Hi Dominique, This will definitely be very useful for accessing the large array of problem instances written in AMPL.
As for writing solvers in Julia around this format, I'm admittedly biased but I don't think it's an ideal approach. We already have a pure-Julia implementation of AD for exact sparse Hessians in JuMP. That said, solvers written in Julia shouldn't be tied to a particular AD implementation or modeling language; ideally they will just implement a nonlinear MathProgBase interface (which doesn't quite exist yet), on top of which they could be called from JuMP or AMPL. I agree with Tony that it could be very interesting to use this interface as an interchangeable backend for JuMP's AD. Also, I'm not sure if you've considered it, but by licensing this interface under GPL, all solvers that use it must be released under GPL also, I believe. Miles On Sunday, April 13, 2014 6:14:51 AM UTC+1, Dominique Orban wrote: > > I just put together a few C and Julia files that let users read models in > the AMPL modeling language for optimization. > > https://github.com/dpo/ampl.jl > > It's not quite a module or a package; please bear with me as I'm still > learning Julia. This gives access to a huge collection of problems already > written in AMPL, e.g., > > http://orfe.princeton.edu/~rvdb/ampl/nlmodels/index.html > https://github.com/mpf/Optimization-Test-Problems (many of the same > problems, without the solve command) > http://netlib.org/ampl/models/ > etc. > > AMPL computes first and second derivatives for you, so it should be easy > to pass such problems to solvers written in Julia, and to write solvers > around this model format. > > Cheers. >
