Approximation Algorithms for 2-Stage Stochastic Optimization Problems

Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inability to know the future. In this survey, we outline some recent progress in the design of polynomialtime algorithms with performance guarantees on the quality of the solutions found for an important class of stochastic programming problems -- 2-stage problems with recourse. In particular, we show that for a number of concrete problems, algorithmic approaches that have been applied for their deterministic analogues are also effective in this more challenging domain. More specifically, this work highlights the role of tools from linear programming, rounding techniques, primal-dual algorithms, and the role of randomization more generally.

• Type: Conference
• Year: 2006
• Where: FSTTCS
• Authors: Chaitanya Swamy, David B. Shmoys
• Keywords: FSTTCS 2006, Optimization Problems, Software Engineering, Stochastic Optimization, Stochastic Programming Problems