Title	A Tractable Format for Distributionally Robust Optimization
Creator	Sim, Melvyn
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2018-03-05T09:45
Description	We present a unified and tractable framework for distributionally robust optimization that could encompass a variety of statistical information including, among others things, constraints on expectation, scenario-wise expectations, Wasserstein metric, and uncertain probabilities defined by $phi$-divergence. To address a distributionally robust optimization problem with recourse, we introduce the scenario wise linear decision rule, which is based on the classical linear decision rule and can also be applied in situations where the recourse decisions are discrete. Based in this format, we has also developed a new Matlab based algebraic modeling language to model and solve distributionally robust optimization problems with recourse. This is a joint work with Zhi Chen and Peng Xiong.
Extent	36.0 minutes
Subject	Mathematics; Operations research, mathematical programming; Probability theory and stochastic processes; Operation research
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: NUS Business School
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-04-02
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0377743
URI	http://hdl.handle.net/2429/69401
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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A Tractable Format for Distributionally Robust Optimization Sim, Melvyn

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