scipy.optimize.
fixed_point#
- scipy.optimize.fixed_point(func, x0, args=(), xtol=1e-08, maxiter=500, method='del2')[source]#
- 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.- Parameters:
- funcfunction
- Function to evaluate. 
- x0array_like
- Fixed point of function. 
- argstuple, optional
- Extra arguments to func. 
- xtolfloat, optional
- Convergence tolerance, defaults to 1e-08. 
- maxiterint, 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^2convergence acceleration [1]. The “iteration” method simply iterates the function until convergence is detected, without attempting to accelerate the convergence.
 
 - References [1]- Burden, Faires, “Numerical Analysis”, 5th edition, pg. 80 - Examples - >>> 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)) array([ 1.4920333 , 1.37228132])