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I use lp_solve (from R) currently to solve reasonably large (but sparse) LPs/IPs for planning/optimization and other Operations Research flavored problems that arise at work.

Broadly speaking, it works well, and i like working in R, but i know that the models we consider will need to evolve at least to being quadratic in nature (with the possibility of being non-convex in general).

I'd like to know what the "industry standard" is in this space: should i not look beyond the AMPL/CPLEX combination (and invest some time there) ? Additional questions: Which of GAMS/AMPL is a better choice? (not proficient in either, but know what i need functionally: sparse matrix support for example) For really large LPs/QPs/IPs, how well does CPLEX scale across clusters? How much of a curve is there to be able to setup/run such a cluster?

jumblees
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  • You could try posting your question on www.or-exchange.org, as well. – LarrySnyder610 Nov 27 '14 at 20:51
  • Be aware that if you go non-linear (you suggest "at least quadratic and possibly non-convex") then your choice of solvers is somewhat reduced. CPLEX, Gurobi etc don't do general non-linear, just quadratic and SOCP I think. Also you will usually take a big hit in terms of performance and/or problem size you can tackle once you introduce quadratic terms - I have seen e.g. 3 orders of magnitude difference just from adding quadratic terms into a model in one case. – TimChippingtonDerrick Nov 27 '14 at 22:15
  • For comparison of AMPL and GAMS, see http://stackoverflow.com/a/4009637/471164 – vitaut Nov 27 '14 at 22:43
  • @grendelsdad : thanks for the pointer, will post there – jumblees Dec 01 '14 at 21:14
  • @TimChippingtonDerrick : noted, thanks: seems to make the case to stick with adding more types of constraints into a LP model. – jumblees Dec 01 '14 at 21:19
  • @vitaut: thanks for the pointer to the ampl/gams comparison – jumblees Dec 01 '14 at 21:19
  • see also this [question](http://stackoverflow.com/questions/26858236/using-gurobi-with-java-vs-gurobi-with-ampl/26879355#comment43005131_26879355) – David Nehme Dec 10 '14 at 16:22

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