An Exact Penalty Method for Stochastic Equality-Constrained Optimization

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

GPU-Accelerated First-Order Method with Randomized Sampling for Binary Integer Programs

We present a scalable, GPU-accelerated algorithmic framework for large binary integer programs that operates end-to-end with minimal synchronization overhead. The proposed method combines a first-order routine that guides search in the continuous relaxation with a randomized, feasibility-aware sampling module that generates batched binary candidates. We establish a residual convergence guarantee for the first-order component in … Read more