A primal-dual framework with adaptive dual regularizer achieves O(√T) regret and O(√T log T) constraint violation for constrained OCO without Slater's condition under stochastic constraints, with extensions to adversarial constraints and strongly convex losses.
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Constrained Online Convex Optimization without Slater's Condition
A primal-dual framework with adaptive dual regularizer achieves O(√T) regret and O(√T log T) constraint violation for constrained OCO without Slater's condition under stochastic constraints, with extensions to adversarial constraints and strongly convex losses.