ISyE Seminar Series: Andrew Lim

"Calibration of Robust Empirical Optimization Problems"

Presentation by Professor Andrew Lim
Department of Analytics and Operations/Department of Finance
National University of Singapore

Wednesday, September 4
3:15pm - Refreshments, Lind Hall 305
3:30pm - Graduate Seminar, Lind Hall 305



Lim will discuss recent results on the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the “robustness parameter” for worst-case maximization problems with concave reward functions. Building on the intuition that robust optimization reduces the sensitivity to model misspecification by controlling the spread of the reward distribution, Lim will show that the first-order benefit of a “little bit of robustness” is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller. One implication is that a substantial reduction in the variance of the out-of-sample reward (i.e., sensitivity of the expected reward to model misspecification) is possible at little cost if the robustness parameter is properly calibrated. To this end, Lim will introduce the notion of a robust mean-variance frontier to select the robustness parameter and show that it can be approximated using resampling methods like the bootstrap. Examples show that robust solutions resulting from “open loop” calibration methods (e.g., selecting a 90% confidence level regardless of the data and objective function) can be very conservative out-of-sample, while selecting an ambiguity parameter that optimizes an estimate of the out-of-sample expected reward (e.g., via the bootstrap) with no regard for the variance is often insufficiently robust. Lim will also explain why the out-of-sample expected reward generated by the solution of a worst-case problem can sometimes exceed that of a sample-average optimizer.



Andrew Lim is a Professor in the Department of Analytics and Operations and the Department of Finance at the National University of Singapore. Prior to that, he was a faculty member in the Department of Industrial Engineering and Operations Research at the University of California (Berkeley). He is a past recipient of an NSF CAREER Award and has served on the editorial boards of a number of journals including Operations Research, Management Science, and the IEEE Transactions on Automatic Control. He has a PhD from the Australian National University. His research interests are in the areas of stochastic control and optimization, decision making under uncertainty, robust optimization, and financial engineering.


Seminar Video:

Start date
Wednesday, Sept. 4, 2019, 3:15 p.m.

Lind Hall
Room 305