Yawen Cui

In this paper, we study nonconvex equality-constrained optimization problems in which only stochastic first-order approximations of the objective and constraint functions are available. Owing to the stochasticity in both objective and constraints, most existing stochastic first-order methods incur relatively high oracle complexity, particularly in terms of stochastic constraint function evaluations. To address this issue, we … Read more

In this paper, we study a penalty method for stochastic equality-constrained optimization, where both the objective and constraints are expressed in general expectation form. We introduce a novel adaptive strategy for updating the penalty parameter, guided by iteration progress to balance reductions in the penalty function with improvements in constraint violation, while each penalty subproblem … Read more

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