Distributionally Robust Optimization (18w5102)

Arriving in Banff, Alberta Sunday, March 4 and departing Friday March 9, 2018

Organizers

(HEC Montréal)

(Ecole Polytechnique Federale de Laussane)

(Singapore University of Technology and Design)

Wolfram Wiesemann (Imperial College London)

Description

The Banff International Research Station will host the "Distributionally Robust Optimization" workshop from March 4th to March 9th, 2018.


Mathematical optimization problems traditionally model uncertainty via probability distributions. However, observable statistical data can often be explained by many strikingly different distributions. This "uncertainty about the uncertainty" poses a major challenge for optimization problems with uncertain parameters: estimation errors in the parameters' distribution are amplified through the optimization process and lead to biased (overly optimistic) optimization results as well as post-decision disappointment in out-of-sample tests.

The emerging field of distributionally robust optimization (DRO) seeks to propose new optimization models whose solutions are optimized against all distributions consistent with the given prior information. Recent findings have shown that many DRO models can be solved in polynomial time even when the corresponding stochastic models are intractable. DRO models also offer a more realistic account of uncertainty and mitigate the post-decision disappointment characteristic of stochastic models.

The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).