TY  - CPAPER
AU  - Lin, Y
AU  - Shames, I
AU  - Nesic, D
Y2  - 2020/11/17
Y1  - 2020/03/12
SN  - 9781728113982
SN  - 2576-2370
UR  - http://hdl.handle.net/11343/251784
AB  - We study a class of distributed optimization problems of minimizing the sum of potentially non-differentiable convex objective functions (without requiring strong convexity). A novel approach to the analysis of asynchronous distributed optimization is developed. An iterative algorithm based on dual decomposition and block coordinate ascent is implemented in an edge based manner. We extend available results in the literature by allowing multiple and potentially overlapping blocks to be updated at the same time with non-uniform probabilities assigned to different blocks. Sublinear convergence with probability one is proved for the algorithm under the aforementioned weak assumptions. A numerical example is provided to illustrate the effectiveness of the algorithm.
LA  - English
PB  - IEEE
T1  - Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Ascent
DO  - 10.1109/CDC40024.2019.9029518
IS  - Proceedings of the ... IEEE Conference on Decision & Control / IEEE Control Systems Society. IEEE Conference on Decision & Control
VL  - 2019-December
SP  - 6380-6385
OP  - DP170104099
L1  - /bitstream/handle/11343/251784/Lin_Asynchronous%20distributed%20optimization%20via%20dual%20decomposition%20and%20block%20coordinate%20ascent.pdf?sequence=2&isAllowed=y
ER  -