# [Matrix-SIG] Nonlinear optimization routines anyone?

**David Ascher
**
da@ski.org

*Mon, 15 Mar 1999 12:10:07 -0800 (Pacific Standard Time)*

>* The conjugate gradient algorithm is probably about twenty lines or less of
*>* matrix/vector statements in Python, assuming you have a preconditioner you
*>* can express that way. So just code it up in Python. It will be fast enough,
*>* all the hard work is in the dot products and matrix multiplies.
*
CG is fine for some cases, but not all. What I miss in Python is the
choices offered by e.g. Matlab's Optimization Toolbox:
- Unconstrained Optimization
- Quasi-Newton
- Least Squares
- Nonlinear Least Squares
- Gauss-Newton
- Levenberg Marquart
- Constrained optimization
- Sequential quadratic programming
- Multiobjective optimization
[taken from the TOC].
I suspect that if I knew enough about optimization I could code up the
subset that I need, but I'm not sure I'd trust my own code to do this...
--david