Grid-Enabled Optimization with GAMS

We describe a framework for modeling optimization problems for solution on a grid computer. The framework is easy to adapt to multiple grid engines, and can seamlessly integrate evolving mechanisms from particular computing platforms. It facilitates the widely used master/worker model of computing and is shown to be exible and powerful enough for a large variety of optimization applications. In particular, we summarize a number of new features of the GAMS modeling system that provide a lightweight, portable and powerful framework for optimization on a grid. We provide downloadable examples of its use for embarrasingly parallel nancial applications, decomposition and iterative algorithms and for solving very di cult mixed integer programs to optimality. Computational results are provided for a number of di erent grid engines, including multi-core machines, a pool of machines controlled by the Condor resource manager and the grid engine from Sun Microsystems.