报告人：秦泗甜教授，哈尔滨工业大学（威海）

报告题目：A Novel Neurodynamic Approach to Constrained Complex-Variable Pseudoconvex Optimization

摘要：Complex-variable pseudoconvex optimization has been widely used in numerous scientific and engineering optimization problems. A neurodynamic approach is proposed in this paper for complex-variable pseudoconvex optimization problems subject to bound and linear equality constraints. An efficient penalty function is introduced to guarantee the boundedness of the state of the presented neural network, and make the state enter the feasible region of the considered optimization in finite time and stay there thereafter. The state is also shown to be convergent to an optimal point of the considered optimization. Compared with other neurodynamic approaches, the presented neural network does not need any penalty parameters, and has lower model complexity. Furthermore, some additional assumptions in other existing related neural networks are also removed in this paper, such as the assumption that the objective function is lower bounded over the equality constraint set and so on.

报告时间：2021年3月26日 16:30-17:30

报告地点：理学楼1001

报告人简介：秦泗甜、博士、教授、博士生导师。主要研究方向：神经动力学优化、神经网络动力学行为，多智能体与分布式优化。主持国家自然科学基金3项。中科院一区或发表学术论文近10篇，JCR一区期刊发表学术论文10余篇，出版专著2部，担任下列国际主流杂志《IEEE Transactions on Automatic Control》，《IEEE Transactions on Neural Networks and Learning Systems,IEEE Transactions on Cybernetics》，《IEEE Transactions on  IndustrialInformatics》, 《IEEE Transactions on Systems, Man and Cybernetics: Systems,Neural Networks》，《Neurocomputing》等审稿人。