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Stochastic-Constrained Stochastic Optimization with Markovian Data
This paper considers stochastic-constrained stochastic optimization where the stochastic constraint is to satisfy that the expectation of a random function is below a certain threshold.
Online Convex Optimization with Stochastic Constraints: Zero Constraint Violation and Bandit Feedback
We propose a variant of the drift-plus-penalty algorithm that guarantees $O(\sqrt{T})$ expected regret and zero constraint violation, after a fixed number of iterations, which improves the vanilla drift-plus-penalty method with $O(\sqrt{T})$ constraint violation.
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