Simplex

class astropy.modeling.optimizers.Simplex

Bases: Optimization

Neald-Mead (downhill simplex) algorithm.

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

References

[1]

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

Attributes Summary

supported_constraints

Methods Summary

__call__(objfunc, initval, fargs, **kwargs)

Run the solver.

Attributes Documentation

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

Methods Documentation

__call__(objfunc, initval, fargs, **kwargs)

Run the solver.

Parameters:

objfuncpython:callable()

objection function

initvalpython:iterable

initial guess for the parameter values

fargspython:tuple

other arguments to be passed to the statistic function

kwargspython:dict

other keyword arguments to be passed to the solver