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