Next: About this document
Parallel Decomposition of Large-Scale Stochastic Nonlinear Programs
John R. Birge
Department of Industrial and Operations Engineering, University of Michigan Ann Arbor, MI 48109, USA
Charles H. Rosa
International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria
Abstract: Many practical decision problems involve both nonlinear relationships and uncertainties. The resulting stochastic nonlinear programs become quite difficult to solve as the number of possible scenarios increases. In this paper, we provide a decomposition method for problems in which nonlinear constraints appear within periods. We also show how the method extends to upper bounding refinements of the set of scenarios when the random data are independent from period to period. We then apply the method to a stochastic model of the U.S. economy based on the Global 2100 method developed by Manne and Richels.
Key words: decomposition,economics, environment, parallel computation, stochastic programming
This paper is a technical report in the Department of Industrial and Operations Engineering, University of Michigan. For a postscript version of the full paper, click here.