public double GetStepLength(
Func <double[], double> f,
Func <double[], double>[] df,
OptVector d,
OptVector x0,
double alpham)
{
int maxIter = 15;
double alphap = 0;
double c1 = 1E-4;
double c2 = 0.5;
Random rnd = new Random();
double alphax = 1;
double fx0 = f(x0.MinArray);
double gx0 = OptimizationHelper.Derivative(df, x0) * d;
double fxp = fx0;
double gxp = gx0;
for (int i = 1; i < maxIter; i++)
{
OptVector xx = x0 + alphax * d;
double fxx = f(xx.MinArray);
double gxx = OptimizationHelper.Derivative(df, xx) * d;
if (fxx > fx0 + c1 * alphax * gx0 ||
(i > 1 && fxx >= fxp))
{
return(Zoom(f, df, x0, d, alphap, alphax));
}
if (Math.Abs(gxx) <= -c2 * gx0)
{
return(alphax);
}
if (gxx >= 0)
{
return(Zoom(f, df, x0, d, alphax, alphap));
}
alphap = alphax;
fxp = fxx;
gxp = gxx;
alphax = alphax + (alpham - alphax) * 0.3;
}
return(alphax);
}
public double GetStepLength(
Func <double[], double> f,
OptVector d,
OptVector x0,
double alpham,
int maxIter)
{
double alphap = 0;
double c1 = 1E-7;
double c2 = 0.5;
double alphax = alpham;//rnd.NextDouble();
double fx0 = f(x0.MinArray);
double gx0 = new OptVector(numericalDerivative.EvaluatePartialDerivative(f, x0.MinArray, 1)) * d;
double fxp = fx0;
double gxp = gx0;
for (int i = 1; i < maxIter; i++)
{
OptVector xx = x0 + alphax * d;
double fxx = f(xx.MinArray);
double gxx = new OptVector(numericalDerivative.EvaluatePartialDerivative(f, xx.MinArray, 1)) * d;
if (fxx > fx0 + c1 * alphax * gx0 ||
(i > 1 && fxx >= fxp))
{
return(Zoom(f, x0, d, alphap, alphax, maxIter));
}
if (Math.Abs(gxx) <= -c2 * gx0)
{
return(alphax);
}
if (gxx >= 0)
{
return(Zoom(f, x0, d, alphax, alphap, maxIter));
}
alphap = alphax;
fxp = fxx;
gxp = gxx;
double r = rnd.NextDouble();
alphax = alphax + (alpham - alphax) * r;
}
return(alphax);
}
public double PolakRibiere(
OptVector derivativeNew,
OptVector derivativeOld)
{
double num = derivativeNew * (derivativeNew - derivativeOld);
double den = derivativeOld * derivativeOld;
if (den == 0.0)
{
return(1.0);
}
return(Math.Max(0, num / den));
}
public double GetAlpha(
OptVector[] A,
OptVector p,
double num)
{
var denom = p * OptVector.Mult(A, p);
if (denom == 0.0)
{
return(1.0);
}
return(num / denom);
}
private OptVector[] BuildMatrixA(
List <Func <double[], double> > equalityConstraints,
List <InequalityConstraintProperties> inequalityConstraints,
OptVector[] lagrangianHessian,
OptVector x)
{
List <OptVector> constraintDerivative = new List <OptVector>();
for (int i = 0; i < equalityConstraints.Count; i++)
{
constraintDerivative.Add(new OptVector(numericalDerivative.EvaluatePartialDerivative(equalityConstraints[i], x.MinArray, 1)));
}
for (int i = 0; i < inequalityConstraints.Count; i++)
{
if (inequalityConstraints[i].IsActive)
{
constraintDerivative.Add(new OptVector(numericalDerivative.EvaluatePartialDerivative(inequalityConstraints[i].Function, x.MinArray, 1)));
}
}
OptVector[] bufHessian = new OptVector[lagrangianHessian.Length];
OptVector[] modifiedLagrangian = OptVector.Sum(lagrangianHessian, epsilonNw);
Array.Copy(modifiedLagrangian, bufHessian, lagrangianHessian.Length);
for (int i = 0; i < bufHessian.Length; i++)
{
OptVector buf = new OptVector(constraintDerivative.Count);
for (int j = 0; j < constraintDerivative.Count; j++)
{
buf[j] = constraintDerivative[j][i];
}
bufHessian[i].Add(buf);
}
var lagrangianList = bufHessian.ToList();
foreach (var item in constraintDerivative)
{
var res = OptVector.Add(item, new OptVector(constraintDerivative.Count, -epsilonSe));
lagrangianList.Add(res);
}
return(lagrangianList.ToArray());
}
private bool CheckEarlyExit(
OptVector xNew,
OptVector xOld)
{
OptVector diff = xNew - xOld;
double result = 0.0;
foreach (var value in diff.MinArray)
{
result += value * value;
}
return(result < Precision);
}
public OptVector Solve(
OptVector[] A,
OptVector b,
OptVector startX,
int nIter)
{
OptVector[] normA = A;
OptVector normb = b;
if (!OptVector.Equals(A, OptVector.Transpose(A)))
{
OptVector[] At = OptVector.Transpose(A);
normA = OptVector.Mult(At, A);
normb = OptVector.Mult(At, b);
}
OptVector rNew = normb - OptVector.Mult(normA, startX);
OptVector p = rNew;
OptVector x = new OptVector(startX);
double r2Old = rNew * rNew;
double alpha = 1.0;
double beta = 1.0;
for (int i = 0; i < nIter; i++)
{
alpha = GetAlpha(normA, p, r2Old);
x = x + alpha * p;
rNew = rNew - alpha * OptVector.Mult(normA, p);
double r2New = rNew * rNew;
if (r2New < Precision)
{
return(x);
}
beta = GetBeta(r2New, r2Old);
p = rNew + beta * p;
r2Old = r2New;
}
return(x);
}
private double Zoom(
Func <double[], double> f,
OptVector x0,
OptVector d,
double alphal,
double alphah,
int maxIter)
{
double c1 = 1E-7;
double c2 = 0.5;
double fx0 = f(x0.MinArray);
double gx0 = new OptVector(numericalDerivative.EvaluatePartialDerivative(f, x0.MinArray, 1)) * d;
double alphax = 0.0;
for (int i = 0; i < maxIter; i++)
{
alphax = 0.5 * (alphal + alphah);
OptVector xx = x0 + alphax * d;
double fxx = f(xx.MinArray);
OptVector xl = x0 + alphal * d;
double fxl = f(xl.MinArray);
if (fxx > fx0 + c1 * alphax * gx0 ||
fxx >= fxl)
{
alphah = alphax;
}
else
{
double gxx = new OptVector(numericalDerivative.EvaluatePartialDerivative(f, xx.MinArray, 1)) * d;
if (Math.Abs(gxx) <= -c2 * gx0)
{
return(alphax);
}
if (gxx * (alphah - alphal) >= 0.0)
{
alphah = alphal;
}
alphal = alphax;
}
}
return(alphax);
}
private static double Zoom(
Func <double[], double> f,
Func <double[], double>[] df,
OptVector x0,
OptVector d,
double alphal,
double alphah)
{
int maxIter = 10;
double c1 = 1E-4;
double c2 = 0.5;
double fx0 = f(x0.MinArray);
double gx0 = OptimizationHelper.Derivative(df, x0) * d;
double alphax = 0.0;
for (int i = 0; i < maxIter; i++)
{
alphax = 0.5 * (alphal + alphah);
OptVector xx = x0 + alphax * d;
double fxx = f(xx.MinArray);
double gxx = OptimizationHelper.Derivative(df, xx) * d;
OptVector xl = x0 + alphal * d;
double fxl = f(xl.MinArray);
if (fxx > fx0 + c1 * alphax * gx0 ||
fxx >= fxl)
{
alphah = alphax;
}
else
{
if (Math.Abs(gxx) <= -c2 * gx0)
{
return(alphax);
}
if (gxx * (alphah - alphal) >= 0.0)
{
alphah = alphal;
}
alphal = alphax;
}
}
return(alphax);
}
private List <InequalityConstraintProperties> SetInequalityLambda(
List <InequalityConstraintProperties> inqManager,
OptVector inqLambda)
{
int lambdaIndex = 0;
for (int i = 0; i < inqManager.Count; i++)
{
if (inqManager[i].IsActive)
{
inqManager[i].Lambda = inqLambda[lambdaIndex];
lambdaIndex++;
}
}
return(inqManager);
}
private OptVector ApplyBound(
OptVector x,
double[] lowerBound,
double[] upperBound)
{
OptVector boundx = new OptVector(x);
if (lowerBound != null &&
upperBound != null)
{
for (int i = 0; i < x.Count; i++)
{
if (x[i] < lowerBound[i])
{
boundx[i] = lowerBound[i];
}
else if (x[i] > upperBound[i])
{
boundx[i] = upperBound[i];
}
}
}
else if (lowerBound != null)
{
for (int i = 0; i < x.Count; i++)
{
if (x[i] < lowerBound[i])
{
boundx[i] = lowerBound[i];
}
}
}
else if (upperBound != null)
{
for (int i = 0; i < x.Count; i++)
{
if (x[i] > upperBound[i])
{
boundx[i] = upperBound[i];
}
}
}
return(boundx);
}
private OptVector CalculateDirection(
OptVector[] A,
OptVector b,
OptVector x,
OptVector lambdaEq,
OptVector lambdaIq)
{
OptVector leq = new OptVector(lambdaEq);
OptVector liq = new OptVector(lambdaIq);
OptVector startValue = OptVector.Add(x, leq);
startValue = OptVector.Add(startValue, liq);
//startValue = new MinVector(startValue.Count);
OptVector result = linearSolver.Solve(A, b, startValue, 1000);
return(result);
}
private bool RemoveNegativeLambda(
ref List <InequalityConstraintProperties> inequalityConstraintsProp,
OptVector lambda)
{
bool removed = false;
inequalityConstraintsProp = SetInequalityLambda(inequalityConstraintsProp, lambda);
foreach (var item in inequalityConstraintsProp)
{
if (item.Lambda < 0.0 &&
item.IsActive)
{
item.IsActive = false;
removed = true;
break;
}
}
return(removed);
}
static void TestFunc()
{
OptVector a = new OptVector(new double[] { 1, 2 });
OptVector b = new OptVector(new double[] { 3, 4 });
OptVector c = new OptVector(new double[] { 1, 3 });
OptVector d = new OptVector(new double[] { 4, 7 });
var res = OptVector.Mult(a, b);
var res1 = OptVector.SubtractFromIdentity(res);
var res2 = OptVector.Div(res, 2);
OptVector[] aa = new OptVector[] { a, b };
OptVector[] bb = new OptVector[] { c, d };
var res3 = OptVector.Mult(res, bb);
var res4 = OptVector.Sum(res, bb);
var res5 = OptVector.Mult(res, c);
}
private double GetEqualityConstraintsViolation(
List <Func <double[], double> > equalityConstraints,
OptVector x)
{
if (equalityConstraints.Count == 0)
{
return(0.0);
}
double maxViolation = 0.0;
foreach (var func in equalityConstraints)
{
double violation = Math.Abs(func(x.MinArray));
if (violation > maxViolation)
{
maxViolation = violation;
}
}
return(maxViolation);
}
static void TestCGMethod()
{
MINRES minres = new MINRES();
CGMethod cg = new CGMethod();
OptVector[] A2 = new OptVector[3];
A2[0] = new OptVector(new double[] { 2, 1, 3 });
A2[1] = new OptVector(new double[] { 2, 6, 8 });
A2[2] = new OptVector(new double[] { 6, 8, 18 });
OptVector b2 = new OptVector(new double[] { 1, 3, 5 });
var minresSol = minres.Solve(A2, b2, new OptVector(new double[3]), 50);
var sol2 = cg.Solve(A2, b2, new OptVector(new double[3]), 50);
OptVector[] A = new OptVector[3];
A[0] = new OptVector(new double[] { 2, 0, 1 });
A[1] = new OptVector(new double[] { 1, 6, 0 });
A[2] = new OptVector(new double[] { 3, 2, 3 });
OptVector x = new OptVector(new double[] { 2, 5, 7 });
var sol = cg.Solve(A, x, new OptVector(new double[3]), 50);
var solm = minres.Solve(A, x, new OptVector(new double[3]), 50);
OptVector[] A1 = new OptVector[3];
A1[0] = new OptVector(new double[] { 3, 1, -6 });
A1[1] = new OptVector(new double[] { 2, 1, -5 });
A1[2] = new OptVector(new double[] { 6, -3, 3 });
OptVector b1 = new OptVector(new double[] { -10, -8, 0 });
var sol1 = cg.Solve(A1, b1, new OptVector(new double[3]), 200);
var solm1 = minres.Solve(A1, b1, new OptVector(new double[3]), 200);
OptVector diff = b1 - OptVector.Mult(A1, solm1);
OptVector diff1 = b1 - OptVector.Mult(A1, sol1);
}
private double[] Execute(
Func <double[], double> f,
List <Func <double[], double> > equalityConstraints,
List <Func <double[], double> > inequalityConstraints,
double[] lowerBound,
double[] upperBound,
double[] startValues,
int nIter)
{
OptVector xOld = new OptVector(startValues);
OptVector xNew = new OptVector(xOld);
OptVector lastFeasibleSolution = new OptVector(xOld);
List <Func <double[], double> > eqConstraints = new List <Func <double[], double> >();
if (equalityConstraints != null)
{
eqConstraints = new List <Func <double[], double> >(equalityConstraints);
}
List <Func <double[], double> > inqConstraints = new List <Func <double[], double> >();
if (inequalityConstraints != null)
{
inqConstraints = new List <Func <double[], double> >(inequalityConstraints);
}
var boundsConstraints = CreateBoundsConstraints(lowerBound, upperBound);
OptVector lambdaEq = new OptVector(eqConstraints.Count);
List <InequalityConstraintProperties> inequalityConstraintsProp = new List <InequalityConstraintProperties>();
foreach (var item in inqConstraints)
{
inequalityConstraintsProp.Add(new InequalityConstraintProperties(false, 0.0, item, false, 0.0, false, -1));
}
foreach (var item in boundsConstraints)
{
inequalityConstraintsProp.Add(new InequalityConstraintProperties(false, 0.0, item.Function, false, 0.0, true, item.Index));
}
OptVector[] hessian = OptVector.GetIdentity(startValues.Length);
int iqIndexStart = -1;
SetInequalityActiveConstraint(xNew, ref inequalityConstraintsProp, ref iqIndexStart);
Func <double[], double> lagrangian = BuildLagrangian(f, eqConstraints, inequalityConstraintsProp, lambdaEq.MinArray);
double directionValue = 0.0;
double lastMinValue = double.MaxValue;
double[] eqPenaltyParams = GetAndSetPenaltyParam(
f,
eqConstraints,
ref inequalityConstraintsProp,
xOld);
for (int i = 0; i < nIter; i++)
{
OptVector lambdaIq = new OptVector(inequalityConstraintsProp.Where(x => x.IsActive).Select(x => x.Lambda).ToArray());
OptVector direction = GetGradientDirection(
xNew,
eqConstraints,
lambdaEq,
inequalityConstraintsProp,
hessian,
lagrangian,
lambdaIq);
OptVector directionX = ExtractX(direction, xOld);
lambdaIq = lambdaIq + ExtractInequalityLambda(direction, xOld, lambdaEq, lambdaIq);
if (RemoveNegativeLambda(ref inequalityConstraintsProp, lambdaIq))
{
lagrangian = BuildLagrangian(f, eqConstraints, inequalityConstraintsProp, lambdaEq.MinArray);
continue;
}
directionValue = directionX * directionX;
if (directionValue < Precision)
{
inequalityConstraintsProp = SetInequalityLambda(inequalityConstraintsProp, lambdaIq);
SetInequalityActiveConstraint(xNew, ref inequalityConstraintsProp, ref iqIndexStart);
if (EarlyExit &&
!inequalityConstraintsProp.Any(x => !x.IsValid))
{
lastFeasibleSolution = xNew;
break;
}
lagrangian = BuildLagrangian(f, eqConstraints, inequalityConstraintsProp, lambdaEq.MinArray);
}
else
{
double step = ComputeStepLength(
f,
equalityConstraints,
inequalityConstraintsProp,
directionX,
xOld,
1.0,
eqPenaltyParams);
OptVector stepValue = step * directionX;
xNew = xOld + stepValue;
OptVector gradient = xNew - xOld;
if (gradient.Length() == 0.0 ||
stepValue.Length() < 1E-10)
{
OptVector nDirection = directionX.Normalize();
step = ComputeStepLength(
f,
equalityConstraints,
inequalityConstraintsProp,
nDirection,
xOld,
1.0,
eqPenaltyParams);
OptVector stVal = step * nDirection;
if (stVal.Length() < localMinCheck)
{
stepValue = nDirection;
hessian = OptVector.GetIdentity(xNew.Count);
}
else
{
stepValue = stVal;
}
xNew = xOld + stepValue;
}
if (xNew.MinArray.Contains(double.NaN))
{
xNew = lastFeasibleSolution;
xOld = xNew;
hessian = OptVector.GetIdentity(xNew.Count);
foreach (var item in inequalityConstraintsProp)
{
item.Lambda = 0.0;
}
lambdaEq = new OptVector(eqConstraints.Count);
eqPenaltyParams = GetAndSetPenaltyParam(
f,
eqConstraints,
ref inequalityConstraintsProp,
xNew);
lagrangian = BuildLagrangian(f, eqConstraints, inequalityConstraintsProp, lambdaEq.MinArray);
continue;
}
lambdaEq = lambdaEq + ExtractEqualityLambda(direction, xOld, lambdaEq);
inequalityConstraintsProp = SetInequalityLambda(inequalityConstraintsProp, lambdaIq);
SetInequalityActiveConstraint(xNew, ref inequalityConstraintsProp, ref iqIndexStart);
hessian = CalculateLagrangianHessian(lagrangian, hessian, xNew, xOld, stepValue);
lagrangian = BuildLagrangian(f, eqConstraints, inequalityConstraintsProp, lambdaEq.MinArray);
//Set penalty params
eqPenaltyParams = SetPenaltyEqParam(eqPenaltyParams, lambdaEq);
SetPenaltyIqParam(ref inequalityConstraintsProp);
//Penalty Parameters
double newEqConstraintsViolation = GetEqualityConstraintsViolation(eqConstraints, xNew);
double minValue = f(xNew.MinArray);
if (minValue < lastMinValue &&
!inequalityConstraintsProp.Any(x => !x.IsValid) &&
newEqConstraintsViolation < equalityConstraintTol)
{
lastFeasibleSolution = xNew;
lastMinValue = minValue;
}
xOld = xNew;
}
}
return(lastFeasibleSolution.MinArray);
}
private void SetInequalityActiveConstraint(
OptVector x,
ref List <InequalityConstraintProperties> inequalityConstraints,
ref int iqIndexStart)
{
int zeroIndex = -1;
int maxIndex = -1;
double maxViolation = double.MinValue;
for (int i = 0; i < inequalityConstraints.Count; i++)
{
double fValue = inequalityConstraints[i].Function(x.MinArray);
inequalityConstraints[i].IsValid = (fValue > inequalityConstraintTol) ?
false :
true;
}
bool checkValidity = inequalityConstraints.Any(k => !k.IsValid);
for (int i = 0; i < inequalityConstraints.Count; i++)
{
double fValue = inequalityConstraints[i].Function(x.MinArray);
//Check negative lambda
if (inequalityConstraints[i].Lambda < 0.0)
{
continue;
}
//Check zero
else if (Math.Abs(fValue) < 1E-15 &&
!checkValidity &&
!inequalityConstraints[i].IsActive)
{
zeroIndex = i;
}
else if (fValue > inequalityConstraintTol &&
zeroIndex < 0 &&
fValue > maxViolation &&
!inequalityConstraints[i].IsActive)
{
maxIndex = i;
maxViolation = fValue;
}
}
//Add constraint
if (zeroIndex >= 0)
{
inequalityConstraints[zeroIndex].IsActive = true;
}
else if (maxIndex >= 0)
{
inequalityConstraints[maxIndex].IsActive = true;
}
else
//Add constraint if all active have lambda > 0 (not feasible case)
if (inequalityConstraints.Count(k => k.IsActive) != 0 &&
inequalityConstraints.Count(k => k.IsActive) ==
inequalityConstraints.Count(k => k.IsActive && k.Lambda > 0.0))
{
//maxIndex = GetIqNotValidIndex(inequalityConstraints);
ActivateInqConstraint(ref inequalityConstraints, ref iqIndexStart);
}
else if (!inequalityConstraints.Any(k => k.IsActive) &&
inequalityConstraints.Any(k => !k.IsValid))
{
ActivateInqConstraint(ref inequalityConstraints, ref iqIndexStart);
}
}
private double ComputeStepLength(
Func <double[], double> f,
List <Func <double[], double> > equalityConstraints,
List <InequalityConstraintProperties> inequalityConstraints,
OptVector direction,
OptVector x,
double maxStep,
double[] eqPenaltyParams)
{
double stepLength = 1.0;
double meritFunction(double[] k)
{
double normEq = 0.0;
if (equalityConstraints != null)
{
for (int i = 0; i < equalityConstraints.Count; i++)
{
normEq += eqPenaltyParams[i] * equalityConstraints[i](k);
}
}
double normIq = 0.0;
if (inequalityConstraints != null)
{
for (int i = 0; i < inequalityConstraints.Count; i++)
{
normIq += inequalityConstraints[i].PenaltyParam * Math.Max(0.0, inequalityConstraints[i].Function(k));
}
}
return(f(k) + normEq + normIq);
}
stepLength = strongWolfeLineSearch.GetStepLength(meritFunction, direction, x, maxStep, 350);
foreach (var item in inequalityConstraints.Where(k => !k.IsActive))
{
double fvalue = item.Function((x + direction).MinArray);
if (fvalue < inequalityConstraintTol)
{
continue;
}
double step = 1.0;
if (item.Linear)
{
step = 1.0 - Math.Abs(fvalue / direction[item.BoundIndex]);
}
else
{
OptVector derivative = new OptVector(numericalDerivative.EvaluatePartialDerivative(item.Function, x.MinArray, 1));
double fVal = item.Function(x.MinArray);
Func <double[], double> fBase = (k) =>
{
OptVector diff = new OptVector(k) - x;
double mult = derivative * diff;
return(Math.Abs(fVal + mult));
};
step = strongWolfeLineSearch.GetStepLength(fBase, direction, x, 1.0, 120);
}
if (step < stepLength &&
step > 0)
{
stepLength = step;
}
}
return(stepLength);
}
private OptVector[] CalculateLagrangianHessian(
Func <double[], double> lagrangian,
OptVector[] lagrangianHessian,
OptVector xNew,
OptVector xOld,
OptVector step)
{
OptVector s = step;
OptVector y = new OptVector(numericalDerivative.EvaluatePartialDerivative(lagrangian, xNew.MinArray, 1)) -
new OptVector(numericalDerivative.EvaluatePartialDerivative(lagrangian, xOld.MinArray, 1));
OptVector[] yy = OptVector.Mult(y, y);
double ys = y * s;
OptVector partialDenom = OptVector.Mult(lagrangianHessian, s);
OptVector[] num = OptVector.Mult(partialDenom, OptVector.Mult(s, lagrangianHessian));
double denom = -1.0 * s * partialDenom;
#region Positive definiteness
if (ys < 1E-15)
{
double theta = 0.999999;
OptVector yNew = new OptVector(y);
double ysNew = ys;
while (ysNew < lambda * s * partialDenom &&
theta >= 0.0)
{
yNew = y * theta + (1.0 - theta) * partialDenom;
ysNew = yNew * s;
theta = theta - 1E-5;
}
y = yNew;
ys = ysNew;
yy = OptVector.Mult(y, y);
}
#endregion
if (ys == 0.0)
{
if (step.Length() > 0)
{
return(OptVector.GetIdentity(xNew.Count, step));
}
return(OptVector.GetIdentity(xNew.Count));
}
OptVector[] addParam1 = OptVector.Div(yy, ys);
OptVector[] addParam2 = OptVector.Div(num, denom);
return(OptVector.Sum(OptVector.Sum(lagrangianHessian, addParam1), addParam2));
}
public OptVector Solve(
OptVector[] A,
OptVector b,
OptVector startX,
int nIter)
{
OptVector[] symmA = A;
OptVector normb = b;
//Symmetrize matrix
if (CheckSymmetry)
{
OptVector[] At = OptVector.Transpose(A);
if (!OptVector.Equals(A, At))
{
symmA = OptVector.Mult(At, A);
normb = OptVector.Mult(At, b);
}
}
OptVector v0 = new OptVector(b.Count);
OptVector v1 = normb - OptVector.Mult(symmA, startX);
double beta1 = v1.Length();
double betaN = 0.0;
double n = beta1;
double c0 = 1.0;
double c1 = 1.0;
double s0 = 0.0;
double s1 = 0.0;
OptVector w0 = new OptVector(v1.Count);
OptVector w_1 = new OptVector(v1.Count);
OptVector x = new OptVector(startX);
for (int i = 0; i < nIter; i++)
{
//Calculate Lanczos Vectors
OptVector v = (1.0 / beta1) * v1;
OptVector Av = OptVector.Mult(symmA, v);
double alpha = v * Av;
v1 = Av - alpha * v - beta1 * v0;
betaN = v1.Length();
//Calculate QR factors
double lambda = c1 * alpha - c0 * s1 * beta1;
double p1 = Math.Sqrt(lambda * lambda + betaN * betaN);
double p2 = s1 * alpha + c0 * c1 * beta1;
double p3 = s0 * beta1;
//Calculate New Givens Rotations
c0 = c1;
c1 = lambda / p1;
s0 = s1;
s1 = betaN / p1;
//Update Solution
OptVector w = (1.0 / p1) * (v - p3 * w_1 - p2 * w0);
x = x + c1 * n * w;
n = -s1 * n;
residual = Math.Abs(n);
if (residual < precisionConst)
{
break;
}
beta1 = betaN;
v0 = v;
w_1 = w0;
w0 = w;
}
return(x);
}
private OptVector ExtractX(
OptVector direction,
OptVector x)
{
return(new OptVector(SubArray(direction.MinArray, 0, x.Count)));
}
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();
}
private OptVector GetGradientDirection(OptVector xNew, List <Func <double[], double> > eqConstraints, OptVector lambdaEq, List <InequalityConstraintProperties> inequalityConstraintsProp, OptVector[] hessian, Func <double[], double> lagrangian, OptVector lambdaIq)
{
OptVector b = BuildVectorB(eqConstraints, inequalityConstraintsProp, lagrangian, xNew);
OptVector[] A = BuildMatrixA(eqConstraints, inequalityConstraintsProp, hessian, xNew);
OptVector direction = CalculateDirection(A, b, xNew, lambdaEq, lambdaIq);
return(direction);
}