`scipy.optimize._nonlin:Anderson`

source: build-install/usr/lib/python3.14/site-packages/scipy/optimize/_nonlin.py:1042

Signature

class Anderson(alpha = None, w0 = 0.01, M = 5)

Members

Summary

Find a root of a function, using (extended) Anderson mixing.

Extended Summary

The Jacobian is formed by for a 'best' solution in the space spanned by last M vectors. As a result, only a MxM matrix inversions and MxN multiplications are required. [Ey]

Parameters

%(params_basic)s
alpha : float, optional: Initial guess for the Jacobian is (-1/alpha).
M : float, optional: Number of previous vectors to retain. Defaults to 5.
w0 : float, optional: Regularization parameter for numerical stability. Compared to unity, good values of the order of 0.01.
%(params_extra)s

Examples

The following functions define a system of nonlinear equations

def fun(x):
    return [x[0]  + 0.5 * (x[0] - x[1])**3 - 1.0,
            0.5 * (x[1] - x[0])**3 + x[1]]

✓

A solution can be obtained as follows.

from scipy import optimize
sol = optimize.anderson(fun, [0, 0])

✓

sol

✗

array([0.84117, 0.15884])

Aliases

scipy.optimize._nonlin.Anderson