Teaching Numerical Gradients and Hessians
14 Feb 2011
14 Aug 2012)
Simple, well-commented Matlab code to demonstrate how to take numerical derivatives and Hessians.
|num_hess(func, X, NFV, h)
% [H, NFV] = num_hess(func, X, NFV, h)
% Function to compute the numerical Hessian of an arbitrary objective
% -> func: Function handle for which numerical Hessian is to be
% -> X: Point of interest about which Hessian is to be obtained.
% -> NFV: Accumulator to keep track of number of function
% -> h: Tolerance for differentiation.
% -> H: Numerical Hessian of function func (square symmetric
% matrix of size n=length(X)).
% -> NFV: Accumulator incremented by the number of function
% evaluations which have taken place.
% Created by: Brendan C. Wood
% Created on: February 14, 2011
% Copyright (c) 2011, Brendan C. Wood <firstname.lastname@example.org>
function [H, NFV] = num_hess(func, X, NFV, h)
H = zeros(length(X));
% for each dimension of objective function
% derivative at first point (left)
x1 = X;
x1(i) = X(i) - h;
[df1, NFV] = num_grad(func, x1, NFV, h);
% derivative at second point (right)
x2 = X;
x2(i) = X(i) + h;
[df2, NFV] = num_grad(func, x2, NFV, h);
% differentiate between the two derivatives
d2f = (df2-df1) / (2*h);
% assign as row i of Hessian
H(i,:) = d2f';