Miles can speak to the design, but JuMP provides quite a bit more than just AD. It gives a user-friendly modeling environment like Pyomo or Yalmip, or the .mod layer of AMPL which is neither open-source nor a modern general-purpose programming language. Miles and Iain have a very interesting paper from last year discussing more of the motivation here http://www.mit.edu/~mlubin/juliacomputing.pdf - although as I understand it the nonlinear section from that paper differs in implementation from what's under development in JuMP right now.
In your Python workflow, were you using Pyomo, or custom generating AMPL format input files in some other way? There are major performance and accessibility advantages to having the user-facing modeling environment in pure Julia. If both the AD implementation and the solver were using binary C/C++ libraries, that would be several trips in and out of various libraries and their individual data structures at each solver iteration. And it introduces more binary dependencies which make maintenance and installation more challenging. Julia has better tools for doing this in a smooth cross-platform way than any other environment I've seen, but it takes some investment to set up in the first place. And the upstream library needs to be set up correctly - the vast majority of upstream dependencies have much slower development cycles than Julia does. Performance benchmarks will surely be forthcoming to compare the performance of the pure-Julia AD to AMPL as well as established C libraries like ADOL-C and CppAD. On Monday, April 14, 2014 4:14:00 PM UTC-7, Dominique Orban wrote: > > I realize I should probably have posted this on the Julia-Opt group, but I > didn't know about it. I have to look around JuMP more to understand its > design. I have 10+ year of successful research with the ampl.jl model (only > in Python) so I'm quite confident that it has most of what a continuous > optimizer needs, with the exception perhaps of cone optimization. Also I'd > like to ask why you would reimplement backward AD. Some people spend their > entire career writing reliable AD software. It's not a judgement on your > code; I would have interfaced ADOL-C which already provides much of what > optimization needs, including Jacobian-vector products and Hessian-vector > products (and Hessian of the Lagrangian - vector products). > > On Sunday, April 13, 2014 2:48:26 AM UTC-7, Miles Lubin wrote: >> >> 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. >>> >>
