A Tractable Approach for designing Piecewise Affine Policies in Two-stage Adjustable Robust Optimization

TL;DR

This paper introduces a new framework for designing piecewise affine policies in two-stage robust optimization, approximating complex uncertainty sets with a dominating simplex to enable efficient computation and improved performance over traditional affine policies.

Contribution

The authors propose a novel method to approximate uncertainty sets with a dominating simplex, allowing efficient computation of piecewise affine policies with better performance guarantees.

Findings

The proposed policy outperforms affine policies on various uncertainty sets.

The method enables polynomial-time computation for certain uncertainty sets.

The approach achieves an $O(m^{1/4})$-approximation for hypersphere uncertainty sets.

Abstract

We consider the problem of designing piecewise affine policies for two-stage adjustable robust linear optimization problems under right-hand side uncertainty. It is well known that a piecewise affine policy is optimal although the number of pieces can be exponentially large. A significant challenge in designing a practical piecewise affine policy is constructing good pieces of the uncertainty set. Here we address this challenge by introducing a new framework in which the uncertainty set is "approximated" by a "dominating" simplex. The corresponding policy is then based on a mapping from the uncertainty set to the simplex. Although our piecewise affine policy has exponentially many pieces, it can be computed efficiently by solving a compact linear program given the dominating simplex. Furthermore, we can find the dominating simplex in a closed form if the uncertainty set satisfies some symmetries and can be computed using a MIP in general. The performance of our policy is significantly better than the affine policy for many important uncertainty sets, such as ellipsoids and norm-balls, both theoretically and numerically. For instance, for hypersphere uncertainty set, our piecewise affine policy can be computed by an LP and gives a $O(m^{1/4})$-approximation whereas the affine policy requires us to solve a second order cone program and has a worst-case performance bound of $O(\sqrt m)$.