Is CUTEst.jl easier to get working these days? The issue I opened in March seems to still be open.
— John On Jul 27, 2014, at 6:40 AM, Tim Holy <[email protected]> wrote: > A package of test functions sounds worthwhile. There's also CUTEst.jl: > https://github.com/lpoo/CUTEst.jl > > --Tim > > On Sunday, July 27, 2014 06:25:28 AM Hans W Borchers wrote: >> Ken: >> >> (1) Thanks for pointing out this approach and for implementing it. >> Unfortunately, I was not able to locate your code at Github. I would >> certainly try it out on some of my examples in global optimization. >> >> (2) Did you include (or do you plan to include) the improvements of >> MinFinder, >> as discussed in "MinFinder 2.0: An improved version of MinFinder" by >> Tsoulos and Lagaris? >> >> (3) Also this article contains examples of functions with many local minima. >> Most of these are test functions for global optimization procedures. Did >> you test your function on these examples? >> >> I have implemented some of these functions for my own purposes. >> I wonder whether it would be useful to have a Julia package of its own for >> compiling optimization test functions. >> >> (4) Are you sure/Is it guaranteed MinFinder will *reliably* find *all* >> local minima? >> This is a difficult problem, and for example there is a long discussion on >> this topic in Chapter 4, by Stan Wagon, in the book "The SIAM 100 Digit >> Challenge" about all the preventive measures to be taken to be able to >> guarantee to find all local minima -- and thus also the one global minimum. >> >> On Sunday, July 27, 2014 8:26:31 AM UTC+2, Ken B wrote: >>> Hi Charles, >>> >>> You can have a look at the MinFinder algorithm for which I've just created >>> a pull request to Optim.jl (talk about a coincidence!): >>> https://github.com/JuliaOpt/Optim.jl/pull/72 >>> >>> I'd like to add the possibility to run each optimization in parallel, but >>> I have no experience with these things, although I have time to learn :). >>> Would you like to collaborate on this? >>> >>> Does anyone know of some parallel sample code to have a look at? Basically >>> it's sending each optimization problem to a separate worker and getting >>> the >>> results, taking into account that some optimizations might take much >>> longer >>> than others. >>> >>> Cheers, >>> Ken >>> >>> On Saturday, 26 July 2014 23:13:28 UTC-5, Charles Martineau wrote: >>>> Yes I could do that but it is simpler (I think) to execute the code in >>>> parallel instead of sending 20 codes to be executed on the cluste.r >>>> >>>> On Saturday, July 26, 2014 10:08:20 AM UTC-7, Michael Prentiss wrote: >>>>> 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 >
