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!"$#%&"')(10 2 35476 8 94A@ B C 3ED5F GIH PRQSQUTWVYX

Summary:  !"$#%&"')(10
2 35476 8 94A@ B C 3ED5F GIH PRQSQUTWVYX
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dfehgbesgikjmlsnpo n%qyrsnIn%qutSvxwrqynzR{}|~w
1 %p1p %mh %ÿ
źǼƣüʣǼƣmr %p %pҿps̡֧פp$ҩժҤ
Chance-Constrained Semidefinite Programming
K. A. Ariyawansa
and Yuntao Zhu
Semidefinite programs are a class of optimization problems that have been the focus of in-
tense research during the past fifteen years. Semidefinite programs extend linear programs,
and both are defined using deterministic data. However, uncertainty is naturally present in
applications leading to optimization problems. Stochastic linear programs with recourse have
been studied since the fifties as a way to deal with uncertainty in data defining linear pro-
grams. Recently, the authors have defined an analogous extension of semidefinite programs
termed stochastic semidefinite programs with recourse to deal with uncertainty in data defining
semidefinite programs. A prominent alternative for handling uncertainty in data defining linear
programs is chance-constrained linear programming. In this paper we introduce an analogous


Source: Ariyawansa, Ari - Department of Mathematics, Washington State University


Collections: Mathematics