Fetching the paper…
Reading the bibliography…
Although the field of distributed optimization is well-developed, relevant literature focused on the application of distributed optimization to multi-robot problems is limited.
M. Todescato, A. Carron, R. Carli, and L. Schenato, “Distributed localization from relative noisy measurements: A robust gradient based approach,” in 2015 European Control Conference (ECC) . IEEE, 2015, pp. 1914–1919
1919
Earlier work this paper cites.
R. T. Rockafellar, “Monotone operators and the proximal point algorithm,” SIAM journal on control and optimization , vol. 14, no. 5, pp. 877–898, 1976
1976
Earlier work this paper cites.
J. N. Tsitsiklis, “Problems in decentralized decision making and computation.” Massachusetts Inst of Tech Cambridge Lab for Information and Decision Systems, Tech. Rep., 1984
1984
Earlier work this paper cites.
J. Tsitsiklis, D. Bertsekas, and M. Athans, “Distributed asynchronous deterministic and stochastic gradient optimization algorithms,” IEEE Transactions on Automatic Control , vol. 31, no. 9, pp. 803–812, 1986
1986
Earlier work this paper cites.
D. P. Bertsekas and J. N. Tsitsiklis, Parallel and distributed computation: numerical methods . Prentice hall Englewood Cliffs, NJ, 1989, vol. 23
1989
Earlier work this paper cites.
N. A. Lynch, Distributed algorithms . Elsevier, 1996
1996
Earlier work this paper cites.
D. Kempe, A. Dobra, and J. Gehrke, “Gossip-based computation of aggregate information,” in 44th Annual IEEE Symposium on Foundations of Computer Science . IEEE, 2003, pp. 482–491
2003
Earlier work this paper cites.
S. Boyd, S. P. Boyd, and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Earlier work this paper cites.
A. Nedic and A. Ozdaglar, “Distributed subgradient methods for multi-agent optimization,” IEEE Transactions on Automatic Control , vol. 54, no. 1, pp. 48–61, 2009
2009
Earlier work this paper cites.
F. Bullo, J. Cortés, and S. Martínez, Distributed Control of Robotic Networks , ser. Applied Mathematics Series. Princeton University Press, 2009, electronically available at http://coordinationbook.info
2009
Earlier work this paper cites.
A. Olshevsky and J. N. Tsitsiklis, “Convergence speed in distributed consensus and averaging,” SIAM Journal on Control and Optimization , vol. 48, no. 1, pp. 33–55, 2009
2009
Earlier work this paper cites.
Y. Nesterov, “Primal-dual subgradient methods for convex problems,” Mathematical programming , vol. 120, no. 1, pp. 221–259, 2009
2009
Earlier work this paper cites.
R. Tron and R. Vidal, “Distributed image-based 3-d localization of camera sensor networks,” in Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference . IEEE, 2009, pp. 901–908
2009
Earlier work this paper cites.
A. Sarlette and R. Sepulchre, “Consensus optimization on manifolds,” SIAM Journal on Control and Optimization , vol. 48, no. 1, pp. 56–76, 2009
2009
Earlier work this paper cites.
G. Mateos, J. A. Bazerque, and G. B. Giannakis, “Distributed sparse linear regression,” IEEE Transactions on Signal Processing , vol. 58, no. 10, pp. 5262–5276, 2010
2010
Earlier work this paper cites.
B. Yang and M. Johansson, “Distributed optimization and games: A tutorial overview,” Networked Control Systems , pp. 109–148, 2010
2010
Earlier work this paper cites.
M. Mesbahi and M. Egerstedt, Graph theoretic methods in multiagent networks . Princeton University Press, 2010, vol. 33
2010
Earlier work this paper cites.
I. Lobel and A. Ozdaglar, “Distributed subgradient methods for convex optimization over random networks,” IEEE Transactions on Automatic Control , vol. 56, no. 6, pp. 1291–1306, 2010
2010
Earlier work this paper cites.
F. Bénézit, V. Blondel, P. Thiran, J. Tsitsiklis, and M. Vetterli, “Weighted gossip: Distributed averaging using non-doubly stochastic matrices,” in 2010 ieee international symposium on information theory . IEEE, 2010, pp. 1753–1757
2010
Earlier work this paper cites.
S. S. Ram, A. Nedić, and V. V. Veeravalli, “Distributed stochastic subgradient projection algorithms for convex optimization,” Journal of optimization theory and applications , vol. 147, no. 3, pp. 516–545, 2010
2010
Earlier work this paper cites.
B. Johansson, M. Rabi, and M. Johansson, “A randomized incremental subgradient method for distributed optimization in networked systems,” SIAM Journal on Optimization , vol. 20, no. 3, pp. 1157–1170, 2010
2010
Earlier work this paper cites.
L. Xiao, “Dual averaging methods for regularized stochastic learning and online optimization,” Journal of Machine Learning Research , vol. 11, no. Oct, pp. 2543–2596, 2010
2010
Earlier work this paper cites.
S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al. , “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends® in Machine learning , vol. 3, no. 1, pp. 1–122, 2011
2011
Earlier work this paper cites.
J. C. Duchi, A. Agarwal, and M. J. Wainwright, “Dual averaging for distributed optimization: Convergence analysis and network scaling,” IEEE Transactions on Automatic control , vol. 57, no. 3, pp. 592–606, 2011
2011
Earlier work this paper cites.
K. I. Tsianos and M. G. Rabbat, “Distributed consensus and optimization under communication delays,” in 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2011, pp. 974–982
2011
Earlier work this paper cites.
H. Terelius, U. Topcu, and R. M. Murray, “Decentralized multi-agent optimization via dual decomposition,” IFAC proceedings volumes , vol. 44, no. 1, pp. 11 245–11 251, 2011
2011
Earlier work this paper cites.
——, “Distributed computer vision algorithms,” IEEE Signal Processing Magazine , vol. 28, no. 3, pp. 32–45, 2011
2011
Earlier work this paper cites.
P. Bianchi and J. Jakubowicz, “Convergence of a multi-agent projected stochastic gradient algorithm for non-convex optimization,” IEEE transactions on automatic control , vol. 58, no. 2, pp. 391–405, 2012
2012
Earlier work this paper cites.
K. I. Tsianos, S. Lawlor, and M. G. Rabbat, “Push-sum distributed dual averaging for convex optimization,” in 2012 ieee 51st ieee conference on decision and control (cdc) . IEEE, 2012, pp. 5453–5458
2012
Earlier work this paper cites.
T. Erseghe, “A distributed and scalable processing method based upon admm,” IEEE Signal Processing Letters , vol. 19, no. 9, pp. 563–566, 2012
2012
Earlier work this paper cites.
R. Tron, Distributed optimization on manifolds for consensus algorithms and camera network localization . The Johns Hopkins University, 2012
2012
Earlier work this paper cites.
K. I. Tsianos, S. Lawlor, and M. G. Rabbat, “Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning,” in 2012 50th annual allerton conference on communication, control, and computing (allerton) . IEEE, 2012, pp. 1543–1550
2012
Earlier work this paper cites.
M. Bürger, G. Notarstefano, F. Bullo, and F. Allgöwer, “A distributed simplex algorithm for degenerate linear programs and multi-agent assignments,” Automatica , vol. 48, no. 9, pp. 2298–2304, 2012
2012
Earlier work this paper cites.
B. Gharesifard and J. Cortés, “Distributed strategies for generating weight-balanced and doubly stochastic digraphs,” European Journal of Control , vol. 18, no. 6, pp. 539–557, 2012
2012
Earlier work this paper cites.
M. Zargham, A. Ribeiro, A. Ozdaglar, and A. Jadbabaie, “Accelerated dual descent for network flow optimization,” IEEE Transactions on Automatic Control , vol. 59, no. 4, pp. 905–920, 2013
2013
Earlier work this paper cites.
F. Iutzeler, P. Bianchi, P. Ciblat, and W. Hachem, “Asynchronous distributed optimization using a randomized alternating direction method of multipliers,” in 52nd IEEE conference on decision and control . IEEE, 2013, pp. 3671–3676
2013
Earlier work this paper cites.
K.-K. Oh and H.-S. Ahn, “Formation control and network localization via orientation alignment,” IEEE Transactions on Automatic Control , vol. 59, no. 2, pp. 540–545, 2013
2013
Earlier work this paper cites.
J. Knuth and P. Barooah, “Collaborative localization with heterogeneous inter-robot measurements by Riemannian optimization,” in 2013 IEEE International Conference on Robotics and Automation . IEEE, 2013, pp. 1534–1539
2013
Earlier work this paper cites.
S. Hosseini, A. Chapman, and M. Mesbahi, “Online distributed optimization via dual averaging,” in 52nd IEEE Conference on Decision and Control . IEEE, 2013, pp. 1484–1489
2013
Earlier work this paper cites.
S. Shahrampour and A. Jadbabaie, “Exponentially fast parameter estimation in networks using distributed dual averaging,” in 52nd IEEE Conference on Decision and Control . IEEE, 2013, pp. 6196–6201
2013
Earlier work this paper cites.
Q. Ling and A. Ribeiro, “Decentralized dynamic optimization through the alternating direction method of multipliers,” IEEE Transactions on Signal Processing , vol. 62, no. 5, pp. 1185–1197, 2013
2013
Earlier work this paper cites.
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
Earlier work this paper cites.
I. Prodan, F. Stoican, S. Olaru, C. Stoica, and S.-I. Niculescu, “Mixed-integer programming techniques in distributed mpc problems,” in Distributed Model Predictive Control Made Easy . Springer, 2014, pp. 275–291
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
D. Jakovetić, J. Xavier, and J. M. Moura, “Fast distributed gradient methods,” IEEE Transactions on Automatic Control , vol. 59, no. 5, pp. 1131–1146, 2014
2014
Earlier work this paper cites.
T.-H. Chang, M. Hong, and X. Wang, “Multi-agent distributed optimization via inexact consensus ADMM,” IEEE Transactions on Signal Processing , vol. 63, no. 2, pp. 482–497, 2014
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
E. Montijano and A. R. Mosteo, “Efficient multi-robot formations using distributed optimization,” in 53rd IEEE Conference on Decision and Control . IEEE, 2014, pp. 6167–6172
2014
Earlier work this paper cites.
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
Earlier work this paper cites.
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
Earlier work this paper cites.
N. Chatzipanagiotis, D. Dentcheva, and M. M. Zavlanos, “An augmented Lagrangian method for distributed optimization,” Mathematical Programming , vol. 152, no. 1-2, pp. 405–434, 2015
2015
Earlier work this paper cites.
Q. Ling, W. Shi, G. Wu, and A. Ribeiro, “DLM: Decentralized linearized alternating direction method of multipliers,” IEEE Transactions on Signal Processing , vol. 63, no. 15, pp. 4051–4064, 2015
2015
Earlier work this paper cites.
A. Teixeira, E. Ghadimi, I. Shames, H. Sandberg, and M. Johansson, “The admm algorithm for distributed quadratic problems: Parameter selection and constraint preconditioning,” IEEE Transactions on Signal Processing , vol. 64, no. 2, pp. 290–305, 2015
2015
Earlier work this paper cites.
D. Meng, M. Fazel, and M. Mesbahi, “Proximal alternating direction method of multipliers for distributed optimization on weighted graphs,” in 2015 54th IEEE Conference on Decision and Control (CDC) . IEEE, 2015, pp. 1396–1401
2015
Earlier work this paper cites.
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
Earlier work this paper cites.
S. Choudhary, L. Carlone, H. I. Christensen, and F. Dellaert, “Exactly sparse memory efficient SLAM using the multi-block alternating direction method of multipliers,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 1349–1356
2015
Cited alongside, same era.
H. F. Xu, Q. Ling, and A. Ribeiro, “Online Learning over a Decentralized Network Through ADMM,” Journal of the Operations Research Society of China , vol. 3, no. 4, pp. 537–562, 2015
2015
Cited alongside, same era.
S. Zhu and B. Chen, “Distributed average consensus with deterministic quantization: An admm approach,” in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 2015, pp. 692–696
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.
N. Bof, R. Carli, G. Notarstefano, L. Schenato, and D. Varagnolo, “Multiagent newton–raphson optimization over lossy networks,” IEEE Transactions on Automatic Control , vol. 64, no. 7, pp. 2983–2990, 2018
2018
Later among the works it cites.
M. Lahijanian, M. Svorenova, A. A. Morye, B. Yeomans, D. Rao, I. Posner, P. Newman, H. Kress-Gazit, and M. Kwiatkowska, “Resource-performance tradeoff analysis for mobile robots,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 1840–1847, 2018
2018
Later among the works it cites.
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.
A. Testa, A. Rucco, and G. Notarstefano, “Distributed mixed-integer linear programming via cut generation and constraint exchange,” IEEE Transactions on Automatic Control , vol. 65, no. 4, pp. 1456–1467, 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Zhu, M. Hong, and B. Chen, “Quantized consensus admm for multi-agent distributed optimization,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 4134–4138
2016
Cited alongside, same era.
K. Yuan, Q. Ling, and W. Yin, “On the convergence of decentralized gradient descent,” SIAM Journal on Optimization , vol. 26, no. 3, pp. 1835–1854, 2016
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.
B. Houska, J. Frasch, and M. Diehl, “An augmented Lagrangian based algorithm for distributed nonconvex optimization,” SIAM Journal on Optimization , vol. 26, no. 2, pp. 1101–1127, 2016
2016
Cited alongside, same era.
A. Mokhtari, W. Shi, Q. Ling, and A. Ribeiro, “DQM: Decentralized quadratically approximated alternating direction method of multipliers,” IEEE Transactions on Signal Processing , vol. 64, no. 19, pp. 5158–5173, 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. Eriksson, J. Bastian, T.-J. Chin, and M. Isaksson, “A consensus-based framework for distributed bundle adjustment,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 1754–1762
2016
Cited alongside, same era.
S. Kumar, R. Jain, and K. Rajawat, “Asynchronous optimization over heterogeneous networks via consensus ADMM,” IEEE Transactions on Signal and Information Processing over Networks , vol. 3, no. 1, pp. 114–129, 2016
2016
Cited alongside, same era.
——, “ZONE: Zeroth-order nonconvex multiagent optimization over networks,” IEEE Transactions on Automatic Control , vol. 64, no. 10, pp. 3995–4010, 2019
2019
Later among the works it cites.
D. Hajinezhad and M. Hong, “Perturbed proximal primal–dual algorithm for nonconvex nonsmooth optimization,” Mathematical Programming , vol. 176, no. 1-2, pp. 207–245, 2019
2019
Later among the works it cites.
T. Yang, X. Yi, J. Wu, Y. Yuan, D. Wu, Z. Meng, Y. Hong, H. Wang, Z. Lin, and K. H. Johansson, “A survey of distributed optimization,” Annual Reviews in Control , 2019
2019
Later among the works it cites.
V. S. Mai and E. H. Abed, “Distributed optimization over directed graphs with row stochasticity and constraint regularity,” Automatica , vol. 102, pp. 94–104, 2019
2019
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
Later among the works it cites.
R. Xin and U. A. Khan, “Distributed heavy-ball: A generalization and acceleration of first-order methods with gradient tracking,” IEEE Transactions on Automatic Control , 2019
2019
Later among the works it cites.
A. Sundararajan, B. Van Scoy, and L. Lessard, “A canonical form for first-order distributed optimization algorithms,” in American Control Conference . IEEE, 2019, pp. 4075–4080
2019
Later among the works it cites.
G. Qu and N. Li, “Accelerated distributed nesterov gradient descent,” IEEE Transactions on Automatic Control , 2019
2019
Later among the works it cites.
R. Xin, D. Jakovetić, and U. A. Khan, “Distributed Nesterov gradient methods over arbitrary graphs,” IEEE Signal Processing Letters , vol. 26, no. 8, pp. 1247–1251, 2019
2019
Later among the works it cites.
F. Mansoori and E. Wei, “A general framework of exact primal-dual first-order algorithms for distributed optimization,” in 2019 IEEE 58th Conference on Decision and Control (CDC) . IEEE, 2019, pp. 6386–6391
2019
Later among the works it cites.
——, “ECO-PANDA: a computationally economic, geometrically converging dual optimization method on time-varying undirected graphs,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5257–5261
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.
F. Farina, A. Garulli, A. Giannitrapani, and G. Notarstefano, “A distributed asynchronous method of multipliers for constrained nonconvex optimization,” Automatica , vol. 103, pp. 243–253, 2019
2019
Later among the works it cites.
M. L. Elwin, R. A. Freeman, and K. M. Lynch, “Distributed environmental monitoring with finite element robots,” IEEE Transactions on Robotics , vol. 36, no. 2, pp. 380–398, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
A. Khodabandeh and P. Teunissen, “Distributed least-squares estimation applied to GNSS networks,” Measurement Science and Technology , vol. 30, no. 4, p. 044005, 2019
2019
Later among the works it cites.
K. Lu, G. Jing, and L. Wang, “Online distributed optimization with strongly pseudoconvex-sum cost functions,” IEEE Transactions on Automatic Control , vol. 65, no. 1, pp. 426–433, 2019
2019
Later among the works it cites.
Y. Zhang, R. J. Ravier, M. M. Zavlanos, and V. Tarokh, “A distributed online convex optimization algorithm with improved dynamic regret,” in 2019 IEEE 58th Conference on Decision and Control (CDC) . IEEE, 2019, pp. 2449–2454
2019
Later among the works it cites.
R. Altilio, P. Di Lorenzo, and M. Panella, “Distributed data clustering over networks,” Pattern Recognition , vol. 93, pp. 603–620, 2019
2019
Later among the works it cites.
R. Haksar, O. Shorinwa, P. Washington, and M. Schwager, “Consensus-based ADMM for task assignment in multi-robot teams,” in International Symposium on Robotics Research , 2019
2019
Later among the works it cites.
Y. Yu, J. Wu, and L. Huang, “Double quantization for communication-efficient distributed optimization,” Advances in Neural Information Processing Systems , vol. 32, pp. 4438–4449, 2019
2019
Later among the works it cites.
A. Reisizadeh, A. Mokhtari, H. Hassani, and R. Pedarsani, “An exact quantized decentralized gradient descent algorithm,” IEEE Transactions on Signal Processing , vol. 67, no. 19, pp. 4934–4947, 2019
2019
Later among the works it cites.
S. M. Trenkwalder, “Computational resources of miniature robots: Classification and implications,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2722–2729, 2019
2019
Later among the works it cites.
S. Liu, P.-Y. Chen, B. Kailkhura, G. Zhang, A. O. Hero III, and P. K. Varshney, “A primer on zeroth-order optimization in signal processing and machine learning: Principals, recent advances, and applications,” IEEE Signal Processing Magazine , vol. 37, no. 5, pp. 43–54, 2020
2020
Later among the works it cites.
A. Beznosikov, E. Gorbunov, and A. Gasnikov, “Derivative-free method for composite optimization with applications to decentralized distributed optimization,” IFAC-PapersOnLine , vol. 53, no. 2, pp. 4038–4043, 2020
2020
Later among the works it cites.
Y. Tang, J. Zhang, and N. Li, “Distributed zero-order algorithms for nonconvex multiagent optimization,” IEEE Transactions on Control of Network Systems , vol. 8, no. 1, pp. 269–281, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
T.-H. Chang, M. Hong, H.-T. Wai, X. Zhang, and S. Lu, “Distributed learning in the nonconvex world: From batch data to streaming and beyond,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 26–38, 2020
2020
Later among the works it cites.
——, “Analysis and design of first-order distributed optimization algorithms over time-varying graphs,” IEEE Transactions on Control of Network Systems , vol. 7, no. 4, pp. 1597–1608, 2020
2020
Later among the works it cites.
Q. Lü, X. Liao, H. Li, and T. Huang, “A Nesterov-like gradient tracking algorithm for distributed optimization over directed networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2020
2020
Later among the works it cites.
——, “A geometrically converging dual method for distributed optimization over time-varying graphs,” IEEE Transactions on Automatic Control , 2020
2020
Later among the works it cites.
G. Lan, S. Lee, and Y. Zhou, “Communication-efficient algorithms for decentralized and stochastic optimization,” Mathematical Programming , vol. 180, no. 1, pp. 237–284, 2020
2020
Later among the works it cites.
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.
M. Todescato, N. Bof, G. Cavraro, R. Carli, and L. Schenato, “Partition-based multi-agent optimization in the presence of lossy and asynchronous communication,” Automatica , vol. 111, p. 108648, 2020
2020
Later among the works it cites.
T. Fan and T. Murphey, “Majorization minimization methods for distributed pose graph optimization with convergence guarantees,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 5058–5065
2020
Later among the works it cites.
Y. Tian, A. Koppel, A. S. Bedi, and J. P. How, “Asynchronous and parallel distributed pose graph optimization,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5819–5826, 2020
2020
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
Later among the works it cites.
——, “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
Later among the works it cites.
A. Elgabli, J. Park, A. S. Bedi, C. B. Issaid, M. Bennis, and V. Aggarwal, “Q-gadmm: Quantized group admm for communication efficient decentralized machine learning,” IEEE Transactions on Communications , vol. 69, no. 1, pp. 164–181, 2020
2020
Later among the works it cites.
N. Bastianello, R. Carli, L. Schenato, and M. Todescato, “Asynchronous distributed optimization over lossy networks via relaxed admm: Stability and linear convergence,” IEEE Transactions on Automatic Control , vol. 66, no. 6, pp. 2620–2635, 2020
2020
Later among the works it cites.
——, “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.
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
Later among the works it cites.
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.
O. Shorinwa and M. Schwager, “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.