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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 sampl
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