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Performance Bound for Random Programs


P. Mohajerin Esfahani

Invited talk, Risk Analytics and Optimization (RAO), Swiss Federal Institute of Technology at Lausanne (EPFL)

In this talk we consider the Scenario Convex Program (SCP) for two classes of optimization problems that are not tractable in general: Robust Convex Programs (RCPs) and Chance-Constrained Programs (CCPs). We establish a probabilistic bridge from the optimal value of SCP to the optimal values of RCP and CCP in which the uncertainty takes values in a general, possibly infinite dimensional, metric space. We then extend our results to a certain class of non-convex problems that includes, for example, binary decision variables. We demonstrate the applicability of our results on the problem of fault detection in power systems, motivated by a cyber-physical attack on the vulnerabilities introduced by the interaction with IT infrastructure.


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