Python class that implements the Newton method

Here is a python function I wrote to implement the Newton method for optimization for the case where you are trying to optimize a function that takes a vector input and gives a scalar output. I use numdifftools to approximate the hessian and the gradient of the given function then perform the newton method iteration.

import numpy as np
import numdifftools as nd

class multivariate_newton(object):

    def __init__(self,func,start_point,step_size=0.8,num_iter=100,tol=0.000001):
        '''
        func: function to be optimized. Takes a vector argument as input and returns
              a scalar output
        step_size: step size in newton method update step
        num_iter: number of iterations for newton method to run
        tol: tolerance to determine convergence
        '''
        self.func=func
        self.start_point=np.array(start_point)
        self.num_iter=num_iter
        self.step_size=step_size
        self.tol=tol

    def newton_method(self):
        '''
        perform multivariate newton method for function with vector input
        and scalar output
        '''
        x_t=self.start_point
        #Get an approximation to hessian of function
        H=nd.Hessian(self.func)
        #Get an approximation of Gradient of function
        g=nd.Gradient(self.func)

        for i in range(self.num_iter):
            x_tplus1=x_t-self.step_size*np.dot(np.linalg.inv(H(x_t)),g(x_t))
            #check for convergence
            if abs(max(x_tplus1-x_t))<self.tol:
                break
            x_t=x_tplus1

        self.crit_point=x_tplus1
        self.max_min=self.func(x_t)

        return self

    def critical_point(self):
        '''
        print critical point found in newton_method function. newton_method function
        must be called first.
        '''
        print self.crit_point

Pathological cases exist where Newton's method will not converge on a solution. Yet, there is no obvious way for the caller to tell whether convergence was achieved. You should distinguish between whether the loop terminated via the break (success) or by exhaustion of the loop condition (failure).

for _ in range(self.num_iter):
    x_tplus1 = x_t - self.step_size * np.dot(np.linalg.inv(H(x_t)), g(x_t))
    #check for convergence
    if abs(max(x_tplus1-x_t))<self.tol:
        break
    x_t = x_tplus1
else:
    raise SolutionNotFound, "No convergence after %d iterations" % (self.num_iter)

StackExchange Code Review Q#38436, answer score: 2