**报告题目:** Distributed Consensus Optimization in Multiagent Networks With Time-Varying Directed Topologies and Quantized Communication

**报告人：李华青副教授， 西南大学**

**Abstract**

This paper considers solving a class of optimization problems which are modeled as the sum of all agents’** **convex cost functions and each agent is only accessible to its** **individual function. Communication between agents in multiagent networks is assumed to be limited: each agent can only** **interact information with its neighbors by using time-varying** **communication channels with limited capacities. A technique** **which overcomes the limitation is to implement a quantization** **process to the interacted information. The quantized information is first encoded as a binary sequence at the side of each** **agent before sending. After the binary sequence is received by** **the neighboring agent, corresponding decoding scheme is utilized** **to resume the original information with a certain degree of error** **which is caused by the quantization process. With the availability** **of each agent’s encoding states (associated with its out-channels)** **and decoding states (associated with its in-channels), we devise** **a set of distributed optimization algorithms that generate two** **iterative sequences, one of which converges to the optimal solution and the other of which reaches to the optimal value. We** **prove that if the parameters satisfy some mild conditions, the** **quantization errors are bounded and the consensus optimization** **can be achieved. How to minimize the number of quantization** **level of each connected communication channel in fixed networks** **is also explored thoroughly. It is found that, by properly choosing system parameters, one bit information exchange suffices to** **ensure consensus optimization. Finally, we present two numerical** **simulation experiments to illustrate the efficacy of the algorithms** **as well as to validate the theoretical findings.

**报告题目：**Event-Triggered Communication and Data Rate Constraint for Distributed Optimization of Multiagent Systems

**报告人：李华青副教授，西南大学**

**Abstract**

This paper is concerned with solving a large category of convex optimization problems using a group of agents, each only being accessible to its individual convex cost function. The optimization problems are modeled as minimizing the sum of all the agents’ cost functions. The communication process between agents is described by a sequence of time-varying yet balanced directed graphs which are assumed to be uniformly strongly connected. Taking into account the fact that the communication channel bandwidth is limited, for each agent we introduce a vector-valued quantizer with finite quantization levels to preprocess the information to be exchanged. We exploit an event-triggered broadcasting technique to guide information exchange, further reducing the communication cost of the network. By jointly designing the dynamic event-triggered encoding–decoding schemes and the event-triggered sampling rules (to analytically determine the sampling time instant sequence for each agent), a distributed subgradient descent algorithm with constrained information exchange is proposed. By selecting the appropriate quantization levels, all the agents’ states asymptotically converge to a consensus value which is also the optimal solution to the optimization problem, without committing saturation of all the quantizers. We find that one bit of information exchange across each connected channel can guarantee that the optimization problem can be exactly solved. Theoretical analysis shows that the event-triggered subgradient descent algorithm with constrained data rate of networks converges at the rate of *O**(*ln *t/*√*t**)*. We supply a numerical simulation experiment to demonstrate the effectiveness of the proposed algorithm and to validate the correctness of theoretical results.

**李华青副教授概况：**

李华青，博士，副教授，硕士生导师。澳大利亚悉尼大学博士后，新加坡南洋理工大学博士后，美国德州农工大学卡塔尔分校访问学者。主要从事多智能体协同控制，分布式优化理论及其应用（包括智能电网，无线传感器网络等），复杂系统与复杂网络方面的研究。目前是SCI期刊Neural Computing and Applications的副主编，SCI期刊IEEE ACCESS编委, 美国《数学评论》(Mathematical Reviews)评论员。主持国家自然科学基金青年项目1项，博士后基金面上项目一等资助1项，重庆市自然科学基金面上项目1项，中央高校基本业务费项目2项。据GOOGLE学术统计，研究成果被引用1000余次，H指数为20。近5年以第一编辑在包括IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Systems Man and Cybernetics: Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems-I: Regular Papers, IEEE Transactions on Circuits and Systems-II: Express Briefs, Information Sciences和Neural Networks等SCI收录的国际著名期刊上发表学术论文30多篇，其中IEEE Transactions系列论文11篇，有三篇论文入选工程领域ESI高被引论文。是多个国际期刊，如IEEE TAC，IEEE TNNLS，IEEE TCAS-I，IEEE TCAS-II，IEEE TSMC-A，IEEE TSMC-B, IET-CTA，Information Sciences, Neural Networks等和IEEE WCCI, ICONIP, IJCNN等多个著名国际学术会议的审稿人和国家自然科学基金委同行评议专家。