//! Solve least square problem using numerix solver
public Vector perform(ref LeastSquareProblem lsProblem)
{
double eps = accuracy_;
// wrap the least square problem in an optimization function
LeastSquareFunction lsf = new LeastSquareFunction(lsProblem);
// define optimization problem
Problem P = new Problem(lsf, c_, initialValue_);
// minimize
EndCriteria ec = new EndCriteria(maxIterations_, Math.Min(maxIterations_ / 2, 100), eps, eps, eps);
exitFlag_ = (int)om_.minimize(P, ec);
results_ = P.currentValue();
resnorm_ = P.functionValue();
bestAccuracy_ = P.functionValue();
return(results_);
}
// curve optimization called here- adjust optimization parameters here
internal void calculate()
{
FittingCost costFunction = costFunction_;
Constraint constraint = new NoConstraint();
// start with the guess solution, if it exists
Vector x = new Vector(size(), 0.0);
if (!curve_.guessSolution_.empty())
{
x = curve_.guessSolution_;
}
if (curve_.maxEvaluations_ == 0)
{
//Don't calculate, simply use given parameters to provide a fitted curve.
//This turns the fittedbonddiscountcurve into an evaluator of the parametric
//curve, for example allowing to use the parameters for a credit spread curve
//calculated with bonds in one currency to be coupled to a discount curve in
//another currency.
return;
}
//workaround for backwards compatibility
OptimizationMethod optimization = optimizationMethod_;
if (optimization == null)
{
optimization = new Simplex(curve_.simplexLambda_);
}
Problem problem = new Problem(costFunction, constraint, x);
double rootEpsilon = curve_.accuracy_;
double functionEpsilon = curve_.accuracy_;
double gradientNormEpsilon = curve_.accuracy_;
EndCriteria endCriteria = new EndCriteria(curve_.maxEvaluations_,
curve_.maxStationaryStateIterations_,
rootEpsilon,
functionEpsilon,
gradientNormEpsilon);
optimization.minimize(problem, endCriteria);
solution_ = problem.currentValue();
numberOfIterations_ = problem.functionEvaluation();
costValue_ = problem.functionValue();
// save the results as the guess solution, in case of recalculation
curve_.guessSolution_ = solution_;
}
public override EndCriteria.Type minimize(Problem P, EndCriteria endCriteria)
{
// Initializations
double ftol = endCriteria.functionEpsilon();
int maxStationaryStateIterations_ = endCriteria.maxStationaryStateIterations();
EndCriteria.Type ecType = EndCriteria.Type.None; // reset end criteria
P.reset(); // reset problem
Vector x_ = P.currentValue(); // store the starting point
int iterationNumber_ = 0;
// dimension line search
lineSearch_.searchDirection = new Vector(x_.size());
bool done = false;
// function and squared norm of gradient values
double fnew, fold, gold2;
double fdiff;
// classical initial value for line-search step
double t = 1.0;
// Set gradient g at the size of the optimization problem
// search direction
int sz = lineSearch_.searchDirection.size();
Vector prevGradient = new Vector(sz), d = new Vector(sz), sddiff = new Vector(sz), direction = new Vector(sz);
// Initialize cost function, gradient prevGradient and search direction
P.setFunctionValue(P.valueAndGradient(ref prevGradient, x_));
P.setGradientNormValue(Vector.DotProduct(prevGradient, prevGradient));
lineSearch_.searchDirection = prevGradient * -1;
bool first_time = true;
// Loop over iterations
do
{
// Linesearch
if (!first_time)
{
prevGradient = lineSearch_.lastGradient();
}
t = (lineSearch_.value(P, ref ecType, endCriteria, t));
// don't throw: it can fail just because maxIterations exceeded
if (lineSearch_.succeed())
{
// Updates
// New point
x_ = lineSearch_.lastX();
// New function value
fold = P.functionValue();
P.setFunctionValue(lineSearch_.lastFunctionValue());
// New gradient and search direction vectors
// orthogonalization coef
gold2 = P.gradientNormValue();
P.setGradientNormValue(lineSearch_.lastGradientNorm2());
// conjugate gradient search direction
direction = getUpdatedDirection(P, gold2, prevGradient);
sddiff = direction - lineSearch_.searchDirection;
lineSearch_.searchDirection = direction;
// Now compute accuracy and check end criteria
// Numerical Recipes exit strategy on fx (see NR in C++, p.423)
fnew = P.functionValue();
fdiff = 2.0 * Math.Abs(fnew - fold) /
(Math.Abs(fnew) + Math.Abs(fold) + Const.QL_EPSILON);
if (fdiff < ftol ||
endCriteria.checkMaxIterations(iterationNumber_, ref ecType))
{
endCriteria.checkStationaryFunctionValue(0.0, 0.0, ref maxStationaryStateIterations_, ref ecType);
endCriteria.checkMaxIterations(iterationNumber_, ref ecType);
return(ecType);
}
P.setCurrentValue(x_); // update problem current value
++iterationNumber_; // Increase iteration number
first_time = false;
}
else
{
done = true;
}
}while (!done);
P.setCurrentValue(x_);
return(ecType);
}
