Hello everyone. 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 threaded. 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. Thanks, David