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Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems.
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R. Olfati-Saber, “Distributed Kalman filtering for sensor networks,” in 2007 46th IEEE Conference on Decision and Control . IEEE, 2007, pp. 5492–5498
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2009
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S. Giordani, M. Lujak, and F. Martinelli, “A distributed algorithm for the multi-robot task allocation problem,” in International conference on industrial, engineering and other applications of applied intelligent systems . Springer, 2010, pp. 721–730
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2010
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A. I.-A. Chen, “Fast distributed first-order methods,” Ph.D. dissertation, Massachusetts Institute of Technology, 2012
2012
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J. Bento, N. Derbinsky, J. Alonso-Mora, and J. S. Yedidia, “A message-passing algorithm for multi-agent trajectory planning,” in Advances in neural information processing systems , 2013, pp. 521–529
2013
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A. Ahmad, G. D. Tipaldi, P. Lima, and W. Burgard, “Cooperative robot localization and target tracking based on least squares minimization,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 5696–5701
2013
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L. Liu and D. A. Shell, “Optimal market-based multi-robot task allocation via strategic pricing.” in Robotics: Science and Systems , vol. 9, no. 1, 2013, pp. 33–40
2013
Cited alongside, same era.
X. Lian, W. Zhang, C. Zhang, and J. Liu, “Asynchronous decentralized parallel stochastic gradient descent,” in International Conference on Machine Learning . PMLR, 2018, pp. 3043–3052
2018
Later among the works it cites.
Z. Li, W. Shi, and M. Yan, “A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates,” IEEE Transactions on Signal Processing , vol. 67, no. 17, pp. 4494–4506, 2019
2019
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R. N. Haksar, O. Shorinwa, P. Washington, and M. Schwager, “Consensus-based admm for task assignment in multi-robot teams,” in The International Symposium of Robotics Research . Springer, 2019, pp. 35–51
2019
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Y. Zhang and M. M. Zavlanos, “Distributed off-policy actor-critic reinforcement learning with policy consensus,” in 2019 IEEE 58th Conference on Decision and Control (CDC) . IEEE, 2019, pp. 4674–4679
2019
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A. Nedić and A. Olshevsky, “Distributed optimization over time-varying directed graphs,” IEEE Transactions on Automatic Control , vol. 60, no. 3, pp. 601–615, 2014
2014
Cited alongside, same era.
R. Tron and R. Vidal, “Distributed 3-d localization of camera sensor networks from 2-d image measurements,” IEEE Transactions on Automatic Control , vol. 59, no. 12, pp. 3325–3340, 2014
2014
Cited alongside, same era.
W. Shi, Q. Ling, G. Wu, and W. Yin, “EXTRA: An exact first-order algorithm for decentralized consensus optimization,” SIAM Journal on Optimization , vol. 25, no. 2, pp. 944–966, 2015
2015
Cited alongside, same era.
A. Mokhtari, Q. Ling, and A. Ribeiro, “Network Newton,” Conference Record - Asilomar Conference on Signals, Systems and Computers , vol. 2015-April, pp. 1621–1625, 2015
2015
Cited alongside, same era.
N. A. Alwan and A. S. Mahmood, “Distributed gradient descent localization in wireless sensor networks,” Arabian Journal for Science and Engineering , vol. 40, no. 3, pp. 893–899, 2015
2015
Cited alongside, same era.
P. Di Lorenzo and G. Scutari, “NEXT: In-network nonconvex optimization,” IEEE Transactions on Signal and Information Processing over Networks , vol. 2, no. 2, pp. 120–136, 2016
2016
Cited alongside, same era.
V.-L. Dang, B.-S. Le, T.-T. Bui, H.-T. Huynh, and C.-K. Pham, “A decentralized localization scheme for swarm robotics based on coordinate geometry and distributed gradient descent,” in MATEC Web of Conferences , vol. 54. EDP Sciences, 2016, p. 02002
2016
Cited alongside, same era.
A. Mokhtari, W. Shi, Q. Ling, and A. Ribeiro, “A decentralized second-order method with exact linear convergence rate for consensus optimization,” IEEE Transactions on Signal and Information Processing over Networks , vol. 2, no. 4, pp. 507–522, 2016
2016
Cited alongside, same era.
2019
Later among the works it cites.
M. Eisen, A. Mokhtari, and A. Ribeiro, “A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization,” IEEE Transactions on Signal Processing , vol. 67, no. 23, pp. 5983–5997, 2019
2019
Later among the works it cites.
F. Mansoori and E. Wei, “A fast distributed asynchronous newton-based optimization algorithm,” IEEE Transactions on Automatic Control , vol. 65, no. 7, pp. 2769–2784, 2019
2019
Later among the works it cites.
O. Shorinwa, J. Yu, T. Halsted, A. Koufos, and M. Schwager, “Distributed multi-target tracking for autonomous vehicle fleets,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 3495–3501
2020
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O. Shorinwa, T. Halsted, and M. Schwager, “Scalable distributed optimization with separable variables in multi-agent networks,” in 2020 American Control Conference (ACC) . IEEE, 2020, pp. 3619–3626
2020
Later among the works it cites.
O. Shorinwa and M. Schwager, “Scalable collaborative manipulation with distributed trajectory planning,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS’20 , vol. 1. IEEE, 2020, pp. 9108–9115
2020
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V. Khatana and M. V. Salapaka, “D-distadmm: Ao (1/k) distributed admm for distributed optimization in directed graph topologies,” in 2020 59th IEEE Conference on Decision and Control (CDC) . IEEE, 2020, pp. 2992–2997
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
O. Shorinwa and M. Schwager, “Distributed contact-implicit trajectory optimization for collaborative manipulation,” in 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS) . IEEE, 2021, pp. 56–65
2021
Later among the works it cites.
J. Yu, J. A. Vincent, and M. Schwager, “DiNNO: Distributed neural network optimization for multi-robot collaborative learning,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1896–1903, 2022
2022
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2023
Closest in time.
H. Liu, J. Zhang, A. M.-C. So, and Q. Ling, “A communication-efficient decentralized newton’s method with provably faster convergence,” IEEE Transactions on Signal and Information Processing over Networks , 2023
2023
Closest in time.
O. Shorinwa and M. Schwager, “Distributed target tracking in multi-agent networks via sequential quadratic alternating direction method of multipliers,” in 2023 American Control Conference (ACC) . IEEE, 2023, pp. 341–348
2023
Closest in time.
O. Shorinwa, R. N. Haksar, P. Washington, and M. Schwager, “Distributed multirobot task assignment via consensus admm,” IEEE Transactions on Robotics , vol. 39, no. 3, pp. 1781–1800, 2023
2023
Closest in time.
——, “Distributed model predictive control via separable optimization in multiagent networks,” IEEE Transactions on Automatic Control , vol. 69, no. 1, pp. 230–245, 2023
2023
Closest in time.
2024
Closest in time.
J. Xu, S. Zhu, Y. C. Soh, and L. Xie, “Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes,” in 2015 54th IEEE Conference on Decision and Control (CDC) . IEEE, 2015, pp. 2055–2060
2060
Closest in time.
O. Shorinwa and M. Schwager, “Distributed conjugate gradient method via conjugate direction tracking,” in 2024 American Control Conference (ACC) . IEEE, 2024, pp. 2066–2073
2073
Closest in time.