JFR: 一种高效的跳跃前沿松弛策略用于Bellman-Ford
JFR: An Efficient Jump Frontier Relaxation Strategy for Bellman-Ford
AI总结：
JFR是一种基于Bellman-Ford的高效最短路径计算优化策略，通过前沿收缩和多跳传播减少松弛操作，提升计算效率和稳健性。
摘要：
我们提出了JFR，一种基于Bellman-Ford的优化框架，利用前沿收缩和抽象多跳跳跃传播来加速最短路径计算，同时严格保持正确性。JFR在稀疏、密集和负边图中实现了显著的松弛操作减少，范围从-31%到99%，确保了在对抗性或高度连接拓扑下的稳健性能。在具有多达N=10,000个节点和55,000,000条边的超大规模图上，JFR保持了强大的操作减少，并与SPFA-SLF相比具有可比或改进的运行时间，展示了在图规模和密度上的持续稳健性。较低的松弛次数意味着减少的内存访问开销和计算努力；这种标准化的工作减少突显了JFR在需要高吞吐量或节能操作场景中的适用性。未来的工作将聚焦于整合高性能队列结构、自适应前沿策略和缓存感知技术，以进一步减少常数因子开销，并充分实现JFR的实用运行时间潜力。
英文摘要：
We propose JFR, a Bellman-Ford-based optimization framework leveraging frontier contraction and abstract multi-hop jump propagation to accelerate shortest-path computation while strictly preserving correctness. JFR achieves substantial reductions in relaxation operations, ranging from -31 to 99 percent, across sparse, dense, and negative-edge graphs, ensuring robust performance even under adversarial or highly connected topologies. On ultra-large graphs with up to N=10,000 nodes and 55,000,000 edges, JFR maintains strong operational reductions and comparable or improved runtime relative to SPFA-SLF, demonstrating consistent robustness across graph size and density. Lower relaxation counts imply reduced memory-access overheads and computational effort; this normalized work reduction highlights JFR's suitability for scenarios requiring high throughput or energy-conscious operation. Future work focuses on integrating high-performance queue structures, adaptive frontier strategies, and cache-aware techniques to further reduce constant-factor overheads and fully realize JFR's practical runtime potential.
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