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

Steepest descent algorithm, calculating the Cauchy point at each iteration. More...

Go to the source code of this file.

Functions

function steepestDescentCauchy (in obj, in x0, in eps, in maxiter)
 Applies the steepest descent algorithm to solve $\min_x f(x)$ where $f:\mathbb{R}^n\to\mathbb{R}$. The Cauchy point is calculated at each iteration. More...
 

Detailed Description

Steepest descent algorithm, calculating the Cauchy point at each iteration.

Author
Michel Bierlaire
Date
Fri Mar 20 17:07:48 2015

Definition in file steepestDescentCauchy.m.

Function Documentation

function steepestDescentCauchy ( in  obj,
in  x0,
in  eps,
in  maxiter 
)

Applies the steepest descent algorithm to solve $\min_x f(x)$ where $f:\mathbb{R}^n\to\mathbb{R}$. The Cauchy point is calculated at each iteration.

Note
Tested with run1101cauchy.m
Parameters
objthe name of the Octave function defining f(x) and its derivatives
x0the starting point
epsalgorithm stops if $\|F(x)\| \leq \varepsilon $.
maxitermaximum number of iterations (Default: 100)
Returns
[solution,iteres,niter]
solution: local minimum of the function
iteres: sequence of iterates generated by the algorithm. It contains n+2 columns. Columns 1:n contains the value of the current iterate. Column n+1 contains the value of the objective function. Column n+2 contains the value of the norm of the gradient. It contains maxiter rows, but only the first niter rows are meaningful.
niter: total number of iterations
Copyright 2015-2016 Michel Bierlaire