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Does anybody know how these 2 solvers, (Oj algorithms) from Java and SCIP for Python, relate to each other performance wise (as in: which one is the fastest), when dealing with a typical MILP (Mixed Integer Linear Programming) problem? On first sight, I can't seem to find anything online that can point me in the right direction, and I'm curious!

Thanks in advance!

Riley
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  • Stackoverflow doesn't like "which is the best/fastest software for ..." questions. They are too unscientific. (Try quora). And do take a look at the solver [OptaPlanner (java, Apache License)](https://www.optaplanner.org/) too :) – Geoffrey De Smet Oct 24 '18 at 09:10
  • Apologies, I'll try my luck at quora then (and take a look at your OptaPlanner ;-) ) – Riley Oct 24 '18 at 11:07

1 Answers1

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The SCIP Optimization Suite is one of the fastest MIP and MINLP solvers available in source code. PySCIPOpt, its interface to Python, might be a bit slower when constructing the model but solving times are still good since it's running the pure SCIP C library in the background.

To be honest, I have no experience with oj! Algorithms and cannot say how good this solver is. Apparently it allows to link to Gurobi or CPLEX, so guess in this case it's mainly a modelling wrapper around those APIs providing high performance.

In the end it comes down to your modelling preferences/requirements and your specific problem instances.

mattmilten
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