[SciPy-user] advice on stochastic(?) optimisation

[David Cournapeau]

On Fri, Aug 29, 2008 at 12:23 AM, bryan cole <bryan.cole at teraview.com> wrote:
> Hi,
>
> I'll looking for a bit of guidance as to what sort of algorithm is most
> appropriate/efficient for finding the local maximum of a function (in 2
> dimensions), where each function evaluation is 1) noisy and 2)
> expensive/slow to evaluate.
>

Do you have a formula for the function f ? If what you have is only
noisy observations of f(x), without knowing f, that's basically what
stochastic approximation is about: you have a big literature about
this kind of problems. The first article is the one introducing
Robbins-Monro algorithm:

Robbins, H. and Monro, S. "A Stochastic Approximation Method." Ann.
Math. Stat. 22, 400-407, 1951.

A recent book covering the field is the Kushner and Yin book:
http://www.springer.com/math/probability/book/978-0-387-00894-3?cm_mmc=Google-_-Book%20Search-_-Springer-_-0

The problem of those algorithms is that they are hard to implement in
python, because of their recursive nature, hence non vectorizable. If
your function/observation is hard to compute, it may not be a big
problem, though.

cheers,

David