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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.
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
GSL_SUCCESS
if the following condition is achieved,
for each component of x and returns GSL_CONTINUE
otherwise.
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 GSL_CONTINUE
otherwise. This criterion is suitable
for situations where the precise location of the minimum, x,
is unimportant provided a value can be found where the gradient is small
enough.
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.