The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Distributed Optimization for Control
Abstract
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.
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Citations
Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788
Coordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Distributed Subgradient Methods for Multi-Agent Optimization
Nedic, A., & Ozdaglar, A. (2009) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2008.2009515 · Cited by 3,719
Graph Theoretic Methods in Multiagent Networks
Mesbahi, M., & Egerstedt, M. (2010) · Princeton University Press eBooks · doi:10.1515/9781400835355 · Cited by 2,897
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
Shi, W., Ling, Q., Wu, G., & Yin, W. (2015) · SIAM Journal on Optimization · doi:10.1137/14096668x · Cited by 1,261
Distributed Optimization Over Time-Varying Directed Graphs
Nedic, A., & Olshevsky, A. (2015) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2364096 · Cited by 1,187
Self-citationNEXT: In-Network Nonconvex Optimization
Lorenzo, P. D., & Scutari, G. (2016) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2016.2524588 · Cited by 473
Distributed Subgradient Methods for Convex Optimization Over Random Networks
Lobel, I., & Ozdaglar, A. (2011) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2010.2091295 · Cited by 351
Xu, J., Zhu, S., Soh, Y. C., & Xie, L. (2015) · 2015 54th IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2015.7402509 · Cited by 332
Distributed Strategies for Generating Weight-Balanced and Doubly Stochastic Digraphs
Gharesifard, B., & Cortés, J. (2012) · European Journal of Control · doi:10.3166/ejc.18.539-557 · Cited by 171
Cited by
Nonconvex distributed feedback optimization for aggregative cooperative robotics
Carnevale, G., Mimmo, N., & Notarstefano, G. (2024) · Automatica · doi:10.1016/j.automatica.2024.111767 · Cited by 23
Delay-Robust Primal-Dual Dynamics for Distributed Optimization
Şen, G. D., Machado, J. E., Öke Günel, G., & Schiffer, J. (2026) · IEEE Control Systems Letters · doi:10.1109/lcsys.2026.3698431
Metadata
- Authors
- Nedić, A., & Liu, J.
- Venue
- Annual Review of Control, Robotics, and Autonomous Systems
- Publisher
- Annual Reviews
- Published
- 2018-05-28
- Volume
- 1
- Issue
- 1
- Pages
- 77-103
- ISSN
- 2573-5144 (ISSN-L)
- OpenAlex
- W2807424650
- Cited by
- 302
- References
- 13
- FWCI
- 28.68
- Type
- journal article
- Language
- en
- Open access
- closed
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