Set the optimization method.

The primary task of Sherpa is to fit a model M(p) to a set of observed data, where the vector p denotes the model parameters. An optimization method is one that is used to determine the vector of model parameter values, p0, for which the chosen fit statistic is minimized.

Parameters:meth (str) – The name of the method (case is not important). The list_methods function returns the list of supported values.
Raises:sherpa.utils.err.ArgumentErr – If the meth argument is not recognized.

See also

Return the name of the current optimization method.
List the supported optimization methods.
Set the fit statistic.


The available methods include:

The Levenberg-Marquardt method is an interface to the MINPACK subroutine lmdif to find the local minimum of nonlinear least squares functions of several variables by a modification of the Levenberg-Marquardt algorithm [1].
The implementation of the moncar method is based on [2].
The implementation of the Nelder Mead Simplex direct search is based on [3].
This is another name for neldermead.


[1]J.J. More, “The Levenberg Marquardt algorithm: implementation and theory,” in Lecture Notes in Mathematics 630: Numerical Analysis, G.A. Watson (Ed.), Springer-Verlag: Berlin, 1978, pp.105-116.
[2]Storn, R. and Price, K. “Differential Evolution: A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces.” J. Global Optimization 11, 341-359, 1997.
[3]Jeffrey C. Lagarias, James A. Reeds, Margaret H. Wright, Paul E. Wright “Convergence Properties of the Nelder-Mead Simplex Algorithm in Low Dimensions”, SIAM Journal on Optimization,Vol. 9, No. 1 (1998), pages 112-147.


>>> set_method('neldermead')