Parallel many dimensional optimization

Question

I am building a script that generates input data [parameters] for another program to calculate. I would like to optimize the resulting data. Previously I have been using the num

Answer 1

If I haven't gotten wrong what you are asking, you are trying to minimize your function one parameter at the time.

you can obtain it by creating a set of function of a single argument, where for each function you freeze all the arguments except one.

Then you go on a loop optimizing each variable and updating the partial solution.

This method can speed up by a great deal function of many parameters where the energy landscape is not too complex (the dependency between the parameters is not too strong).

given a function

energy(*args) -> value

you create the guess and the function:

guess = [1,1,1,1]
funcs = [ lambda x,i=i: energy( guess[:i]+[x]+guess[i+1:] ) for i in range(len(guess)) ]

than you put them in a while cycle for the optimization

while convergence_condition:
    for func in funcs:
        optimize fot func
        update the guess
    check for convergence

This is a very simple yet effective method of simplify your minimization task. I can't really recall how this method is called, but A close look to the wikipedia entry on minimization should do the trick.