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We introduce primal and dual stochastic gradient oracle methods for decentralized convex optimization problems.
Asynchronous accelerated proximal stochastic gradient for strongly convex distributed finite sums
Hendrikx, H., Bach, F., and Massoulié, L. (2019b) · 1901
Earlier work this paper cites.
Kulunchakov, A. and Mairal, J. (2019a) · 1901
Earlier work this paper cites.
The complexity of making the gradient small in stochastic convex optimization
Foster, D., Sekhari, A., Shamir, O., Srebro, N., Sridharan, K., and Woodworth, B. (2019) · 1902
Earlier work this paper cites.
A short note on concentration inequalities for random vectors with subgaussian norm
Jin, C., Netrapalli, P., Ge, R., Kakade, S. M., and Jordan, M. I. (2019) · 1902
Earlier work this paper cites.
Inexact model: A framework for optimization and variational inequalities
Stonyakin, F., Gasnikov, A., Tyurin, A., Pasechnyuk, D., Agafonov, A., Dvurechensky, P., Dvinskikh, D., and Piskunova, V. (2019a) · 1902
Earlier work this paper cites.
On dual approach for distributed stochastic convex optimization over networks
Dvinskikh, D., Gorbunov, E., Gasnikov, A., Dvurechensky, P., and Uribe, C. A. (2019) · 1903
Earlier work this paper cites.
An accelerated decentralized stochastic proximal algorithm for finite sums
Hendrikx, H., Bach, F., and Massoulie, L. (2019a) · 1905
Earlier work this paper cites.
Estimate sequences for variance-reduced stochastic composite optimization
Kulunchakov, A. and Mairal, J. (2019b) · 1905
Earlier work this paper cites.
A generic acceleration framework for stochastic composite optimization
Kulunchakov, A. and Mairal, J. (2019c) · 1906
Earlier work this paper cites.
Asymptotic network independence in distributed optimization for machine learning
Olshevsky, A., Paschalidis, I. C., and Pu, S. (2019a) · 1906
Earlier work this paper cites.
A non-asymptotic analysis of network independence for distributed stochastic gradient descent
Olshevsky, A., Paschalidis, I. C., and Pu, S. (2019b) · 1906
Earlier work this paper cites.
Robust distributed accelerated stochastic gradient methods for multi-agent networks
Fallah, A., Gurbuzbalaban, M., Ozdaglar, A., Simsekli, U., and Zhu, L. (2019) · 1910
Earlier work this paper cites.
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Earlier work this paper cites.
The practicality of stochastic optimization in imaging inverse problems
Tang, J., Egiazarian, K., Golbabaee, M., and Davies, M. (2019) · 1910
Earlier work this paper cites.
Accelerated primal-dual algorithms for distributed smooth convex optimization over networks
Xu, J., Tian, Y., Sun, Y., and Scutari, G. (2019) · 1910
Earlier work this paper cites.
Derivative-free method for decentralized distributed non-smooth optimization
Beznosikov, A., Gorbunov, E., and Gasnikov, A. (2019) · 1911
Earlier work this paper cites.
Optimal decentralized distributed algorithms for stochastic convex optimization
Gorbunov, E., Dvinskikh, D., and Gasnikov, A. (2019) · 1911
Earlier work this paper cites.
Adaptive catalyst for smooth convex optimization
Ivanova, A., Grishchenko, D., Gasnikov, A., and Shulgin, E. (2019) · 1911
Earlier work this paper cites.
Projected gradient method for decentralized optimization over time-varying networks
Rogozin, A. and Gasnikov, A. (2019) · 1911
Earlier work this paper cites.
Decomposition into functions in the minimization problem
Kibardin, V. (1979) · 1979
Earlier work this paper cites.
Iterative algorithms for singular minimization problems
Poljak, B. (1981) · 1981
Earlier work this paper cites.
Introduction to Optimization
Polyak, B. (1987) · 1987
Earlier work this paper cites.
Parallel and distributed computation: numerical methods
Bertsekas, D. P. and Tsitsiklis, J. N. (1989) · 1989
Earlier work this paper cites.
Lectures on Modern Convex Optimization
Ben-Tal, A. and Nemirovski, A. (2001) · 2001
Earlier work this paper cites.
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., and Friedman, J. (2001) · 2001
Earlier work this paper cites.
Statistically preconditioned accelerated gradient method for distributed optimization
Hendrikx, H., Xiao, L., Bubeck, S., Bach, F., and Massoulie, L. (2020c) · 2002
Earlier work this paper cites.
Revisiting extra for smooth distributed optimization
Li, H. and Lin, Z. (2020) · 2002
Earlier work this paper cites.
Computational methods for inverse problems
Vogel, C. R. (2002) · 2002
Earlier work this paper cites.
Is local sgd better than minibatch sgd?
Woodworth, B., Patel, K. K., Stich, S. U., Dai, Z., Bullins, B., McMahan, H. B., Shamir, O., and Srebro, N. (2020b) · 2002
Earlier work this paper cites.
A unified theory of decentralized sgd with changing topology and local updates
Koloskova, A., Loizou, N., Boreiri, S., Jaggi, M., and Stich, S. U. (2020) · 2003
Earlier work this paper cites.
An optimal algorithm for decentralized finite sum optimization
Hendrikx, H., Bach, F., and Massoulie, L. (2020b) · 2005
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Smooth minimization of non-smooth functions
Nesterov, Y. (2005) · 2005
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Multi-consensus decentralized accelerated gradient descent
Ye, H., Luo, L., Zhou, Z., and Zhang, T. (2020) · 2005
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Dual-free stochastic decentralized optimization with variance reduction
Hendrikx, H., Bach, F., and Massoulié, L. (2020a) · 2006
Earlier work this paper cites.
Optimal and practical algorithms for smooth and strongly convex decentralized optimization
Kovalev, D., Salim, A., and Richtarik, P. (2020) · 2006
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Principled analyses and design of first-order methods with proximal inexact proximal operator
Mathieu, B., Adrien, T., and Francis, B. (2020) · 2006
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Minibatch vs local sgd for heterogeneous distributed learning
Woodworth, B., Patel, K. K., and Srebro, N. (2020a) · 2006
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Federated accelerated stochastic gradient descent
Yuan, H. and Ma, T. (2020) · 2006
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Gradient methods for minimizing composite functions
Nesterov, Y. (2013) · 2007
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Smooth optimization with approximate gradient
d’Aspremont, A. (2008) · 2008
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Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints
Anikin, A. S., Gasnikov, A. V., Dvurechensky, P. E., Tyurin, A. I., and Chernov, A. V. (2017) · 2017
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Dvurechensky, P., Gasnikov, A., and Tiurin, A. (2017) · 2017
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Distributed computation of linear inverse problems with application to computed tomography
Gao, Y. and Blumensath, T. (2017) · 2017
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Gasnikov, A. (2017) · 2017
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Convex optimization in hilbert space with applications to inverse problems
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On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization
Kakade, S., Shalev-Shwartz, S., and Tewari, A. (2009) · 2009
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Li, H., Lin, Z., and Fang, Y. (2020) · 2009
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Primal-dual subgradient methods for convex problems
Nesterov, Y. (2009) · 2009
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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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Stochastic convex optimization
Shalev-Shwartz, S., Shamir, O., Srebro, N., and Sridharan, K. (2009) · 2009
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Lectures on Stochastic Programming
Shapiro, A., Dentcheva, D., and Ruszczynski, A. (2009) · 2009
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Accuracy certificates for computational problems with convex structure
Nemirovski, A., Onn, S., and Rothblum, U. G. (2010) · 2010
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Gasnikov, A., Kabanikhin, S., Mohammed, A., and Shishlenin, M. (2017) · 2017
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Non-asymptotic confidence bounds for the optimal value of a stochastic program
Guigues, V., Juditsky, A., and Nemirovski, A. (2017) · 2017
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Communication-efficient algorithms for decentralized and stochastic optimization
Lan, G., Lee, S., and Zhou, Y. (2017) · 2017
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An optimal randomized incremental gradient method
Lan, G. and Zhou, Y. (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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Optimization methods for inverse problems
Ye, N., Roosta-Khorasani, F., and Cui, T. (2019) · 2017
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How to make the gradients small stochastically: Even faster convex and nonconvex sgd
Allen-Zhu, Z. (2018) · 2018
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On acceleration with noise-corrupted gradients
Cohen, M. B., Diakonikolas, J., and Orecchia, L. (2018) · 2018
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Decentralize and randomize: Faster algorithm for wasserstein barycenters
Dvurechenskii, P., Dvinskikh, D., Gasnikov, A., Uribe, C., and Nedich, A. (2018) · 2018
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Parallel algorithms and probability of large deviation for stochastic convex optimization problems
Dvurechensky, P., Gasnikov, A., and Lagunovskaya, A. (2018) · 2018
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Universal method for stochastic composite optimization problems
Gasnikov, A. V. and Nesterov, Y. E. (2018) · 2018
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An accelerated method for derivative-free smooth stochastic convex optimization
Gorbunov, E., Dvurechensky, P., and Gasnikov, A. (2018) · 2018
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Accelerated decentralized optimization with local updates for smooth and strongly convex objectives
Hendrikx, H., Bach, F., and Massoulié, L. (2018) · 2018
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Kim, D. and Fessler, J. A. (2018) · 2018
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Random gradient extrapolation for distributed and stochastic optimization
Lan, G. and Zhou, Y. (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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Graph-theoretic analysis of belief system dynamics under logic constraints
Nedić, A., Olshevsky, A., and Uribe, C. A. (2018) · 2018
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Implementable tensor methods in unconstrained convex optimization
Nesterov, Y. (2018a) · 2018
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Optimal distributed optimization on slowly time-varying graphs
Rogozin, A., Uribe, C. A., Gasnikov, A., Malkovsky, N., and Nedić, A. (2018) · 2018
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Optimal algorithms for non-smooth distributed optimization in networks
Scaman, K., Bach, F., Bubeck, S., Massoulié, L., and Lee, Y. T. (2018) · 2018
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Distributed non-convex first-order optimization and information processing: Lower complexity bounds and rate optimal algorithms
Sun, H. and Hong, M. (2018) · 2018
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Distributed computation of Wasserstein barycenters over networks
Uribe, C. A., Dvinskikh, D., Dvurechensky, P., Gasnikov, A., and Nedić, A. (2018) · 2018
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Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Woodworth, B. E., Wang, J., Smith, A., McMahan, B., and Srebro, N. (2018) · 2018
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Lectures on optimization methods for machine learning
Lan, G. (2019) · 2019
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Accelerated and non accelerated stochastic gradient descent in model generality
Dvinskikh, D. M., Turin, A. I., Gasnikov, A. V., and Omelchenko, S. S. (2020) · 2020
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On the rates of convergence of parallelized averaged stochastic gradient algorithms
Godichon-Baggioni, A. and Saadane, S. (2020) · 2020
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Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
Nedic, A. (2020) · 2020
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Penalty-based method for decentralized optimization over time-varying graphs
Rogozin, A. and Gasnikov, A · 2020
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Mirror descent for constrained optimization problems with large subgradient values of functional constraints
Stonyakin, F. S., Stepanov, A. N., Gasnikov, A. V., and Titov, A. A. (2020) · 2020
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A dual approach for optimal algorithms in distributed optimization over networks
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