TY - GEN
T1 - Continuous-time algorithm for distributed nonsmooth optimization via decomposition design
AU - Zhou, Hongbing
AU - Zeng, Xianlin
AU - Hong, Yiguang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/4
Y1 - 2017/8/4
N2 - This paper is concerned with a class of distributed nonsmooth convex constrained optimization problems with set constraints. The objective function is a sum of local convex functions, which are not necessarily differentiable. A new distributed continuous-time gradient-based algorithm using the decomposition design is explicitly constructed to solve the distributed optimization problem. Rigorous proofs using nonsmooth convex optimization theory and stability theory of differential inclusions are presented. A numerical simulation is conducted to show the efficacy of the proposed algorithm.
AB - This paper is concerned with a class of distributed nonsmooth convex constrained optimization problems with set constraints. The objective function is a sum of local convex functions, which are not necessarily differentiable. A new distributed continuous-time gradient-based algorithm using the decomposition design is explicitly constructed to solve the distributed optimization problem. Rigorous proofs using nonsmooth convex optimization theory and stability theory of differential inclusions are presented. A numerical simulation is conducted to show the efficacy of the proposed algorithm.
KW - Distributed constrained optimization
KW - continuous-time algorithm
KW - decomposition design
KW - nonsmooth objective function
UR - https://www.scopus.com/pages/publications/85029903112
U2 - 10.1109/ICCA.2017.8003056
DO - 10.1109/ICCA.2017.8003056
M3 - Conference contribution
AN - SCOPUS:85029903112
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 180
EP - 185
BT - 2017 13th IEEE International Conference on Control and Automation, ICCA 2017
PB - IEEE Computer Society
T2 - 13th IEEE International Conference on Control and Automation, ICCA 2017
Y2 - 3 July 2017 through 6 July 2017
ER -