private IVector <T> MinimizeInternal(ILeastSquaresFunctional <T> objective, IParametricFunction <T> function, IVector <T> initialParameters, IVector <T> minimumParameters = default,
IVector <T> maximumParameters = default)
{
var la = LinearAlgebra.Value;
var x = initialParameters.Clone() as IVector <T>;
var bindF = function.Bind((x));
var iter = 0;
var residual = objective.Residual(bindF);
var error = la.Sqrt(la.Dot(residual.ToArray(), residual.ToArray()));
var oldError = la.Cast(1000);
while (la.Compare(la.Sub(error, oldError), Eps) == 1 || iter++ < MaxIteration)
{
var jacobi = objective.Jacobian(bindF);
var jacobiT = jacobi.Transpose(); //JT
var jTj = jacobiT.Mult(jacobi); //jTj
var jTj_1 = jTj.Inverse();
var jTj_1jT = jTj_1.Mult(jacobiT);
var temp = jTj_1jT.Mult(residual);
var gamma = GoldenRatioMethod <T> .FindMin(objective, function, x, temp, Eps);
x.Sub(temp.Mult(gamma));
this.ApplyMinimumAndMaximumValues(minimumParameters, maximumParameters, x, la);
oldError = error;
bindF = function.Bind(x);
residual = objective.Residual(bindF);
error = la.Sqrt(la.Dot(residual.ToArray(), residual.ToArray()));
}
return(x);
}
private IVector <T> MinimizeInternal(IDifferentiableFunctional <T> objective, IParametricFunction <T> function, IVector <T> initialParameters, IVector <T> minimumParameters = default,
IVector <T> maximumParameters = default)
{
var la = LinearAlgebra.Value;
var xOld = initialParameters.Clone() as IVector <T>;
//Calculate initial gradient
var bindF = function.Bind(xOld);
var fiNew = objective.Gradient(bindF);
var xNew = initialParameters.Clone() as IVector <T>;
var k = 0;
var normOld = la.Sqrt(la.Dot(fiNew.ToArray(), fiNew.ToArray()));
var normNew = la.Cast(1000);
while (k++ < MaxIteration && (la.Compare(la.Sqrt(la.Dot(fiNew.ToArray(), fiNew.ToArray())), Eps) == 1) && (la.Compare(la.Sub(normNew, normOld), Eps) == 1))
{
bindF = function.Bind(xNew);
fiNew = objective.Gradient(bindF);
normOld = normNew;
normNew = la.Sqrt(la.Dot(fiNew.ToArray(), fiNew.ToArray()));
var gamma = GoldenRatioMethod <T> .FindMin(objective, function, xOld, fiNew, Eps);
xOld = xNew.Clone() as IVector <T>;
xNew = xOld.Sub(fiNew.MultWithCloning(gamma));
this.ApplyMinimumAndMaximumValues(minimumParameters, maximumParameters, xNew, la);
}
return(xNew);
}
private IVector <T> MinimizeInternal(IDifferentiableFunctional <T> objective, IParametricFunction <T> function, IVector <T> initialParameters, IVector <T> minimumParameters = default,
IVector <T> maximumParameters = default)
{
var la = LinearAlgebra.Value;
var xNew = initialParameters.Clone() as IVector <T>;
var bindF = function.Bind(xNew);
var curVal = objective.Value(bindF);
var prevVal = curVal;
var gradient = objective.Gradient(bindF);
var p = objective.Gradient(bindF).Clone() as IVector <T>;
gradient.Mult(la.Cast(-1));
var gradSquare = la.Dot(p.ToArray(), p.ToArray());
int numIter = 0;
do
{
T alpha, beta, newGradSquare;
IVector <T> newGrad;
//Ищем минимум F(x + alpha * p) с помощью метода одномерной оптимизации
alpha = GoldenRatioMethod <T> .FindMin(objective, function, xNew, p, Eps);
xNew = xNew.Add(p.MultWithCloning(la.Mult(la.Cast(-1), alpha)));
this.ApplyMinimumAndMaximumValues(minimumParameters, maximumParameters, xNew, la);
bindF = function.Bind(xNew);
newGrad = objective.Gradient(bindF).Mult(la.Cast(-1));
newGradSquare = la.Dot(newGrad.ToArray(), newGrad.ToArray());
beta = numIter % (5 * SpaceSize) == 0
? la.GetZeroValue()
: la.Div(la.Mult(la.Cast(-1), la.Sub(newGradSquare, la.Dot(newGrad.ToArray(), gradient.ToArray()))), gradSquare);
p.Mult(beta).Add(newGrad);
prevVal = curVal;
curVal = objective.Value(bindF);
gradient = newGrad;
gradSquare = newGradSquare;
} while (la.Compare(gradSquare, Eps) == 1 && MaxIteration > numIter++);
return(xNew);
}
