Optimization: principles and algorithms, by Michel Bierlaire
Functions
newtonLocalQuadratic.m File Reference

Algorithm 10.2: Local Newton for optimization using the quadratic model. More...

Go to the source code of this file.

Functions

function newtonLocalQuadratic (in obj, in x0, in eps, in cg, in maxiter)
 Applies local Newton algorithm to solve $\nabla f(x)=0$ where $\nabla f:\mathbb{R}^n\to\mathbb{R}^n $ is the gradient of the objective function. More...
 

Detailed Description

Algorithm 10.2: Local Newton for optimization using the quadratic model.

Implementation of algorithm 10.2 of [1]

Author
Michel Bierlaire
Date
Fri Mar 20 16:14:06 2015

Definition in file newtonLocalQuadratic.m.

Function Documentation

function newtonLocalQuadratic ( in  obj,
in  x0,
in  eps,
in  cg,
in  maxiter 
)

Applies local Newton algorithm to solve $\nabla f(x)=0$ where $\nabla f:\mathbb{R}^n\to\mathbb{R}^n $ is the gradient of the objective function.

Note
Tested with run0508quadratic.m
Calls quadraticDirect
Calls conjugateGradient
Parameters
objthe name of the Octave function defining $\nabla f(x)$ and the hessian $\nabla^2 f(x)$
x0the starting point
epsalgorithm stops if $\|F(x)\| \leq \varepsilon $.
cgif 0, the quadratic model is solved using the direct method. If not zero, using the conjugate gradient algorithm.
maxitermaximum number of iterations (Default: 100)
Returns
[solution,f]
solution: root of the function
f: value of F at the solution
Copyright 2015-2016 Michel Bierlaire