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Mini-HOWTO on using Octave for Unconstrained Nonlinear Optimization1
Nonlinear optimization problems are very common and when a solution
cannot be found analytically, one usually tries to find it numerically.
This document shows how to perform unconstrained nonlinear minimization
using the Octave language for numerical computation. We assume to
be so lucky as to have an initial guess from which to start an iterative
method, and so impatient as to avoid as much as possible going into
the details of the algorithm. In the following examples, we consider
multivariable problems, but the single variable case is solved in
exactly the same way.
All the algorithms used below return numerical approximations of local
minima of the optimized function. In the following examples, we minimize
a function with a single minimum (Figure 1), which
is relatively easily found. In practice, success of optimization algorithms
greatly depend on the optimized function and on the starting point.
Søren Hauberg
2008-04-29