Abstract
Semidefinite programs are a class of optimization problems that have been studied extensively during the past 15 years. Semidefinite programs are naturally related to linear programs, and both are defined using deterministic data. Stochastic programs were introduced in the 1950s as a paradigm for dealing with uncertainty in data defining linear programs. In this paper, we introduce stochastic semidefinite programs as a paradigm for dealing with uncertainty in data defining semidefinite programs.
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The work of this author was supported in part by the U.S. Army Research Office under Grant DAAD 19-00-1-0465. The material in this paper is part of the doctoral dissertation of this author in preparation at Washington State University.
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Ariyawansa, K.A., Zhu, Y. Stochastic semidefinite programming: a new paradigm for stochastic optimization. 4OR 4, 239–253 (2006). https://doi.org/10.1007/s10288-006-0016-2
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DOI: https://doi.org/10.1007/s10288-006-0016-2