We introduce a fusion of GPU accelerated primal heuristics for Mixed Integer Programming. Leveraging GPU acceleration enables exploration of larger search regions and faster iterations. A GPU-accelerated PDLP serves as an approximate LP solver, while a new probing cache facilitates rapid roundings and early infeasibility detection. Several state-of-the-art heuristics, including Feasibility Pump, Feasibility Jump, and Fix-and-Propagate, are further accelerated and enhanced. The combined approach of these GPU-driven algorithms yields significant improvements over existing methods, both in the number of feasible solutions and the quality of objectives by achieving 221 feasible solutions and 22% objective gap in the MIPLIB2017 benchmark on a presolved dataset.

翻译：本文提出了一种融合GPU加速的混合整数规划原始启发式算法框架。利用GPU加速能够探索更大的搜索区域并实现更快的迭代速度。其中，GPU加速的PDLP作为近似线性规划求解器，而新型探测缓存机制则支持快速取整与早期不可行性检测。包括可行性泵、可行性跳跃以及固定传播在内的多种先进启发式算法均得到进一步加速与增强。这些GPU驱动算法的组合策略在可行解数量与目标函数质量方面均显著优于现有方法：在预求解后的MIPLIB2017基准测试数据集上，该框架获得了221个可行解，并实现了22%的目标函数间隙。