Computational grids to solve large scale optimization problems with uncertain data

In this paper, we discuss the use of computational grids to solve stochastic optimization problems. These problems are generally difficult to solve and are often characterized by a high number of variables and constraints. Furthermore, for some applications, it is required to achieve a real-time solution. Obtaining reasonable results is a difficult objective without the use of high-performance computing. We present a grid-enabled path-following algorithm and we discuss some experimental results.