Bipartite Matching

Bipartite matching, the task of optimally pairing elements from two sets based on edge weights, is a fundamental problem with broad applications in areas like online advertising and ridesharing. Current research focuses on improving the efficiency and robustness of online matching algorithms, particularly in scenarios with sequentially revealed information and deadlines, employing techniques such as graph neural networks (GNNs) and reinforcement learning (RL) to approximate optimal solutions or enhance existing algorithms like greedy approaches. These advancements aim to address the computational challenges posed by large-scale applications while also incorporating fairness considerations and providing theoretical guarantees on performance, impacting both theoretical computer science and practical deployment in real-time systems.