Abstract
The vast majority of the literature on stochastic semidefinite programs (stochastic SDPs) with recourse is concerned with risk-neutral models. In this paper, we introduce mean-risk models for stochastic SDPs and study structural properties as convexity and (Lipschitz) continuity. Special emphasis is placed on stability with respect to changes of the underlying probability distribution. Perturbations of the true distribution may arise from incomplete information or working with (finite discrete) approximations for the sake of computational efficiency. We discuss extended formulations for stochastic SDPs under finite discrete distributions, which turn out to be deterministic (mixed-integer) SDPs that are (almost) block-structured for many popular risk measures.
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The authors gratefully acknowledge the support of the German Research Foundation (DFG) within the collaborative research center TRR 154 “Mathematical Modeling, Simulation and Optimization Using the Example of Gas Networks”.
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Claus, M., Schultz, R., Spürkel, K. et al. On Risk-Averse Stochastic Semidefinite Programs with Continuous Recourse. Vietnam J. Math. 47, 865–879 (2019). https://doi.org/10.1007/s10013-019-00368-0
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DOI: https://doi.org/10.1007/s10013-019-00368-0