E. Fragniere, J. Gondzio and J.-P. Vial
Abstract
We present an integrated procedure to build and solve big stochastic
programming models. The individual components of the system
--the modeling language, the solver and the hardware-- are easily
accessible, or a least affordable to a large audience. The procedure
is applied to a simple financial model, which can be expanded
to arbitrarily large sizes by enlarging the number of scenarios.
We generated a model with one million scenarios, whose deterministic
equivalent linear program has 1,111,112 constraints and 2,555,556
variables. We have been able to solve it on the cluster of ten PCs
in less than 3 hours.
Key words: Algebraic Modeling Language, Decomposition Methods, Distributed Systems, Large-Scale Optimization, Stochastic Programming.