Asynchronous Distributed Optimization via Dual Decomposition and Block Coordinate Ascent
Y Lin, I Shames, D Nesic
Proceedings of the ... IEEE Conference on Decision & Control / IEEE Control Systems Society. IEEE Conference on Decision & Control | IEEE | Published : 2020
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 numerica..View full abstract
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Awarded by Australian Research Council
This work was supported by the Australian Research Council under the Discovery Project DP170104099.