A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems

Abstract

Distributed optimization problem (DOP) over multi-agent systems, which can be described by minimizing the sum of agents’ local objective functions, has recently attracted widespread attention owing to its applications in diverse domains. In this paper, inspired by penalty method and subgradient descent method, a continuous-time neurodynamic approach is proposed for solving a DOP with inequality and set constraints. The state of continuous-time neurodynamic approach exists globally and converges to an optimal solution of the considered DOP. Comparisons reveal that the proposed neurodynamic approach can not only resolve more general convex DOPs, but also has lower dimension of solution space. Additionally, the discretization of the neurodynamic approach is also introduced for the convenience of implementation in practice. The iteration sequence of discrete-time method is also convergent to an optimal solution of DOP from any initial point. The effectiveness of the neurodynamic approach is verified by simulation examples and an application in L1-norm minimization problem in the end.