Proceedings ArticleDOI
Control approach to distributed optimization
Jing Wang,Nicola Elia +1 more
- pp 557-561
TLDR
The model proposed does not suffer from the limitation of diminishing step size in gradient searching and allows fast asymptotic convergence, and shows robustness to additive noise, which is a main curse for algorithms based on convex mixing or consensus.Abstract:
In this paper, we propose a novel computation model for solving the distributed optimization problem where the objective function is formed by the sum of convex functions available to individual agent. Our approach differentiates from the existing approach by local convex mixing and gradient searching in that we force the states of the model to the global optimal point by controlling the subgradient of the global optimal function. In this way, the model we proposed does not suffer from the limitation of diminishing step size in gradient searching and allows fast asymptotic convergence. The model also shows robustness to additive noise, which is a main curse for algorithms based on convex mixing or consensus.read more
Citations
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Journal ArticleDOI
Distributed Continuous-Time Convex Optimization on Weight-Balanced Digraphs
TL;DR: In this article, the authors show that the consensus-based dynamics that solves the continuous-time distributed optimization problem for undirected graphs fails to converge when transcribed to the directed setting.
Journal ArticleDOI
Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication
TL;DR: The exponential convergence of the proposed algorithm under strongly connected and weight-balanced digraph topologies when the local costs are strongly convex with globally Lipschitz gradients is established, and an upper bound on the stepsize is provided that guarantees exponential convergence over connected graphs for implementations with periodic communication.
Journal ArticleDOI
A survey of distributed optimization
Tao Yang,Xinlei Yi,Junfeng Wu,Ye Yuan,Di Wu,Ziyang Meng,Yiguang Hong,Hong Wang,Zongli Lin,Karl Henrik Johansson +9 more
TL;DR: This survey paper aims to offer a detailed overview of existing distributed optimization algorithms and their applications in power systems, and focuses on the application of distributed optimization in the optimal coordination of distributed energy resources.
Proceedings ArticleDOI
A control perspective for centralized and distributed convex optimization
Jing Wang,Nicola Elia +1 more
TL;DR: A general approach is obtained that allows to analyze and design (distributed) optimization systems converging to the solution of given convex optimization problems and demonstrates the natural tracking and adaptation capabilities of the system to changing constraints.
Journal ArticleDOI
Distributed gradient algorithm for constrained optimization with application to load sharing in power systems
Peng Yi,Yiguang Hong,Feng Liu +2 more
TL;DR: Both theoretical and numerical results show that the optimal load sharing can be achieved within both generation and delivering constraints in a distributed way.
References
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Distributed Subgradient Methods for Multi-Agent Optimization
Angelia Nedic,Asuman Ozdaglar +1 more
TL;DR: The authors' convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.