[SciPy-User] optimization with multiple processors.
J. David Lee
Mon Aug 13 15:49:35 CDT 2012
For my work I will need to perform an optimization using an objective
function that is slow to evaluate (a few to tens of minutes). I have
access to a virtually unlimited number of processors for my work to
speed this up, but the optimization routines in scipy are all single
I see two simple ways to utilize multiple processors for newton-like or
gradient-descent optimization methods:
1) numerical gradient or Jacobian calculation
2) line search
To some extent, parallel processing could be applied to a simplex-like
method as well (the simplest example would be doing parallel computation
of reflection, extension and contraction points).
I think this functionality would fit perfectly in scipy, and I will be
working on it in any event, so if anyone would be interested in
collaborating or providing guidance, please let me know.
More information about the SciPy-User