Enhancements of two-stage stochastic decomposition

This paper presents some enhancements associated with stochastic decomposition (SD). Specifically, we study two issues: (a) Are there any conditions under which the regularized version of SD generates a unique solution? (b) Is there a way to modify the SD algorithm so that a user can trade-off solution times with solution quality? The second issue addresses the scalability of SD for very large scale problems for which computational resources may be limited and the user may be willing to accept solutions that are “nearly optimal”. We show that by using bootstrapping (re-sampling) the regularized SD algorithm can be accelerated without significant loss of optimality. We report computational results that demonstrate the viability of this approach.