How to compare gams vs matlab in optimization
Matlab and GAMS are very different in how they approach modeling. GAMS is organized along the concept of equations (essentially an optimization model is a collection of equations). This is both for LP, MIP, MINLP and other types of models. These equations largely resemble how you would write things down in Math. Matlab views an optimization model (LP/MIP) as a matrix (or two matrices depending on whether we deal with equalities or inequalities). You have to translate your constraints in these one or two matrices by populating them. Depending on the model this can be a difficult task. For structured models it is not so bad, but for large, complex models the GAMS approach is much more natural and convenient.
NLP problems in GAMS are just like LPs: equation based. GAMS uses automatic differentiation so no need to write gradients and GAMS targets large scale NLP problems. Matlab uses functions in their NLP solvers, and these are mostly suited for smaller problems. Gradients are provided by the user.
GAMS supports many solvers. MATLAB has an optimization toolbox, but these solvers are largely targeted to smaller and medium sized models. Having said that many state-of-the-art solvers have a Matlab interface (e.g. Cplex, Gurobi).
Not all solvers available under GAMS are directly callable from Matlab but many are (sometimes using external toolboxes).Автор: Erwin Kalvelagen Размещён: 21.08.2016 09:35