What you are doing makes sense. Starting from multiple starting points is important.
I am curious why you just don't just run 20 different 1-processor jobs instead of bothering with the parallelism? On Saturday, July 26, 2014 11:22:07 AM UTC-5, Iain Dunning wrote: > > The idea is to call the optimize function multiple times in parallel, not > to call it once and let it do parallel multistart. > > Check out the "parallel map and loops" section of the parallel programming > chapter in the Julia manual, I think it'll be clearer there. > > On Friday, July 25, 2014 8:00:40 PM UTC-4, Charles Martineau wrote: >> >> Thank you for your answer. So I would have to loop over, say 20 random >> set of starting points, where in my loop I would use the Optim package to >> minimize my MLE function for each random set. Where online is the documents >> that shows how to specify that we want the command >> >> Optim.optimize(my function, etc.) to be parallelized? Sorry for my >> ignorance, I am new to Julia! >> >> >> On Friday, July 25, 2014 2:04:08 PM UTC-7, Iain Dunning wrote: >>> >>> I'm not familiar with that particular package, but the Julia way to do >>> it could be to use the Optim.jl package and create a random set of starting >>> points, and do a parallel-map over that set of starting points. Should work >>> quite well. Trickier (maybe) would be to just give each processor a >>> different random seed and generate starting points on each processor. >>> >>> On Friday, July 25, 2014 3:05:05 PM UTC-4, Charles Martineau wrote: >>>> >>>> Dear Julia developers and users, >>>> >>>> I am currently using in Matlab the multisearch algorithm to find >>>> multiple local minima: >>>> http://www.mathworks.com/help/gads/multistart-class.html for a MLE >>>> function. >>>> I use this Multisearch in a parallel setup as well. >>>> >>>> Can I do something similar in Julia using parallel programming? >>>> >>>> Thank you >>>> >>>> Charles >>>> >>>>
