`scipy.optimize._minpack_py:fixed_point`

Signature

   def     fixed_point (    func    ,    x0    ,    args    =  ()   ,    xtol    =  1e-08   ,    maxiter    =  500   ,    method    =  del2     )

Find a fixed point of the function.

Given a function of one or more variables and a starting point, find a fixed point of the function: i.e., where func(x0) == x0.

func : function: Function to evaluate.
x0 : array_like: Fixed point of function.
args : tuple, optional: Extra arguments to func.
xtol : float, optional: Convergence tolerance, defaults to 1e-08.
maxiter : int, optional: Maximum number of iterations, defaults to 500.
method : {"del2", "iteration"}, optional: Method of finding the fixed-point, defaults to "del2", which uses Steffensen's Method with Aitken's Del^2 convergence acceleration ^[1]. The "iteration" method simply iterates the function until convergence is detected, without attempting to accelerate the convergence.

import numpy as np
from scipy import optimize
def func(x, c1, c2):
   return np.sqrt(c1/(x+c2))
c1 = np.array([10,12.])
c2 = np.array([3, 5.])

✓

optimize.fixed_point(func, [1.2, 1.3], args=(c1,c2))

✗