Improving convergence of the stochastic decomposition algorithm by using an efficient sampling technique

This work focuses on the basic stochastic decomposition (SD) algorithm of Higle and Sen [J.L. Higle, S. Sen, Stochastic Decomposition, Kluwer Academic Publishers, 1996] for two-stage stochastic linear programming problems with complete recourse. The algorithm uses sampling when the random variables are represented by continuous distribution functions. Traditionally, this method has been applied by using Monte Carlo (MC) sampling to generate the samples of the stochastic variables. However, Monte Carlo methods can result in large error bounds and variance. Hence, some other approaches use importance sampling to reduce variance and achieving convergence faster that the method based on the Monte Carlo sampling technique. This work proposes an improvement on this respect. Hence, we propose to replace the use of the Monte Carlo sampling technique or the importance sampling in the SD algorithm by the use of the novel Hammersley sequence sampling (HSS) technique. Recently, such a technique h...

• Type: Journal
• Year: 2004
• Where: CCE
• Authors: José María Ponce-Ortega, Vicente Rico-Ramírez, Salvador Hernández-Castro, Urmila M. Diwekar
• Keywords: Carlo Sampling Technique, CCE 2004, CCE 2007, Monte Carlo, Monte Carlo Sampling