# Approximate Newton's method with constant approximate inverse Hessian

## Definition

Approximate Newton's method with constant approximate inverse Hessian refers to a type of approximate Newton's method for optimization of a function of multiple variables where the matrix used as the stand-in for the inverse Hessian is constant across iterations, independent both of the current value of the iterate and of the number of iterations.

## Iterative step

Note that we use parenthesized superscripts to denote iterates and iteration-specific variables, and subscripts to denote coordinates. Parenthesized superscripts do not denote exponents.

Suppose we are applying gradient descent to minimize a function $f:\R^n \to \R$. We will denote the input to $f$ as a vector $\vec{x} = (x_1,x_2,\dots,x_n)$. Given a guess $\vec{x}^{(k)}$, gradient descent computes the next guess $\vec{x}^{(k+1)}$ as follows:

$\vec{x}^{(k+1)} = \vec{x}^{(k)} - S \nabla f\left(\vec{x}^{(k)}\right)$

Here, $S$ is intended as a stand-in (an approximation or a bound of some sort) on the inverse matrix of the Hessian matrix of $f$. Unlike the usual Newton's method for optimization of a function of multiple variables, where $S$ would be replaced by the inverse Hessian at the point $\vec{x}^{(k)}$, the matrix $S$ needs to be independent of $\vec{x}^{(k)}$.

## Particular cases

### The one-dimensional version is just gradient descent with constant learning rate

In one dimension, this reduces to gradient descent with constant learning rate, where the constant approximate inverse Hessian is the $1 \times 1$ matrix with entry equal to the constant learning rate. Therefore, the analysis of that problem applies here. In particular, see:

### The general case

Function type Discussion of approximate Newton's method with constant approximate inverse Hessian
quadratic function of multiple variables approximate Newton's method with constant approximate inverse Hessian for a quadratic function of multiple variables
convex function of multiple variables approximate Newton's method with constant approximate inverse Hessian for a convex function of multiple variables