class astropy.modeling.optimizers.Simplex[source] [edit on github]

Bases: astropy.modeling.optimizers.Optimization

Neald-Mead (downhill simplex) algorithm.

This algorithm [1] only uses function values, not derivatives. Uses scipy.optimize.fmin.


[1](1, 2) Nelder, J.A. and Mead, R. (1965), “A simplex method for function minimization”, The Computer Journal, 7, pp. 308-313

Attributes Summary


Methods Summary

__call__(objfunc, initval, fargs, **kwargs) Run the solver.

Attributes Documentation

supported_constraints = ['bounds', 'fixed', 'tied']

Methods Documentation

__call__(objfunc, initval, fargs, **kwargs)[source] [edit on github]

Run the solver.

objfunc : callable

objection function

initval : iterable

initial guess for the parameter values

fargs : tuple

other arguments to be passed to the statistic function

kwargs : dict

other keyword arguments to be passed to the solver