Nope. One could write a SIF parser from scratch, but it would take some time.
--Tim On Sunday, July 27, 2014 08:51:51 AM John Myles White wrote: > 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
