- A Tractable Approach for designing Piecewise Affine Policies in Dynamic Robust Optimization Aharon Ben-Tal(abentalie.technion.ac.il) Omar El Housni(oe2148columbia.edu) Vineet Goyal(vg2277columbia.edu) 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 the map 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. Furthermore, the performance of our policy is significantly better than the affine policy for many important uncertainty sets 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 $O(\sqrt m)$. To the best of our knowledge, this is the first tractable approach for designing piecewise affine policies with significantly improved theoretical performance guarantees. Keywords: Robust Optimization, Adaptive Optimization, Approximation algorithms Category 1: Robust Optimization Citation: Submitted to Math Programming Download: [PDF]Entry Submitted: 07/24/2016Entry Accepted: 07/24/2016Entry Last Modified: 07/24/2016Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.