Title	Data-Driven Chance Constrained Programs over Wasserstein Balls
Creator	Wiesemann, Wolfram
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-01-14T16:12
Description	We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the $1$-norm or the $\infty$-norm, the cone is the nonnegative orthant, and the chance constrained program can be reformulated as a mixed-integer linear program. Using our reformulation, we show that two popular approximation schemes based on the conditional-value-at-risk and the Bonferroni inequality can perform poorly in practice and that these two schemes are generally incomparable with each other.
Extent	41.0 minutes
Subject	Mathematics; Operations research, mathematical programming; Computer science; Applied computer science
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Imperial College London
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2019-07-14
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0379829
URI	http://hdl.handle.net/2429/70976
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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Data-Driven Chance Constrained Programs over Wasserstein Balls Wiesemann, Wolfram

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