Based on your expertise, what is the better optimizer tool between GAMS ( https://www.gams.com/ ) and Gurobi ( https://www.gurobi.com/ )?
Please also let me know your field of research.
GAMS itself is no solver, it is a modeling system with interfaces to a number of solvers - Gurobi being one of them.
So there is no point in trying to compare GAMS with any solver.
Gurobi is a solver for linear and (convex) quadratic mixed-integer problems.
GAMS has interfaces to solvers for different problem classes, too (like more general nonlinear optimization problems).
But if your problem is a MILP, you might compare the modeling via GAMS with the modeling via the Gurobi Python API.
I have worked with both (and with other modeling systems like AMPL, OPL and other solvers, especially CPLEX) in research on discrete stochastic optimization. The choice of the modeling interface depends simply on your preferences and the tasks to be performed (like modifying the problem according to the solution and solving a series of problems). If you are used to the GAMS language you might prefer it to others. GAMS and AMPL are not free software and you have to pay for many of the solvers for them, too.
Best regards
Ralf
Gurobi is a state of the art solver, albeit with a steep learning curve.
GAMS is actually a “platform“ linked to a number of solvers (LP, NLP, MIP etc). You can formulate the problem and select a specific solver (Incorporated within GAMS) to compute the solution.
Share your problem... I have been using the software for a while.
kind regards
Lagouge
Dear Lagouge Tartibu , thank you for your answer. I use already GAMS but I havn't used Gurobi. My question was what is the better solver tool between the two, and for which field of research.
GAMS itself is no solver, it is a modeling system with interfaces to a number of solvers - Gurobi being one of them.
So there is no point in trying to compare GAMS with any solver.
Gurobi is a solver for linear and (convex) quadratic mixed-integer problems.
GAMS has interfaces to solvers for different problem classes, too (like more general nonlinear optimization problems).
But if your problem is a MILP, you might compare the modeling via GAMS with the modeling via the Gurobi Python API.
I have worked with both (and with other modeling systems like AMPL, OPL and other solvers, especially CPLEX) in research on discrete stochastic optimization. The choice of the modeling interface depends simply on your preferences and the tasks to be performed (like modifying the problem according to the solution and solving a series of problems). If you are used to the GAMS language you might prefer it to others. GAMS and AMPL are not free software and you have to pay for many of the solvers for them, too.
Best regards
Ralf
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