I have a function which takes 100+ coefficients and outputs $x$. I wish to optimise $x$.
Running the simulation 50 000 times will take around 15 minutes, however, this happens in parallel - and the separate CPUs do not communicate with each other.
When I try usual gradient descent methods I end up with something which I am confident is a local minimum. Methods which are more likely to find a global maximum, such as simulated annealing I can only get to run "linearly", so I cannot run the simulation in parallel, and so is prohibitively slow.
I was thinking that machine learning might have a potential solution when dealing with this dimensionality of data, and hopefully, one which is readily accessible (read: function I can call). I would run a lot of training sets, then ask the machine learning algorithm to help identify and predict maxima (even if it isn't the global one).
I understand at the heart of most machine learning problems lies optimisation. But, I seem to find that mots machine learning algorithms which are used for different generic goals (i.e., classification, clustering, regression). I cannot easily find many which would guide an optimisation algorithm. Is there any which would help in this case?