In this paper, the distributed strongly convex optimization problem is studied with spatio-temporal compressed communication and equality constraints. For the case where each agent holds an distributed local equality constraint, a distributed saddle-point algorithm is proposed by employing distributed filters to derive errors of the transmitted states for spatio-temporal compression purposes. It is shown that the resulting distributed compressed algorithm achieves linear convergence. Furthermore, the algorithm is generalized to the case where each agent holds a portion of the global equality constraint, i.e., the constraints across agents are coupled. By introducing an additional design freedom, the global equality constraint is shown to be equivalent to the one where each agent holds an equality constraint, for which the proposed distributed compressed saddle-point algorithm can be adapted to achieve linear convergence. Numerical simulations are adopted to validate the effectiveness of the proposed algorithms.
Distributed Nonlinear Equality-Constrained Optimization via Feedback Linearization and Singular Perturbation
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Australia Centre for Field Robotics, The University of Sydney（悉尼大学澳大利亚野外机器人中心）
Distributed Kalman Filters with State Equality Constraints: Time-based and Event-triggered Communications
KTH Royal Institute of Technology（皇家理工学院）；Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统与控制重点实验室）；Rocket Force University of Engineering（火箭军工程大学）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（香港科技大学电子及计算机工程系）
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation
DC-DistADMM: ADMM Algorithm for Constrained Distributed Optimization over Directed Graphs
本文提出了一种求解具有公共决策变量、局部线性等式和不等式约束以及集合约束的多智能体分布式优化问题的算法，该算法具有收敛速度保证。\textcolor{black}{The algorithm accrues all the benefits of the Alternating Direction Method of Multipliers (ADMM) approach}。它还克服了现有方法的局限性，凸优化问题的线性不等式，等式和集合约束，允许有向通信拓扑。此外，该算法可以分布式综合。所开发的算法具有：（i）当各个函数是凸的但不一定是可微的时，$O(1/k)$的收敛速率，其中$k$是迭代计数器，以及（ii）当目标函数是光滑的并且在最优解处被限制为强凸的时，收敛到最优解的任意小邻域的几何速率。该算法的有效性进行评估，通过比较与国家的最先进的约束优化算法在解决一个约束分布式$\ell_1$正则化逻辑回归问题，和无约束优化算法在解决一个$\ell_1$正则化Huber损失最小化问题。此外，该算法的性能与其他算法在文献中，利用多个通信步骤的比较。
Optimizing Reweighted Belief Propagation for Distributed Likelihood Fusion Problems
Zenuity AB（Zenuity公司）；ICTEAM Institute/CORE, Université Catholique de Louvain（天主教鲁汶大学ICTEAM研究所/CORE中心）；Electrical Engineering Department, Chalmers University of Technology（查尔姆斯理工大学电气工程系）
