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In the last few years, the theory of decentralized distributed convex optimization has made significant progress.
A stochastic approximation method
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
Monotone (nonlinear) operators in Hilbert space
G. J. Minty · 1962
Earlier work this paper cites.
Decomposition into functions in the minimization problem
V. Kibardin · 1979
Earlier work this paper cites.
Problems in decentralized decision making and computation
J. N. Tsitsiklis · 1984
Earlier work this paper cites.
Parallel and distributed computation: numerical methods
D. P. Bertsekas and J. N. Tsitsiklis · 1989
Earlier work this paper cites.
First-and second-order diffusive methods for rapid, coarse, distributed load balancing
S. Muthukrishnan, B. Ghosh, and M. H. Schultz · 1998
Earlier work this paper cites.
On the generalization ability of on-line learning algorithms
N. Cesa-bianchi, A. Conconi, and C. Gentile · 2002
Earlier work this paper cites.
Prox-method with rate of convergence o ( 1 / t ) o(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems
A. Nemirovski · 2004
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: a basic course
Y. Nesterov · 2004
Earlier work this paper cites.
Fast linear iterations for distributed averaging
L. Xiao and S. Boyd · 2004
Earlier work this paper cites.
Randomized gossip algorithms
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah · 2006
Earlier work this paper cites.
Finite-Dimensional Variational Inequalities and Complementarity Problems
F. Facchinei and J. Pang · 2007
Earlier work this paper cites.
Theory of Games and Economic Behavior (commemorative edition)
J. von Neumann, O. Morgenstern, and H. Kuhn · 2007
Earlier work this paper cites.
An optimal method for stochastic composite optimization
G. Lan · 2008
Earlier work this paper cites.
On the duality of strong convexity and strong smoothness: Learning applications and matrix regularization
S. Kakade, S. Shalev-Shwartz, and A. Tewari · 2009
Earlier work this paper cites.
Distributed subgradient methods for multi-agent optimization
A. Nedić and A. Ozdaglar · 2009
Earlier work this paper cites.
Robust stochastic approximation approach to stochastic programming
A. Nemirovski, A. Juditsky, G. Lan, and A. Shapiro · 2009
Earlier work this paper cites.
Stochastic convex optimization
S. Shalev-Shwartz, O. Shamir, N. Srebro, and K. Sridharan · 2009
Earlier work this paper cites.
A first-order primal-dual algorithm for convex problems with applications to imaging
A. Chambolle and T. Pock · 2011
Earlier work this paper cites.
Solving variational inequalities with stochastic mirror-prox algorithm
A. Juditsky, A. Nemirovski, and C. Tauvel · 2011
Earlier work this paper cites.
Accelerated linear iterations for distributed averaging
J. Liu and A. S. Morse · 2011
Earlier work this paper cites.
A simple stochastic variance reduced algorithm with fast convergence rates
K. Zhou, F. Shang, and J. Cheng · 2011
Earlier work this paper cites.
Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
S. Ghadimi and G. Lan · 2012
Earlier work this paper cites.
First order methods for non-smooth convex large-scale optimization, i: General purpose methods
A. Juditsky and A. Nemirovski · 2012
Earlier work this paper cites.
How to make the gradients small
Y. Nesterov · 2012
Earlier work this paper cites.
Parametric estimation. finite sample theory
V. Spokoiny et al · 2012
Earlier work this paper cites.
Exactness, inexactness and stochasticity in first-order methods for large-scale convex optimization
O. Devolder · 2013
Earlier work this paper cites.
First-order methods with inexact oracle: the strongly convex case
O. Devolder, F. Glineur, and Y. Nesterov · 2013
Earlier work this paper cites.
Stochastic first- and zeroth-order methods for nonconvex stochastic programming
S. Ghadimi and G. Lan · 2013
Earlier work this paper cites.
Accelerating stochastic gradient descent using predictive variance reduction
R. Johnson and T. Zhang · 2013
Earlier work this paper cites.
Distributed random projection algorithm for convex optimization
S. Lee and A. Nedic · 2013
Earlier work this paper cites.
Saga: A fast incremental gradient method with support for non-strongly convex composite objectives
A. Defazio, F. Bach, and S. Lacoste-Julien · 2014
Earlier work this paper cites.
First-order methods of smooth convex optimization with inexact oracle
O. Devolder, F. Glineur, and Y. Nesterov · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Fast distributed gradient methods
D. Jakovetić, J. Xavier, and J. M. Moura · 2014
Earlier work this paper cites.
Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
A. Juditsky and Y. Nesterov · 2014
Earlier work this paper cites.
Gradient-free method for nonsmooth distributed optimization
J. Li, C. Wu, Z. Wu, and Q. Long · 2014
Earlier work this paper cites.
Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
Earlier work this paper cites.
Distributionally robust logistic regression
S. Abadeh, P. Esfahani, and D. Kuhn · 2015
Earlier work this paper cites.
Communication complexity of distributed convex learning and optimization
Y. Arjevani and O. Shamir · 2015
Earlier work this paper cites.
A universal catalyst for first-order optimization
H. Lin, J. Mairal, and Z. Harchaoui · 2015
Earlier work this paper cites.
Distributed subgradient methods for saddle-point problems
D. Mateos-Núnez and J. Cortés · 2015
Earlier work this paper cites.
Convex analysis
R. T. Rockafellar · 2015
Earlier work this paper cites.
Extra: An exact first-order algorithm for decentralized consensus optimization
W. Shi, Q. Ling, G. Wu, and W. Yin · 2015
Earlier work this paper cites.
A smoothed dual approach for variational wasserstein problems
M. Cuturi and G. Peyré · 2016
Earlier work this paper cites.
Stochastic intermediate gradient method for convex problems with stochastic inexact oracle
P. Dvurechensky and A. Gasnikov · 2016
Earlier work this paper cites.
A. V. Gasnikov, A. A. Lagunovskaya, I. N. Usmanova, and F. A. Fedorenko · 2016
Earlier work this paper cites.
Gradient sliding for composite optimization
G. Lan · 2016
Earlier work this paper cites.
Algorithms for stochastic optimization with expectation constraints
G. Lan and Z. Zhou · 2016
Earlier work this paper cites.
On the convergence of decentralized gradient descent
K. Yuan, Q. Ling, and W. Yin · 2016
Earlier work this paper cites.
QSGD: Communication-efficient SGD via gradient quantization and encoding
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic · 2017
Earlier work this paper cites.
Katyusha: The first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
Earlier work this paper cites.
Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints
A. S. Anikin, A. V. Gasnikov, P. E. Dvurechensky, A. I. Tyurin, and A. V. Chernov · 2017
Earlier work this paper cites.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Earlier work this paper cites.
P. Dvurechensky, A. Gasnikov, and A. Tiurin · 2017
Earlier work this paper cites.
A. Gasnikov · 2017
Earlier work this paper cites.
Communication-efficient algorithms for decentralized and stochastic optimization
G. Lan, S. Lee, and Y. Zhou · 2017
Earlier work this paper cites.
Achieving geometric convergence for distributed optimization over time-varying graphs
A. Nedic, A. Olshevsky, and W. Shi · 2017
Earlier work this paper cites.
Random gradient-free minimization of convex functions
Y. Nesterov and V. G. Spokoiny · 2017
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Harnessing smoothness to accelerate distributed optimization
G. Qu and N. Li · 2017
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
K. Scaman, F. Bach, S. Bubeck, Y. T. Lee, and L. Massoulié · 2017
Cited alongside, same era.
Minimizing finite sums with the stochastic average gradient
M. Schmidt, N. Le Roux, and F. Bach · 2017
Cited alongside, same era.
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback
O. Shamir · 2017
Cited alongside, same era.
An optimal algorithm for bandit and zero-order convex optimization with two-point feedback
Decentralized proximal gradient algorithms with linear convergence rates
S. A. Alghunaim, E. K. Ryu, K. Yuan, and A. H. Sayed · 2020
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Derivative-free method for composite optimization with applications to decentralized distributed optimization
A. Beznosikov, E. Gorbunov, and A. Gasnikov · 2020
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On biased compression for distributed learning
A. Beznosikov, S. Horváth, P. Richtárik, and M. Safaryan · 2020
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Gradient-free methods with inexact oracle for convex-concave stochastic saddle-point problem
A. Beznosikov, A. Sadiev, and A. Gasnikov · 2020
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Stochastic approximation versus sample average approximation for population wasserstein barycenters
D. Dvinskikh · 2020
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O. Shamir · 2017
Cited alongside, same era.
Optimal algorithms for distributed optimization
C. A. Uribe, S. Lee, A. Gasnikov, and A. Nedić · 2017
Cited alongside, same era.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
W. Wen, C. Xu, F. Yan, C. Wu, Y. Wang, Y. Chen, and H. Li · 2017
Cited alongside, same era.
How to make the gradients small stochastically: Even faster convex and nonconvex sgd
Z. Allen-Zhu · 2018
Cited alongside, same era.
Mirror descent and convex optimization problems with non-smooth inequality constraints
A. Bayandina, P. Dvurechensky, A. Gasnikov, F. Stonyakin, and A. Titov · 2018
Cited alongside, same era.
Towards optimal running times for optimal transport
J. Blanchet, A. Jambulapati, C. Kent, and A. Sidford · 2018
Cited alongside, same era.
Decentralize and randomize: Faster algorithm for wasserstein barycenters
P. Dvurechenskii, D. Dvinskikh, A. Gasnikov, C. Uribe, and A. Nedich · 2018
Cited alongside, same era.
D. Dvinskikh, A. Gasnikov, A. Rogozin, and A. Beznosikov · 2020
Closest in time.
Accelerated and non accelerated stochastic gradient descent in model generality
D. M. Dvinskikh, A. I. Turin, A. V. Gasnikov, and S. S. Omelchenko · 2020
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Local sgd: Unified theory and new efficient methods
E. Gorbunov, F. Hanzely, and P. Richtárik · 2020
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A unified theory of sgd: Variance reduction, sampling, quantization and coordinate descent
E. Gorbunov, F. Hanzely, and P. Richtárik · 2020
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Linearly converging error compensated sgd
E. Gorbunov, D. Kovalev, D. Makarenko, and P. Richtárik · 2020
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An optimal algorithm for decentralized finite sum optimization
H. Hendrikx, F. Bach, and L. Massoulie · 2020
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Statistically preconditioned accelerated gradient method for distributed optimization
H. Hendrikx, L. Xiao, S. Bubeck, F. Bach, and L. Massoulie · 2020
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Tighter theory for local sgd on identical and heterogeneous data
A. Khaled, K. Mishchenko, and P. Richtárik · 2020
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A unified theory of decentralized sgd with changing topology and local updates
A. Koloskova, N. Loizou, S. Boreiri, M. Jaggi, and S. U. Stich · 2020
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Optimal and practical algorithms for smooth and strongly convex decentralized optimization
D. Kovalev, A. Salim, and P. Richtárik · 2020
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First-order and Stochastic Optimization Methods for Machine Learning
G. Lan · 2020
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Decentralized accelerated gradient methods with increasing penalty parameters
H. Li, C. Fang, W. Yin, and Z. Lin · 2020
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Revisiting extra for smooth distributed optimization
H. Li and Z. Lin · 2020
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H. Li, Z. Lin, and Y. Fang · 2020
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Near-optimal algorithms for minimax optimization
T. Lin, C. Jin, and M. I. Jordan · 2020
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A decentralized parallel algorithm for training generative adversarial nets
M. Liu, W. Zhang, Y. Mroueh, X. Cui, J. Ross, T. Yang, and P. Das · 2020
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Distributed gradient methods for convex machine learning problems in networks: Distributed optimization
A. Nedic · 2020
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Penalty-based method for decentralized optimization over time-varying graphs
A. Rogozin and A. Gasnikov · 2020
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Y. Sun, A. Daneshmand, and G. Scutari · 2020
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Distributed zero-order algorithms for nonconvex multi-agent optimization
Y. Tang, J. Zhang, and N. Li · 2020
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A dual approach for optimal algorithms in distributed optimization over networks
C. A. Uribe, S. Lee, A. Gasnikov, and A. Nedić · 2020
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Minibatch vs local sgd for heterogeneous distributed learning
B. Woodworth, K. K. Patel, and N. Srebro · 2020
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Is local sgd better than minibatch sgd?
B. Woodworth, K. K. Patel, S. U. Stich, Z. Dai, B. Bullins, H. B. McMahan, O. Shamir, and N. Srebro · 2020
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A catalyst framework for minimax optimization
J. Yang, S. Zhang, N. Kiyavash, and N. He · 2020
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Multi-consensus decentralized accelerated gradient descent
H. Ye, L. Luo, Z. Zhou, and T. Zhang · 2020
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Decentralized accelerated proximal gradient descent
H. Ye, Z. Zhou, L. Luo, and T. Zhang · 2020
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Decentralized local stochastic extra-gradient for variational inequalities
A. Beznosikov, P. Dvurechensky, A. Koloskova, V. Samokhin, S. U. Stich, and A. Gasnikov · 2021
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Optimal distributed algorithms for stochastic variational inequalities
A. Beznosikov, D. Kovalev, A. Sadiev, P. Richtarik, and A. Gasnikov · 2021
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Near-optimal decentralized algorithms for saddle point problems over time-varying networks
A. Beznosikov, A. Rogozin, D. Kovalev, and A. Gasnikov · 2021
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Distributed saddle-point problems under data similarity
A. Beznosikov, G. Scutari, A. Rogozin, and A. Gasnikov · 2021
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Decentralized algorithms for wasserstein barycenters
D. Dvinskikh · 2021
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Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems
D. Dvinskikh and A. Gasnikov · 2021
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Improved complexity bounds in wasserstein barycenter problem
D. Dvinskikh and D. Tiapkin · 2021
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Accelerated meta-algorithm for convex optimization problems
A. Gasnikov, D. Dvinskikh, P. Dvurechensky, D. Kamzolov, V. Matyukhin, D. Pasechnyuk, N. Tupitsa, and A. Chernov · 2021
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On a combination of alternating minimization and nesterov’s momentum
S. Guminov, P. Dvurechensky, N. Tupitsa, and A. Gasnikov · 2021
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An improved analysis of gradient tracking for decentralized machine learning
A. Koloskova, T. Lin, and S. U. Stich · 2021
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Lower bounds and optimal algorithms for smooth and strongly convex decentralized optimization over time-varying networks
D. Kovalev, E. Gasanov, A. Gasnikov, and P. Richtarik · 2021
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Adom: Accelerated decentralized optimization method for time-varying networks
D. Kovalev, E. Shulgin, P. Richtárik, A. Rogozin, and A. Gasnikov · 2021
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Mirror-prox sliding methods for solving a class of monotone variational inequalities
G. Lan and Y. Ouyang · 2021
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Accelerated gradient tracking over time-varying graphs for decentralized optimization
H. Li and Z. Lin · 2021
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Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data
T. Lin, S. P. Karimireddy, S. U. Stich, and M. Jaggi · 2021
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Distributed stochastic gradient tracking methods
S. Pu and A. Nedić · 2021
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Decentralized distributed optimization for saddle point problems
A. Rogozin, A. Beznosikov, D. Dvinskikh, D. Kovalev, P. Dvurechensky, and A. Gasnikov · 2021
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An accelerated method for decentralized distributed stochastic optimization over time-varying graphs
A. Rogozin, M. Bochko, P. Dvurechensky, A. Gasnikov, and V. Lukoshkin · 2021
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Towards accelerated rates for distributed optimization over time-varying networks
A. Rogozin, V. Lukoshkin, A. Gasnikov, D. Kovalev, and E. Shulgin · 2021
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Optimal gradient tracking for decentralized optimization
Z. Song, L. Shi, S. Pu, and M. Yan · 2021
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Provably accelerated decentralized gradient method over unbalanced directed graphs
Z. Song, L. Shi, S. Pu, and M. Yan · 2021
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I. Stepanov, A. Voronov, A. Beznosikov, and A. Gasnikov · 2021
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Inexact model: A framework for optimization and variational inequalities
F. Stonyakin, A. Tyurin, A. Gasnikov, P. Dvurechensky, A. Agafonov, D. Dvinskikh, M. Alkousa, D. Pasechnyuk, S. Artamonov, and V. Piskunova · 2021
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Acceleration in distributed optimization under similarity
Y. Tian, G. Scutari, T. Cao, and A. Gasnikov · 2021
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On accelerated methods for saddle-point problems with composite structure
V. Tominin, Y. Tominin, E. Borodich, D. Kovalev, A. Gasnikov, and P. Dvurechensky · 2021
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An accelerated method for derivative-free smooth stochastic convex optimization
E. Gorbunov, P. Dvurechensky, and A. Gasnikov · 2022
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