Node:Search Stopping Parameters for Minimization Algorithms, Next:Minimization Algorithms using Derivatives, Previous:Iteration of the Minimization Algorithm, Up:Nonlinear Least-Squares Fitting
A minimization procedure should stop when one of the following conditions is true:
The handling of these conditions is under user control. The functions below allow the user to test the current estimate of the best-fit parameters in several standard ways.
int gsl_multifit_test_delta (const gsl_vector * dx, const gsl_vector * x, double epsabs, double epsrel) | Function |
This function tests for the convergence of the sequence by comparing the
last step dx with the absolute error epsabs and relative
error epsrel to the current position x. The test returns
for each component of x and returns |
int gsl_multifit_test_gradient (const gsl_vector * g, double epsabs) | Function |
This function tests the residual gradient g against the absolute
error bound epsabs. Mathematically, the gradient should be
exactly zero at the minimum. The test returns GSL_SUCCESS if the
following condition is achieved,
and returns |
int gsl_multifit_gradient (const gsl_matrix * J, const gsl_vector * f, gsl_vector * g) | Function |
This function computes the gradient g of \Phi(x) = (1/2) ||F(x)||^2 from the Jacobian matrix J and the function values f, using the formula g = J^T f. |