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^2
convergence 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
>>> 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])