Distributed Optimization for Control
By Angelia Nedić, Ji Liu. Annu. Rev. Control. Robotics Auton. Syst., 2018
342 citations recorded by the source corpus.
Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.
Subfields: Computer Networks and Communications, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/c0eea5c4bab0c810e4820c67d3aee8df763c672e
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In Computer Networks and Communications
- Angelia Nedić
- Alexander Olshevsky
- Ali H. Sayed
- Wei Shi
- Michael G. Rabbat
- Qing Ling
- Wotao Yin
- Asuman E. Ozdaglar
In Control and Systems Engineering
Citations in the corpus
- Distributed Gradient Methods for Convex Machine Learning Problems in Networks: Distributed Optimization (2020, background): “A recent survey [10] thoroughly reviews consensus problem and variations and extensions of the distributed method that account for time-varying graphs, delays and noisy (or quantized) communication links, noisy (stochastic) gradient evaluations, as well as some asynchronous methods.”