The paper introduces a fully distributed continuous‑time algorithm for solving Generalized Nash Equilibrium Problems (GNEPs) with shared linear equality constraints. Unlike existing methods that require exchanging Lagrange multipliers, this approach converges to any GNE without multiplier communication, thereby reducing communication overhead and enhancing privacy. Discrete‑time variants are also presented and the method is demonstrated on a multi‑robot placement task.
By Shao-An Yin, Mingyi Hong, Nicola Elia
arXiv:2504.09192v5 Announce Type: replace
Abstract: The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncer...
By Zhiyong Wang
arXiv:2004. 06321v2 Announce Type: replace Abstract: We study the sequential batch learning problem in linear contextual bandits with finite action sets, where the decision maker is constrained to split incoming individuals into (at most) a fixed number of batches and can only observe outcomes for the individuals within a batch at the batch's end.
By Yanjun Han, Zhengqing Zhou, Zihao Hu, Jose Blanchet, Peter W. Glynn, Yinyu Ye, Zhengyuan Zhou
arXiv:2603. 27803v2 Announce Type: replace Abstract: We provide a distributed online algorithm for multi-agent submodular maximization under communication delays.
By Zirui Xu, Vasileios Tzoumas
arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.
By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm
arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.
By Yin Huang, Qingsong Liu, Jie Xu