Light Gradient Based Optimization.
Author: Alexis Mignon (c) Oct. 2012 E-mail: [email protected]
This module provides routines for gradient based optimization. It focuses on the case where gradient computation is expensive and should be avoided as much as possible.
To achieve this, the inner line search procedure uses only one gradient estimation. The line search is then performed using quadratic and cubic interpolation as described in:
J. Nocedal and S. Wright. Numerical Optimization. Chap.3 p56
Three optimization schemes are provided:
- steepest gradient descent (since sometimes it's still the most practicle way to do it).
- low-memory GFBS Quasi-Newton method.
- conjugate gradient (Fletcher-Reeves method)
Why writing optimization code while there exists optimized packages to do so ?
Because the problems I had to deal with had the following properties:
- the gradient computation is expensive (don't even think of computing the Hessian),
- the domains on which I have to optimize may not be Euclidean.
The only assumptions made on the optimization variables (and gradient values) is that they support some basic operations in normed vector spaces:
- addition,
- multiplication by a scalar,
- inner product (a custom inner product function can be provided).