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We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes.
Communication-efficient accurate statistical estimation
Fan, J., Guo, Y., and Wang, K. (2019) · 1906
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Optimal complexity and certification of bregman first-order methods
Dragomir, R.-A., Taylor, A., d’Aspremont, A., and Bolt, J. (2019) · 1911
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Convex Analysis
Rockafellar, R. T. (1970) · 1970
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Multi-consensus decentralized accelerated gradient descent
Ye, H., Luo, L., Zhou, Z., and Zhang, T. (2020a) · 2005
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Towards accelerated rates for distributed optimization over time-varying networks
Rogozin, A., Lukoshkin, V., Gasnikov, A., Kovalev, D., and Shulgin, E. (2020) · 2009
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Scaling up Machine Learning: Parallel and Distributed Approaches
Bekkerman, R., Bilenko, M., and Langford, J. (2011) · 2011
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Libsvm: a library for support vector machines
Chang, C.-C. and Lin, C.-J. (2011) · 2011
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Recent theoretical advances in decentralized distributed convex optimization
Gorbunov, E., Rogozin, A., Beznosikov, A., Dvinskikh, D., and Gasnikov, A. (2020) · 2011
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Iterative solution of large linear systems
Wien, A. (2011) · 2011
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The mnist database of handwritten digit images for machine learning research
Deng, L. (2012) · 2012
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Stochastic dual coordinate ascent methods for regularized loss
Shalev-Shwartz, S. and Zhang, T. (2013) · 2013
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Communication-efficient distributed optimization using an approximate newton-type method
Shamir, O., Srebro, N., and Zhang, T. (2014) · 2014
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Communication complexity of distributed convex learning and optimization
Arjevani, Y. and Shamir, O. (2015) · 2015
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A universal catalyst for first-order optimization
Hongzhou, L., Mairal, J., and Harchaoui, Z. (2015) · 2015
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Disco: Distributed optimization for self-concordant empirical loss
Zhang, Y. and Lin, X. (2015) · 2015
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Next: In-network nonconvex optimization
Di Lorenzo, P. and Scutari, G. (2016) · 2016
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Aide: Fast and communication efficient distributed optimization
Accelerated gossip in networks of given dimension using jacobi polynomial iterations
Berthier, R., Bach, F., and Gaillard, P. (2020) · 2020
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Optimal and practical algorithms for smooth and strongly convex decentralized optimization
Kovalev, D., Salim, A., and Richtárik, P. (2020) · 2020
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Revisiting extra for smooth distributed optimization
Li, H. and Lin, Z. (2020) · 2020
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Relatively smooth convex optimization by first-order methods, and applications
Lu, H., Freund, R. M., and Nesterov, Y. (2020) · 2020
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A dual approach for optimal algorithms in distributed optimization over networks
Uribe, C. A., Lee, S., Gasnikov, A., and Nedić, A. (2020) · 2020
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Improving communication-efficient distributed sgd with slow momentum
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Reddi, S. J., Konecny, J., Richtarik, P., Poczos, B., and Smola, A. (2016) · 2016
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Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
Lian, X., Zhang, C., Zhang, H., Hsieh, C.-J., Zhang, W., and Liu., J. (2017) · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Lee, Y. T., and Massoulié, L. (2017) · 2017
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Convergence of asynchronous distributed gradient methods over stochastic networks
Xu, J., Zhu, S., Soh, Y. C., and Xie, L. (2017) · 2017
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Catalyst acceleration for first-order convex optimization: from theory to practice
Hongzhou, L., Mairal, J., and Harchaoui, Z. (2018) · 2018
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A sharp convergence rate analysis for distributed accelerated gradient methods
Li, H., Fang, C., Yin, W., and Lin, Z. (2018) · 2018
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Dual-free stochastic decentralized optimization with variance reduction
Hendrikx, H., Bach, F., and Massoulié, L. (2020a)
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Wang, J., Tantia, V., Ballas, N., and Rabbat, M. (2020) · 2020
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Decentralized accelerated proximal gradient descent
Ye, H., Zhou, Z., Luo, L., and Zhang, T. (2020b) · 2020
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On convergence of distributed approximate newton methods: Globalization, sharper bounds and beyond
Yuan, X.-T. and Li, P. (2020) · 2020
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d’Aspremont, A., Scieur, D., and Taylor, A. B. (2021) · 2021
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Distributed optimization based on gradient-tracking revisited: Enhancing convergence rate via surrogation
Sun, Y., Daneshmand, A., and Scutari, G. (2022) · 2022
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