A Neurodynamic Approach to Nonsmooth Quaternion Distributed Convex Optimization with Inequality and Affine Equality Constraints

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Li, Guocheng, Luan, Linhua, Qin, Sitian, Feng, Jiqiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive controller; Color; Color imagery; Convex analysis; Convexity; Data mining; Image processing; Inequality; Linear programming; multi-agent system; Multi-agent systems; Multiagent systems; Neurodynamics; nonsmooth; Optimization; quaternion distributed optimization problem (QDOP); quaternion-valued neurodynamic approach (QNA); Quaternions; Solid modeling; Topology
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3180366

Quaternions have appeared in many practical fields, such as image processing and data mining and so on. This paper focuses on designing an efficient quaternion-valued neurodynamic approach (QNA) based on multi-agent systems to solve nonsmooth convex quaternion distributed optimization problems (QDOPs) with inequality and affine equality constraints. Each agent in the system cooperatively solves the minimum of the global objective function through the information of itself and its neighbors. At first, the related convex analysis of the quaternion field are given, which provides a theoretical basis for solving the nonsmooth QDOP. Then the considered QDOP is equivalently transformed by using the connectivity of the communication topology. After that, a distributed QNA is presented, where the adaptive controller is introduced to ensure that the penalty terms with respect to the inequality constraints can self-adjust according to the local states. It is shown that all agents reach a consensus while obtaining the optimal solution to the related QDOP. Finally, a numerical example and an application in dictionary sparse representation of color images based on quaternion are realized to intuitively describe the effectiveness and practical significance of the proposed QNA.