000256724 001__ 256724
000256724 005__ 20190317001027.0
000256724 037__ $$aARTICLE
000256724 245__ $$aData-Driven Chance Constrained Programs over Wasserstein Balls
000256724 260__ $$c2018
000256724 269__ $$a2018
000256724 336__ $$aJournal Articles
000256724 500__ $$aAvailable from Optimization Online
000256724 520__ $$aWe provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the $1$-norm or the $\infty$-norm, the cone is the nonnegative orthant, and the chance constrained program can be reformulated as a mixed-integer linear program. Using our reformulation, we show that two popular approximation schemes based on the conditional-value-at-risk and the Bonferroni inequality can perform poorly in practice and that these two schemes are generally incomparable with each other.
000256724 6531_ $$aDistributionally robust optimization
000256724 6531_ $$aAmbiguous chance constraints
000256724 6531_ $$aWasserstein distance
000256724 700__ $$aChen, Zhi
000256724 700__ $$aKuhn, Daniel
000256724 700__ $$aWiesemann, Wolfram
000256724 773__ $$t-
000256724 8560_ $$fdaniel.kuhn@epfl.ch
000256724 85641 $$uhttp://www.optimization-online.org/DB_HTML/2018/06/6671.html
000256724 909C0 $$xU12788$$pRAO$$malexandra.vonschack@epfl.ch$$0252496
000256724 909CO $$qGLOBAL_SET$$pCDM$$particle$$ooai:infoscience.epfl.ch:256724
000256724 960__ $$adaniel.kuhn@epfl.ch
000256724 961__ $$apierre.devaud@epfl.ch
000256724 973__ $$aEPFL$$sSUBMITTED$$rREVIEWED
000256724 980__ $$aARTICLE
000256724 981__ $$aoverwrite