I have a bit of code that fits theoretical prediction to experimental data, and I want to run a LMA (Levenberg-Marquardt Algorithm) to fit the theory to experiment. However the calculations are non-trivial, with each model taking ~10-30 minutes to calculate on a single processor, however the problem is embarrassingly parallelisable and the code is currently set up to submit the different components (of a single itteration) to a cluster computer (this calculation still takes ~1-2 minutes).
Now this submission script is set up within a callable function within python - so for setting it up with the scipy LMA (scipy.optimise.leastsq) it is relatively trivial - however the scipy LMA will, I imagine, pass each individual calculation (for gauging the gradient) in serial, and wait for the return, whereas I'd prefer the LMA to send an entire set of calculations at a time, and then await the return. The python submission script looks a bit like:
def submission_script(number_iterations,number_parameters,value_parameters):
fitness_parameter = [0]*number_iterations
<fun stuff>
return (fitness_parameter)
Where the "value_parameters" is a nested list of dimensions [number_iterations][number_parameters] which contains the variables that are to be calculated for each model, "number_parameters" is the number of parameters that are to be fitted, "number_iterations" is the number of models to be calculated (so each step, to gauge the gradient, the LMA calculates 2*number_parameters models), and "fitness_parameter" is the value that has to be minimised (and has the dimensions [iterations]).
Now, obviously, I could write my own LMA, but that is a little bit of reinventing the wheel - I was wondering if there was anything out there that would satisfy my needs (or if the scipy LMA can be used in this way).
A Gauss-Newton algorithm should also work, as the starting point should be near the minima. The ability to constrain the fit (i.e. set maximum and minimum values for the fitted parameters) would be nice, but isn't necessary.
The scipy.optimize.leastsq function gives you the opportunity to provide a function J for evaluating the jacobian for a given parameter vector. You could implement a multiprocessing solution for calculating this matrix instead of having scipy.optimize.leastsq approximate it by serially calling your function f.
Unfortunately the LMA implementation in scipy uses separate functions for f and J. You may want to cache information you calculate in f in order to reuse it in J if it is called with the same parameter vecor. Alternatively you can implement a own LMA version that uses a single fJ call.