[m, s] = 1.0;
}
//filter
if (preferences != null)
{
if (capRate[s] < preferences.MinCapRate || capRate[s] > preferences.MaxCapRate ||
capMaturity[m]<preferences.MinCapMaturity|| capMaturity[m]>preferences.MaxCapMaturity)
{logic[m, s] = 0; blackCaps[m, s] = 0;}
}
}
}
DateTime t0 = DateTime.Now;
CapHW1 hw1Caps = new CapHW1(zr);
Matrix caps = hw1Caps.HWMatrixCaps(capMaturity, capRate, a, sigma, deltak);
for (int m = 0; m < capMaturity.Length; m++)
{
for (int s = 0; s < capRate.Length; s++)
{
caps[m, s] = logic[m, s] * caps[m, s];
}
}
CapsHW1OptimizationProblem problem = new CapsHW1OptimizationProblem(hw1Caps, blackCaps, capMaturity, capRate, deltak);
Vector provaparam = new Vector(2);
var solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 10;
o.Verbosity = 1;
o.Parallel = false;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.05, 0.01 });
o.controller = controller;
solution = solver.Minimize(problem, o, x0);
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 100;
solution = solver2.Minimize(problem, o, solution.x);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
//solution.x[0] *= 3;
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
return result;
}
void Visualize()
{
idx = 0;
vDataPoints = new List <GameObject>(); // vDataPoints is a list of GameOjbects (prefix 'v' is for visualized)
// mapping each type to a material index assuming number of unique types is less than number of materials
int materialIndex = 0;
Dictionary <string, int> typeToColor = new Dictionary <string, int>();
foreach (DataPoint dataPoint in dataPoints)
{
if (!typeToColor.ContainsKey(dataPoint.type))
{
typeToColor.Add(dataPoint.type, materialIndex);
materialIndex++;
}
}
/*
* order is a list of datapoints in the order of removal by steepest descent
* it's easier to imagine order as dataPoints but with a different ordering of elements
* this order of elements matters because it's the order of the traversal of the visualization method.
*
* TODO change from list to array because size is known beforehand
*/
List <DataPoint> order = new List <DataPoint>();
while (dataPoints.Any()) // FIXME SteepestDescent returns NaN when there are no unique coordinates in dataPoints
{
if (dataPoints.Count == 1)
{
order.Add(dataPoints.Last());
break;
}
double init_x = dataPoints[0].x, init_y = dataPoints[0].y; // initial point is first point
Vector2[] steps = SteepestDescent.Run(dataPoints, init_x, init_y, 0.03, 300);
int nearest = GetNearest(dataPoints, steps.Last());
order.Add(dataPoints[nearest]);
dataPoints.RemoveAt(nearest); // TODO optimize using dictionary or boolean visited array
}
// instantiate datapoints
foreach (DataPoint dataPoint in order)
{
double y = SteepestDescent.CauchyTotalPotential(order, dataPoint.x, dataPoint.y);
double x = dataPoint.x;
double z = dataPoint.y;
Vector3 position = new Vector3(XZ_Scale * (float)x, Y_Scale * (float)y, XZ_Scale * (float)z);
GameObject vPoint = Instantiate(rockPrefab, position, Quaternion.identity);
int colorIndex = typeToColor[dataPoint.type];
vPoint.GetComponent <MeshRenderer>().material = colors[colorIndex];
vDataPoints.Add(vPoint);
}
// instaniate trail
trail = Instantiate(trailPrefab, Vector3.zero, Quaternion.identity);
StartCoroutine(Animate());
}
/// <summary>
/// Attempts a calibration through <see cref="CapsHW1OptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
PFunction zr = new PFunction(null);
zr.VarName = "zr";
var preferences = settings as Fairmat.Calibration.CapVolatilityFiltering;
// Loads ZR
double[,] zrvalue = (double[, ])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
BlackModel bm = new BlackModel(zr);
double deltak = dataset.CapTenor;
if (dataset.CapVolatility == null)
{
return(new EstimationResult("Cap not available at requested date"));
}
Matrix capVolatility = dataset.CapVolatility;
Vector capMaturity = dataset.CapMaturity;
Vector capRate = dataset.CapRate;
double a = 0.1;
double sigma = 0.1;
// Matrix calculated with Black.
Matrix blackCaps = new Matrix(capMaturity.Length, capRate.Length);
Matrix logic = new Matrix(capMaturity.Length, capRate.Length);
for (int m = 0; m < capMaturity.Length; m++)
{
for (int s = 0; s < capRate.Length; s++)
{
blackCaps[m, s] = bm.Cap(capRate[s], capVolatility[m, s], deltak, capMaturity[m]);
if (double.IsNaN(blackCaps[m, s]))
{
bm.Cap(capRate[s], capVolatility[m, s], deltak, capMaturity[m]);
throw new Exception("Malformed black caps");
}
if (blackCaps[m, s] == 0.0)
{
logic[m, s] = 0.0;
}
else
{
logic[m, s] = 1.0;
}
//filter
if (preferences != null)
{
if (capRate[s] < preferences.MinCapRate || capRate[s] > preferences.MaxCapRate ||
capMaturity[m] < preferences.MinCapMaturity || capMaturity[m] > preferences.MaxCapMaturity)
{
logic[m, s] = 0; blackCaps[m, s] = 0;
}
}
}
}
DateTime t0 = DateTime.Now;
CapHW1 hw1Caps = new CapHW1(zr);
Matrix caps = hw1Caps.HWMatrixCaps(capMaturity, capRate, a, sigma, deltak);
for (int m = 0; m < capMaturity.Length; m++)
{
for (int s = 0; s < capRate.Length; s++)
{
caps[m, s] = logic[m, s] * caps[m, s];
}
}
CapsHW1OptimizationProblem problem = new CapsHW1OptimizationProblem(hw1Caps, blackCaps, capMaturity, capRate, deltak);
Vector provaparam = new Vector(2);
var solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 10;
o.Verbosity = 1;
o.Parallel = false;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.05, 0.01 });
o.controller = controller;
solution = solver.Minimize(problem, o, x0);
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 100;
solution = solver2.Minimize(problem, o, solution.x);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
//solution.x[0] *= 3;
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
return(result);
}
/// <summary>
/// Attempts to solve the Heston optimization problem using
/// <see cref="Heston.HestonOptimizationProblem"/>.
/// </summary>
/// <param name="marketData">Data to be used in order to perform the optimization.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">IController.</param>
/// <returns>The results of the optimization.</returns>
public EstimationResult Estimate(List <object> marketData, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
DateTime t0 = DateTime.Now;
var interestDataSet = (CurveMarketData)marketData[0];
CallPriceMarketData callDataSet = (CallPriceMarketData)marketData[1];
EquityCalibrationData equityCalData = new EquityCalibrationData(callDataSet, interestDataSet);
var spotPrice = (DVPLI.MarketDataTypes.Scalar)marketData[2];
Setup(equityCalData, settings);
var calSettings = settings as HestonCalibrationSettings;
// Creates the context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
// Optimization problem instance.
Vector matBound = new Vector(2);
Vector strikeBound = new Vector(2);
if (calSettings != null)
{
matBound[0] = calSettings.MinMaturity;
matBound[1] = calSettings.MaxMaturity;
strikeBound[0] = calSettings.MinStrike;
strikeBound[1] = calSettings.MaxStrike;
}
else
{
//use defaults
matBound[0] = 1.0 / 12; // .25;
matBound[1] = 6; // 10; //Up to 6Y maturities
strikeBound[0] = 0.4;
strikeBound[1] = 1.6;
}
Console.WriteLine(callDataSet);
/*
* //CBA TEST
* matBound[0] = 1;// .25;
* matBound[1] = 3.5;// 10; //Up to 6Y maturities
* strikeBound[0] = 0.5;// 0.5;
* strikeBound[1] = 2;//1.5;
*/
HestonCallOptimizationProblem problem = NewOptimizationProblem(equityCalData, matBound, strikeBound);
int totalOpts = problem.numCall + problem.numPut;
Console.WriteLine("Calibration based on " + totalOpts + " options. (" + problem.numCall + " call options and " + problem.numPut + " put options).");
IOptimizationAlgorithm solver = new QADE();
//IOptimizationAlgorithm solver = new MultiLevelSingleLinkage();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.controller = controller;
// If true the optimization algorithm will operate in parallel.
o.Parallel = Engine.MultiThread;
o.h = 10e-8;
o.epsilon = 10e-8;
SolutionInfo solution = null;
double minObj = double.MaxValue;
Vector minX = null;
int Z = 1;
//if (problem.GetType() == typeof(Heston.HestonCallSimulationOptimizationProblem))
// Z = 2;
for (int z = 0; z < Z; z++)
{
if (solver.GetType() == typeof(MultiLevelSingleLinkage))
{
o.NP = 50;
o.MaxIter = 25;
o.MaxGamma = 6;
}
else
{
o.NP = 60;
o.MaxIter = 35;
}
o.Verbosity = 1;
Vector x0 = null;// new Vector(new double[] { 0.5, 0.5, 0.8, -0.5, 0.05 });
// GA
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
{
return(null);
}
o.options = "qn";
o.MaxIter = 500;// 1000;
if (solution != null)
{
solution = solver2.Minimize(problem, o, solution.x);
}
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
{
return(null);
}
if (solution.obj < minObj)
{
minObj = solution.obj;
minX = solution.x.Clone();
}
}
solution.obj = minObj;
solution.x = minX;
//Displays pricing error structure
HestonCallOptimizationProblem.displayObjInfo = true;
problem.Obj(solution.x);
HestonCallOptimizationProblem.displayObjInfo = false;
Console.WriteLine("Calibration Time (s)\t" + (DateTime.Now - t0).TotalSeconds);
return(BuildEstimate(spotPrice, interestDataSet, callDataSet, equityCalData, solution));
}
/// <summary>
/// Attempts a calibration through <see cref="SwaptionHW1OptimizationProblem"/>
/// using swaption matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
PFunction zr = new PFunction(null);
// Loads the zero rate.
double[,] zrvalue = (double[,])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
double deltak = dataset.SwaptionTenor;
var swaptionsFiltering = settings as SwaptionsFiltering;
if (swaptionsFiltering == null)
swaptionsFiltering = new SwaptionsFiltering();//creates a default
int maturitiesCount = dataset.OptionMaturity.Count(x => x >= swaptionsFiltering.MinSwaptionMaturity && x <= swaptionsFiltering.MaxSwaptionMaturity);
int durationsCount = dataset.SwapDuration.Count(x => x >= swaptionsFiltering.MinSwapDuration && x <= swaptionsFiltering.MaxSwapDuration);
Console.WriteLine(string.Format("Calibrating on {0} swaptions prices [#maturiries x #durations]=[{1} x {2}]", maturitiesCount * durationsCount, maturitiesCount,durationsCount));
if (maturitiesCount * durationsCount == 0)
return new EstimationResult("No swaptions satisfying criteria found, please relax filters");
Matrix swaptionsVolatility = new Matrix(maturitiesCount, durationsCount);// dataset.SwaptionsVolatility;
Vector optionMaturity = new Vector(maturitiesCount);// dataset.OptionMaturity;
Vector swapDuration = new Vector(durationsCount);// dataset.SwapDuration;
//Build filtered matrix and vectors
int fm=0;
for (int m = 0; m < dataset.OptionMaturity.Length; m++)
{
int fd=0;
if (dataset.OptionMaturity[m] >= swaptionsFiltering.MinSwaptionMaturity && dataset.OptionMaturity[m] <= swaptionsFiltering.MaxSwaptionMaturity)
{
for (int d = 0; d < dataset.SwapDuration.Length; d++)
{
if (dataset.SwapDuration[d] >= swaptionsFiltering.MinSwapDuration && dataset.SwapDuration[d] <= swaptionsFiltering.MaxSwapDuration)
{
swaptionsVolatility[fm, fd] = dataset.SwaptionsVolatility[m, d];
swapDuration[fd] = dataset.SwapDuration[d];
fd++; }
}
optionMaturity[fm] = dataset.OptionMaturity[m];
fm++;
}
}
SwaptionsBlackModel swbm = new SwaptionsBlackModel(zr);
Matrix fsr;
var blackSwaptionPrice = 1000.0 * swbm.SwaptionsSurfBM(optionMaturity, swapDuration, swaptionsVolatility, deltak, out fsr);
Console.WriteLine("SwaptionHWEstimator: Black model prices");
Console.WriteLine(blackSwaptionPrice);
SwaptionHW1 swhw1 = new SwaptionHW1(zr);
SwaptionHW1OptimizationProblem problem = new SwaptionHW1OptimizationProblem(swhw1, blackSwaptionPrice, optionMaturity, swapDuration, deltak);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 20;
o.MaxIter = 5;
o.Verbosity = 1;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 0.1, 0.1 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
o.epsilon = 10e-8;
o.h = 10e-8;
o.MaxIter = 1000;
// We can permit this, given it is fast.
o.accourate_numerical_derivatives = true;
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
solution = solver2.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = new string[] { "Alpha", "Sigma" };
EstimationResult result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
double obj = problem.Obj(solution.x);
return result;
}
internal static global::System.Runtime.InteropServices.HandleRef getCPtr(SteepestDescent obj) {
return (obj == null) ? new global::System.Runtime.InteropServices.HandleRef(null, global::System.IntPtr.Zero) : obj.swigCPtr;
}
/// <summary>
/// Attempts to solve the Heston optimization problem using
/// <see cref="Heston.HestonOptimizationProblem"/>.
/// </summary>
/// <param name="marketData">Data to be used in order to perform the optimization.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">IController.</param>
/// <returns>The results of the optimization.</returns>
public EstimationResult Estimate(List<object> marketData, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
DateTime t0 = DateTime.Now;
var interestDataSet = (CurveMarketData)marketData[0];
CallPriceMarketData callDataSet = (CallPriceMarketData)marketData[1];
EquityCalibrationData equityCalData = new EquityCalibrationData(callDataSet, interestDataSet);
var spotPrice = (DVPLI.MarketDataTypes.Scalar)marketData[2];
Setup(equityCalData, settings);
var calSettings = settings as HestonCalibrationSettings;
// Creates the context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
// Optimization problem instance.
Vector matBound = new Vector(2);
Vector strikeBound = new Vector(2);
if (calSettings != null)
{
matBound[0] = calSettings.MinMaturity;
matBound[1] = calSettings.MaxMaturity;
strikeBound[0] = calSettings.MinStrike;
strikeBound[1] = calSettings.MaxStrike;
}
else
{
//use defaults
matBound[0] = 1.0 / 12;// .25;
matBound[1] = 6;// 10; //Up to 6Y maturities
strikeBound[0] = 0.4;
strikeBound[1] = 1.6;
}
Console.WriteLine(callDataSet);
/*
//CBA TEST
matBound[0] = 1;// .25;
matBound[1] = 3.5;// 10; //Up to 6Y maturities
strikeBound[0] = 0.5;// 0.5;
strikeBound[1] = 2;//1.5;
*/
HestonCallOptimizationProblem problem = NewOptimizationProblem(equityCalData, matBound, strikeBound);
int totalOpts = problem.numCall + problem.numPut;
Console.WriteLine("Calibration based on "+totalOpts+ " options. (" + problem.numCall + " call options and "+problem.numPut+" put options).");
IOptimizationAlgorithm solver = new QADE();
//IOptimizationAlgorithm solver = new MultiLevelSingleLinkage();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.controller = controller;
// If true the optimization algorithm will operate in parallel.
o.Parallel = Engine.MultiThread;
o.h = 10e-8;
o.epsilon = 10e-8;
SolutionInfo solution = null;
double minObj=double.MaxValue;
Vector minX= null;
int Z = 1;
//if (problem.GetType() == typeof(Heston.HestonCallSimulationOptimizationProblem))
// Z = 2;
for(int z=0;z<Z;z++)
{
if (solver.GetType() == typeof(MultiLevelSingleLinkage))
{
o.NP = 50;
o.MaxIter = 25;
o.MaxGamma = 6;
}
else
{
o.NP = 60;
o.MaxIter = 35;
}
o.Verbosity = 1;
Vector x0 = null;// new Vector(new double[] { 0.5, 0.5, 0.8, -0.5, 0.05 });
// GA
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return null;
o.options = "qn";
o.MaxIter = 500;// 1000;
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
return null;
if (solution.obj < minObj)
{
minObj = solution.obj;
minX = solution.x.Clone();
}
}
solution.obj = minObj;
solution.x = minX;
//Displays pricing error structure
HestonCallOptimizationProblem.displayObjInfo = true;
problem.Obj(solution.x);
HestonCallOptimizationProblem.displayObjInfo = false;
Console.WriteLine("Calibration Time (s)\t" + (DateTime.Now - t0).TotalSeconds);
return BuildEstimate(spotPrice,interestDataSet, callDataSet, equityCalData, solution);
}
/// <summary>
/// Attempts a calibration through <see cref="PelsserCappletOptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">The controller which may be used to cancel the process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
EstimationResult result;
if ((dataset.ZRMarket == null) || (dataset.CapVolatility == null))
{
result = new EstimationResult();
result.ErrorMessage = "Not enough data to calibrate.\n" +
"The estimator needs a ZRMarket and a CapVolatility " +
"defined inside InterestRateMarketData";
return result;
}
// Backup the dates
DateTime effectiveDate = DateTime.Now.Date;
DateTime valuationDate = DateTime.Now.Date;
if (Document.ActiveDocument != null)
{
effectiveDate = Document.ActiveDocument.ContractDate;
valuationDate = Document.ActiveDocument.SimulationStartDate;
}
// Creates the Context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
Function zr = new PFunction(null);
zr.VarName = "zr";
// Load the zr.
double[,] zrvalue = (double[,])ArrayHelper.Concat(dataset.ZRMarketDates.ToArray(), dataset.ZRMarket.ToArray());
zr.Expr = zrvalue;
prj.Symbols.Add(zr);
BlackModel bm = new BlackModel(zr);
double deltak = dataset.CapTenor;
Matrix capVol = dataset.CapVolatility;
Vector capMat = dataset.CapMaturity;
Vector capK = dataset.CapRate;
var preferences = settings as Fairmat.Calibration.CapVolatilityFiltering;
// Matrix calculated with black.
Matrix blackCaps = new Matrix(capMat.Length, capK.Length);
for (int m = 0; m < capMat.Length; m++)
{
for (int s = 0; s < capK.Length; s++)
{
bool skip = false;
if (preferences != null)
{
if (capK[s] < preferences.MinCapRate || capK[s] > preferences.MaxCapRate ||
capMat[m] < preferences.MinCapMaturity || capMat[m] > preferences.MaxCapMaturity)
{skip = true; }
}
if (capVol[m, s] == 0 || skip)
blackCaps[m, s] = 0;
else
blackCaps[m, s] = bm.Cap(capK[s], capVol[m, s], deltak, capMat[m]);
}
}
if (blackCaps.IsNAN())
{
Console.WriteLine("Black caps matrix has non real values:");
Console.WriteLine(blackCaps);
throw new Exception("Cannot calculate Black caps");
}
// Maturity goes from 0 to the last item with step deltaK.
Vector maturity = new Vector((int)(1.0 + capMat[capMat.Length - 1] / deltak));
for (int l = 0; l < maturity.Length; l++)
maturity[l] = deltak * l;
Vector fwd = new Vector(maturity.Length - 1);
for (int i = 0; i < fwd.Length; i++)
{
fwd[i] = bm.Fk(maturity[i + 1], deltak);
}
// Creates a default Pelsser model.
Pelsser.SquaredGaussianModel model = new Pelsser.SquaredGaussianModel();
model.a1 = (ModelParameter)0.014;
model.sigma1 = (ModelParameter)0.001;
model.zr = (ModelParameter)"@zr";
StochasticProcessExtendible iex = new StochasticProcessExtendible(prj, model);
prj.Processes.AddProcess(iex);
prj.Parse();
DateTime t0 = DateTime.Now;
Caplet cp = new Caplet();
PelsserCappletOptimizationProblem problem = new PelsserCappletOptimizationProblem(prj, cp, maturity, fwd, capK, deltak, capMat, blackCaps);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 35;
o.TargetCost = 0.0025;
o.MaxIter = 10;
o.Verbosity = Math.Max(1, Engine.Verbose);
o.controller = controller;
// Parallel evaluation is not supported for this calibration.
o.Parallel = false;
o.Debug = true;
SolutionInfo solution = null;
Vector x0 = (Vector)new double[] { 0.1, 0.1 };
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
o.epsilon = 10e-7;
o.h = 10e-7;
o.MaxIter = 1000;
o.Debug = true;
o.Verbosity = Math.Max(1, Engine.Verbose);
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
solution = solver2.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine(solution);
string[] names = new string[] { "alpha1", "sigma1" };
result = new EstimationResult(names, solution.x);
result.ZRX = (double[])dataset.ZRMarketDates.ToArray();
result.ZRY = (double[])dataset.ZRMarket.ToArray();
result.Objects = new object[1];
result.Objects[0] = solution.obj;
//result.Fit = solution.obj;//Uncomment in 1.6
// Restore the dates
if (Document.ActiveDocument != null)
{
Document.ActiveDocument.ContractDate = effectiveDate;
Document.ActiveDocument.SimulationStartDate = valuationDate;
}
return result;
}
/// <summary>
/// Attempts a calibration through <see cref="CapsCIROptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">A controller used for the optimization process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List<object> data, IEstimationSettings settings = null, IController controller = null, Dictionary<string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
// Creates the context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
CapCIROptimizationProblem problem = new CapCIROptimizationProblem(dataset);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 50;
o.MaxIter = 50;
o.Verbosity = 1;
o.Parallel = false;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 1, 0.01, 0.05 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
o.epsilon = 10e-10;
o.h = 10e-10;
o.MaxIter = 1000;
if (solution != null)
solution = solver2.Minimize(problem, o, solution.x);
else
solution = solver2.Minimize(problem, o, x0);
if (solution.errors)
return new EstimationResult(solution.message);
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = CIR.parameterNames;
Vector values = new Vector(4);
values[Range.New(0, 2)] = solution.x;
values[3] = problem.r0;
EstimationResult result = new EstimationResult(names, values);
return result;
}
/// <summary>
/// Attempts a calibration through <see cref="CapsCIROptimizationProblem"/>
/// using caps matrices.
/// </summary>
/// <param name="data">The data to be used in order to perform the calibration.</param>
/// <param name="settings">The parameter is not used.</param>
/// <param name="controller">A controller used for the optimization process.</param>
/// <returns>The results of the calibration.</returns>
public EstimationResult Estimate(List <object> data, IEstimationSettings settings = null, IController controller = null, Dictionary <string, object> properties = null)
{
InterestRateMarketData dataset = data[0] as InterestRateMarketData;
// Creates the context.
Document doc = new Document();
ProjectROV prj = new ProjectROV(doc);
doc.Part.Add(prj);
CapCIROptimizationProblem problem = new CapCIROptimizationProblem(dataset);
IOptimizationAlgorithm solver = new QADE();
IOptimizationAlgorithm solver2 = new SteepestDescent();
DESettings o = new DESettings();
o.NP = 50;
o.MaxIter = 50;
o.Verbosity = 1;
o.Parallel = false;
o.controller = controller;
SolutionInfo solution = null;
Vector x0 = new Vector(new double[] { 1, 0.01, 0.05 });
solution = solver.Minimize(problem, o, x0);
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
o.epsilon = 10e-10;
o.h = 10e-10;
o.MaxIter = 1000;
if (solution != null)
{
solution = solver2.Minimize(problem, o, solution.x);
}
else
{
solution = solver2.Minimize(problem, o, x0);
}
if (solution.errors)
{
return(new EstimationResult(solution.message));
}
Console.WriteLine("Solution:");
Console.WriteLine(solution);
string[] names = CIR.parameterNames;
Vector values = new Vector(4);
values[Range.New(0, 2)] = solution.x;
values[3] = problem.r0;
EstimationResult result = new EstimationResult(names, values);
return(result);
}
private static void Main()
{
Parameters.Verbosity = VerbosityLevels.AboveNormal;
// this next line is to set the Debug statements from OOOT to the Console.
Debug.Listeners.Add(new TextWriterTraceListener(Console.Out));
/* In this example, we first present how the details of an optimzation
* problem can be saved to an XML-file so that it can be read in
* and solved as opposed to defining all the details in an imperative
* (code line by code line) way. In the first function, the xml file
* name "test1.xml" is created. */
makeAndSaveProblemDefinition();
/* now we create a series of different optimization methods and test
* them on the problem. The problem is now opened from the file and
* the details are stored in an object of class "Problem Definition".*/
var stream = new FileStream(filename, FileMode.Open);
ProblemDefinition probTest1 = ProblemDefinition.OpenprobFromXml(stream);
abstractOptMethod opty;
/***********Gradient Based Optimization with Steepest Descent****************/
SearchIO.output("***********Gradient Based Optimization with Steepest Descent****************");
opty = new GradientBasedOptimization();
opty.Add(probTest1);
abstractSearchDirection searchDirMethod = new SteepestDescent();
opty.Add(searchDirMethod);
//abstractLineSearch lineSearchMethod = new ArithmeticMean(0.0001, 1, 100);
//abstractLineSearch lineSearchMethod = new DSCPowell(0.0001, 1, 100);
abstractLineSearch lineSearchMethod = new GoldenSection(0.0001, 1);
opty.Add(lineSearchMethod);
opty.Add(new squaredExteriorPenalty(opty, 10));
/* since this is not a population-based optimization method, we need to remove the MaxSpan criteria. */
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
double[] xStar;
var timer = Stopwatch.StartNew();
var fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/***********Gradient Based Optimization with Fletcher-Reeves****************/
SearchIO.output("***********Gradient Based Optimization with Fletcher-Reeves****************");
/* we don't need to reset (invoke the constructor) for GradientBasedOptimization since we are only
* change the seaach direction method. */
searchDirMethod = new FletcherReevesDirection();
/* you could also try the remaining 3 search direction methods. */
//searchDirMethod = new CyclicCoordinates();
//searchDirMethod = new BFGSDirection();
//searchDirMethod = new PowellMethod(0.001, 6);
opty.Add(searchDirMethod);
timer = Stopwatch.StartNew();
opty.ResetFunctionEvaluationDatabase();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Generalized Reduced Gradient***********************/
SearchIO.output("******************Generalized Reduced Gradient***********************");
opty = new GeneralizedReducedGradientActiveSet();
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 10));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/* GRG is the ONLY one here that handles constraints explicity. It find the
* optimal very quickly and accurately. However, many of the other show a
* better value of f*, this is because they are using an imperfect penalty
* function (new squaredExteriorPenalty(opty, 10)). While it seems that GRG
* includes it as well, it is only used in the the line search method. */
/******************Random Hill Climbing ***********************/
SearchIO.output("******************Random Hill Climbing ***********************");
opty = new HillClimbing();
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 8));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor));
opty.Add(new KeepSingleBest(optimize.minimize));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Exhaustive Hill Climbing ***********************/
SearchIO.output("******************Exhaustive Hill Climbing ***********************");
/* Everything else about the Random Hill Climbing stays the same. */
opty.Add(new ExhaustiveNeighborGenerator(probTest1.SpaceDescriptor));
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Simulated Annealing***********************/
SearchIO.output("******************Simulated Annealing***********************");
opty = new SimulatedAnnealing(optimize.minimize);
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 10));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor, 100));
opty.Add(new SACoolingSangiovanniVincentelli(100));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Exhaustive Search ***********************/
// SearchIO.output("******************Exhaustive Search ***********************");
//opty = new ExhaustiveSearch(probTest1.SpaceDescriptor, optimize.minimize);
//opty.Add(probTest1);
/* No convergence criteria is needed as the process concludes when all
* states have been visited but for this problem that is 4 trillion states.*/
//opty.ConvergenceMethods.Clear();
/* if you DID KNOW the best, you can include a criteria like...*/
//opty.ConvergenceMethods.Add(new ToKnownBestXConvergence(new[] { 3.0, 3.0 }, 0.0000001));
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
/* you probably will never see this process complete. Even with the added
* convergence criteria (which is not factored into the estimated time of
* completion), you are probably looking at 1 to 2 years. */
//printResults(opty, xStar, fStar, timer);
}