A Copositive Perspective on Two-Stage Adjustable Robust Linear Optimization with Uncertain Right-Hand Sides

Friday, October 6, 2017 - 9:30am - 11:00am
Lind 409
Guanglin Xu (University of Minnesota, Twin Cities)
We study two-stage adjustable robust linear programming in which the right-hand sides are uncertain and belong to a convex, compact uncertainty set. This problem is NP-hard, and the affine policy is a popular, tractable approximation. We prove that under standard and simple conditions, the two-stage problem can be reformulated as a copositive optimization problem, which in turn leads to a class of tractable, semidefinite-based approximations that are at least as strong as the affine policy. We investigate several examples from the literature demonstrating that our tractable ap- proximations significantly improve the affine policy. In particular, our approach solves exactly in polynomial time a class of instances of increasing size for which the affine policy admits an arbitrarily large gap.