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Strong Duality in Risk-Constrained Nonconvex Functional Programming
We show that a wide class of risk-constrained nonconvex functional optimization problems exhibit strong duality, regardless of nonconvexity. We develop two novel results under distinct sets of assumptions, establishing strong duality over both decomposable policies (matching and extending prior work
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