---

NonLinearLeastSquare Class Template Reference

#include <leastsquare.h>

List of all members.

Public Methods
	NonLinearLeastSquare (double accuracy=1e-4, int maxiter=100)
	Default constructor.
	NonLinearLeastSquare (double accuracy, int maxiter, QuantLib::Handle< OptimizationMethod< V > > om)
	Default constructor.
	~NonLinearLeastSquare ()
	Destructor.
template<class M> V &	Perform (LeastSquareProblem< V, M > &lsProblem)
	Solve least square problem using numerix solver.
V &	results ()
	return the results.
double	residualNorm ()
	return the least square residual norm.
double	lastValue ()
	return last function value.
int	exitFlag ()
	return exit flag.
int	iterationsNumber ()
	return the performed number of iterations.
Private Attributes
V	results_
	solution vector.
V	initialValue_
	solution vector.
double	resnorm_
	least square residual norm.
int	exitFlag_
	Exit flag of the optimization process.
double	accuracy_
	required accuracy of the solver.
double	bestAccuracy_
	required accuracy of the solver.
unsigned int	maxIterations_
	maximum and real number of iterations.
unsigned int	nbIterations_
	maximum and real number of iterations.
QuantLib::Handle< OptimizationMethod< V > >	om_
	Optimization method.

---

Detailed Description

template<class V>
class NonLinearLeastSquare< V >

Default least square method using a given optimization algorithm (default is conjugate gradient).

min { r(x) : x in R^n }

where r(x) = ||f(x)||^2 the euclidian norm of f(x) for some vector-valued function f from R^n to R^m f = (f1, ..., fm) with fi(x) = bi - phi(x,ti) where bi is the vector of target data and phi is a scalar function.

Assuming the differentiability of f, the gradient of r is define by grad r(x) = f'(x)^t.f(x)

V vector class has the requirement of the previous class Handle class is need to manage pointer to optimization method

Definition at line 138 of file leastsquare.h.

---

The documentation for this class was generated from the following file:

• leastsquare.h

---