On 28-Aug-08, at 11:23 AM, bryan cole wrote:
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.
Noisy how, exactly? And do you have gradients (or approximate gradients)? Can you at least be guaranteed that the function you are evaluating is proportional (on average) to the true function? There is a wide and deep literature on stochastic gradient descent, particularly in the context of neural networks. Here are some papers that you might find of interest: Local Gain Adaptation in Stochastic Gradient Descent by N. Schraudolph: http://tinyurl.com/69xm45 A set of lecture notes by Leon Bottou on the subject: http:// leon.bottou.org/papers/bottou-mlss-2004 In two dimensions, though, I doubt anything too complicated will be necessary. David