发表机构
London Business School（伦敦商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PaNGEA，一种GPU友好的混合整数优化原始启发式算法，通过结合线性松弛与局部搜索并利用GPU批处理并行探索多个子问题，在283个实例上相比CPU减少8-18%间隙积分，多节点并行再减少12-19%。
AI 中文摘要
寻找高质量可行解的原始启发式算法是混合整数优化（MIO）求解器中的重要组成部分。近期GPU加速优化算法的进展显示了GPU加速在连续优化中的潜力。本文介绍了并行节点生成与探索算法（PaNGEA），一种面向GPU的MIO原始启发式算法。PaNGEA通过将线性松弛求解与专为GPU高效批量执行设计的局部搜索过程相结合来探索受限子问题。此外，不依赖单一启发式进行迭代变量固定，而是利用GPU的批处理能力并行生成并探索多个子问题。PaNGEA以两种方式利用GPU能力。第一，在283个MIPcc26和MIPLIB实例上，在GPU上实现单节点原始启发式相比CPU版本平均减少8-18%的间隙积分。第二，并行生成和探索多个节点进一步将平均间隙积分减少12-19%。
英文摘要
Primal heuristics for finding high-quality feasible solutions are an important component in mixed-integer optimization (MIO) solvers. Recent advances in GPU-accelerated optimization algorithms show the potential of GPU acceleration for continuous optimization. In this paper, we introduce the Parallel Node Generation and Exploration Algorithm (PaNGEA), a GPU-friendly MIO primal heuristic. PaNGEA explores restricted subproblems by combining linear-relaxation solves with a local-search procedure designed for efficient batched execution on GPUs. In addition, instead of relying on a single heuristic for iterative variable fixing, we leverage GPU batching capabilities to generate and explore multiple subproblems in parallel. PaNGEA leverages GPU capabilities in two ways. First, on 283 MIPcc26 and MIPLIB instances, implementing a single-node primal heuristic on the GPU reduces the average gap integral by 8-18% relative to its CPU counterpart. Second, generating and exploring multiple nodes in parallel further reduces the average gap integral by 12-19%.
