Semidefinite programs are a class of optimization problems that have been the focus of intense 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 programs. Recently, the authors have defined

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... 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 extension of semidefinite programs termed chance-constrained semidefinite programs for handling uncertainty in data defining semidefinite programs. AMS Subject Classifications: 90C15, 90C22, 90C05, 90C51