A hierarchy of policies for adaptive optimization
Name
Bertsimas_A hierarchy.pdf
Size
382.17 KB
Format
Adobe PDF
Checksum (MD5)
5560e62cf709e2ae05dc77d8d1d4d0ef
Author(s)
Iancu, Dan Andrei
Parrilo, Pablo A.
Bertsimas, Dimitris J
Alternative Title
A Hierarchy of Near-Optimal Policies for Multistage Adaptive Optimization
Date Issued
August 2011
Journal
IEEE Transactions on Automatic Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Bertsimas, Dimitris, Dan Andrei Iancu, and Pablo A. Parrilo. “A Hierarchy of Near-Optimal Policies for Multistage Adaptive Optimization.” IEEE Transactions on Automatic Control 56.12 (2011): 2809–2824.
Version
Author's final manuscript
Abstract
In this paper, we propose a new tractable framework for dealing with linear dynamical systems affected by uncertainty, applicable to multistage robust optimization and stochastic programming. We introduce a hierarchy of near-optimal polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. The approach yields a hierarchy parameterized by a single variable (the degree of the polynomial policies), which controls the trade-off between the optimality gap and the computational requirements. We evaluate our framework in the context of three classical applications-two in inventory management, and one in robust regulation of an active suspension system-in which very strong numerical performance is exhibited, at relatively modest computational expense.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/TAC.2011.2162878
