In this work, the novel Distributed Bayesian (D-Bay) algorithm is presented for solving multi-agent problems within the continuous Distributed Constraint Optimization Problem (DCOP) framework. This framework extends the classical DCOP framework towards utility functions with continuous domains. Traditional DCOP solvers discretize the continuous domains, which increases the problem size exponentially. D-Bay overcomes this problem by utilizing Bayesian optimization for the adaptive sampling of variables to avoid discretization entirely. We theoretically show that D-Bay converges to the global optimum of the DCOP for Lipschitz continuous utility functions. The performance of the algorithm is evaluated empirically based on the sample efficiency. The proposed algorithm is compared to a centralized approach with equidistant discretization of the continuous domains for the sensor coordination problem. We find that our algorithm generates better solutions while requiring less samples.
Computational acceleration strategies for large-scale energy system optimization: a comparative study of GPU-accelerated and distributed-memory solvers
Freie Universität Berlin（柏林自由大学）；Deutsches Zentrum für Luft- und Raumfahrt e.V.（德国航空航天中心）；Zuse Institute Berlin（祖斯研究所柏林）；GAMS Software GmbH（GAMS软件有限公司）
CLAIRE: A distributed-memory solver for constrained large deformation diffeomorphic image registration
University of Houston（休斯顿大学）；University of California, Berkeley（加州大学伯克利分校）；University of Pennsylvania（宾夕法尼亚大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Overview of Bayesian Solvers in EEG Distributed Source Models: Prior Selection, Algorithmic Implementation, and Depth Bias Reduction
Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息与通信科学学院）；Institute for Mathematical Innovation, University of Bath（巴斯大学数学创新研究所）
N2N: A Parallel Framework for Large-Scale MILP under Distributed Memory
Shenzhen Research Institute of Big Data（深圳大数据研究 institute）；Theory Lab, Central Research Institute, 2012 Labs, Huawei Technologies Co. Ltd.（华为技术有限公司理论实验室，中央研究所，2012实验室）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Finite-Time Distributed Linear Equation Solver for Minimum $l_1$ Norm Solutions
School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）；Hangzhou Dianzi University（杭州电子科技大学）；The Australian National University（澳大利亚国立大学）；Data-61 CSIRO (formerly NICTA)（CSIRO数据六部（原NICTA））
Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（奥登计算工程与科学研究所，德克萨斯大学奥斯汀分校）；Dept of Biomedical Engineering, The University of Texas at Austin（生物医学工程系，德克萨斯大学奥斯汀分校）
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization
Pumas-AI Inc.（Pumas-AI公司）；The University of Sydney Business School（悉尼大学商学院）；Department of Architecture, Massachusetts Institute of Technology（麻省理工学院建筑系（注：因要求机构粒度到大学级别，此处实际机构应为麻省理工学院））
Distributed Fractional Bayesian Learning for Adaptive Optimization
Department of Control Science and Engineering, Tongji University（控制科学与工程系，同济大学）；State Key Laboratory of Autonomous Intelligent Unmanned Systems（自主智能无人系统国家重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education（智能自主系统前沿科学中心，教育部）；Shanghai Institute of Intelligent Science and Technology, Tongji University（上海智能科学与技术研究院，同济大学）
