Distributed Continuous-Time Constrained Convex Optimization via Nonsmooth Analysis

This paper investigates distributed continuous-time convex optimization problems for a network of agents subject to several constraints, i.e., local feasible constraints, local equality and inequality constraints, in which all convex functions may be not differentiable. For this problem, a distribut...

Full description

Saved in:
Bibliographic Details
Published in2018 IEEE International Conference on Real-time Computing and Robotics (RCAR) pp. 360 - 365
Main Authors Li, Xiuxian, Xie, Lihua, Hong, Yiguang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
Convergence
Convex functions
Distributed algorithms
Eigenvalues and eigenfunctions
Linear programming
Optimization
Online AccessGet full text

Cover

More Information
Summary:This paper investigates distributed continuous-time convex optimization problems for a network of agents subject to several constraints, i.e., local feasible constraints, local equality and inequality constraints, in which all convex functions may be not differentiable. For this problem, a distributed algorithm is proposed by the projection method, and it can be shown that Caratheodory solutions always exist for the proposed algorithm and all agents can ultimately reach an optimal solution for the optimization problem. Additionally, one more fully distributed sufficient condition is presented. To validate the theoretical result, a numerical example is also provided.
DOI:10.1109/RCAR.2018.8621707