David L. Woodruff, University of California, Davis

Abstract

Recent work by Eckstein and Combettes resulted in development of an algorithm for multi-stage, convex stochastic optimization problems with uncertain input data expressed as a set of scenarios. The algorithm is called Asynchronous Projective Hedging (APH). In this talk we describe computational experience with this algorithm primarily based on two well-known problems from the stochastic programming literature: saphir and ssn. We explore various tradeoffs such as computational resources vs. wall-clock vs. solution quality as well primal versus dual solution quality.

Co-authors: Jonathan Eckstein, Rutgers and Jean-Paul Watson, Sandia

Slides    Video