# sigpy.alg.NewtonsMethod¶

class sigpy.alg.NewtonsMethod(gradf, inv_hessf, x, beta=1, f=None, max_iter=10, tol=0)[source]

Newton’s Method.

Parameters: gradf (function) - A function gradf(x) – x -> gradient of f at x. inv_hessf (function) - A function H(x) – x -> inverse Hessian of f at x, which is another function: y -> inverse Hessian of f at x times y. x (function) – beta (scalar) – backtracking linesearch factor. Enables backtracking when beta < 1. f (function or None) – function to compute $$f$$ for backtracking line-search. max_iter (int) – maximum number of iterations. tol (float) – Tolerance for stopping condition.
__init__(gradf, inv_hessf, x, beta=1, f=None, max_iter=10, tol=0)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(gradf, inv_hessf, x[, beta, f, …]) Initialize self. done() Return whether the algorithm is done. update() Perform one update step.