Tag
This paper introduces PIAC, a framework that improves LLM-based automatic construction of parallel algorithm portfolios by using a potential-gain metric that eliminates the need for reference solutions and by leveraging LLMs to generate diverse instance mutators. It consistently outperforms existing LLM-ACP baselines on TSP and CVRP, achieving up to 19.76% relative improvement.
A paper by Chatterjee, Ghosh, Gurjar, Raj, and Thierauf claims to show that the Bipartite Matching problem is in the complexity class NC, resolving a central open problem from the 1980s in parallel algorithms and derandomization.
An introductory resource on parallel algorithms, covering fundamental concepts and techniques, from Carnegie Mellon University.