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There is a recent interest on first-order methods for linear programming (LP).
Alan J Hoffman, On approximate solutions of systems of linear inequalities , Journal of Research of the National Bureau of Standards 49
1952
Earlier work this paper cites.
Abraham Charnes and William W Cooper, The stepping stone method of explaining linear programming calculations in transportation problems , Management science 1
1954
Earlier work this paper cites.
Edward H Bowman, Production scheduling by the transportation method of linear programming , Operations Research 4
1956
Earlier work this paper cites.
Jim Douglas and Henry H Rachford, On the numerical solution of heat conduction problems in two and three space variables , Transactions of the American mathematical Society 82
1956
Earlier work this paper cites.
Fred Hanssmann and Sidney W Hess, A linear programming approach to production and employment scheduling , Management science (1960), no. 1, 46–51
1960
Earlier work this paper cites.
Alan S Manne, Linear programming and sequential decisions , Management Science 6
1960
Earlier work this paper cites.
Galina M Korpelevich, The extragradient method for finding saddle points and other problems , Matecon 12
1976
Earlier work this paper cites.
R. Tyrrell Rockafellar, Monotone operators and the proximal point algorithm , SIAM Journal on Control and Optimization 14
1976
Earlier work this paper cites.
Boris Polyak, Sharp minima , Proceedings of the IIASA Workshop on Generalized Lagrangians and Their Applications, Laxenburg, Austria. Institute of Control Sciences Lecture Notes, Moscow, 1979
1979
Earlier work this paper cites.
Robert M Gower, Mark Schmidt, Francis Bach, and Peter Richtárik, Variance-reduced methods for machine learning , Proceedings of the IEEE 108
1983
Earlier work this paper cites.
Narendra Karmarkar, A new polynomial-time algorithm for linear programming , Proceedings of the sixteenth annual ACM symposium on Theory of computing, 1984, pp. 302–311
1984
Earlier work this paper cites.
Jonathan Eckstein, Dimitri P Bertsekas, et al., An alternating direction method for linear programming , (1990)
1990
Earlier work this paper cites.
Jonathan Eckstein and Dimitri P Bertsekas, On the Douglas—Rachford splitting method and the proximal point algorithm for maximal monotone operators , Mathematical Programming 55
1992
Earlier work this paper cites.
Paul Tseng, On linear convergence of iterative methods for the variational inequality problem , Journal of Computational and Applied Mathematics 60
1995
Earlier work this paper cites.
George Bernard Dantzig, Linear programming and extensions , vol. 48, Princeton university press, 1998
1998
Earlier work this paper cites.
Randy I Anderson, Robert Fok, and John Scott, Hotel industry efficiency: An advanced linear programming examination , American Business Review 18
2000
Earlier work this paper cites.
Arkadi Nemirovski, Prox-method with rate of convergence O(1/t) for variational inequalities with lipschitz continuous monotone operators and smooth convex-concave saddle point problems , SIAM Journal on Optimization 15
2004
Earlier work this paper cites.
Qian Liu and Garrett Van Ryzin, On the choice-based linear programming model for network revenue management , Manufacturing & Service Operations Management 10
2008
Earlier work this paper cites.
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro, Robust stochastic approximation approach to stochastic programming , SIAM Journal on optimization 19
2009
Earlier work this paper cites.
Antonin Chambolle and Thomas Pock, A first-order primal-dual algorithm for convex problems with applications to imaging , Journal of mathematical imaging and vision 40
2011
Earlier work this paper cites.
Jacek Gondzio, Interior point methods 25 years later , European Journal of Operational Research 218
2012
Cited alongside, same era.
Rie Johnson and Tong Zhang, Accelerating stochastic gradient descent using predictive variance reduction , Advances in neural information processing systems 26
2013
Cited alongside, same era.
Yurii Nesterov, Gradient methods for minimizing composite functions , Mathematical Programming 140
2013
Cited alongside, same era.
Aaron Defazio, Francis Bach, and Simon Lacoste-Julien, Saga: A fast incremental gradient method with support for non-strongly convex composite objectives , Advances in neural information processing systems, 2014, pp. 1646–1654
2014
Cited alongside, same era.
2019
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Puya Latafat, Nikolaos M Freris, and Panagiotis Patrinos, A new randomized block-coordinate primal-dual proximal algorithm for distributed optimization , IEEE Transactions on Automatic Control 64
2019
Later among the works it cites.
Kinjal Basu, Amol Ghoting, Rahul Mazumder, and Yao Pan, ECLIPSE: An extreme-scale linear program solver for web-applications , Proceedings of the 37th International Conference on Machine Learning (Virtual) (Hal Daumé III and Aarti Singh, eds.), Proceedings of Machine Learning Research, vol. 119, PMLR, 13–18 Jul 2020, pp. 704–714
2020
Later among the works it cites.
Dmitry Kovalev, Samuel Horváth, and Peter Richtárik, Don’t jump through hoops and remove those loops: Svrg and katyusha are better without the outer loop , Algorithmic Learning Theory, PMLR, 2020, pp. 451–467
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2015
Cited alongside, same era.
Hongzhou Lin, Julien Mairal, and Zaid Harchaoui, A universal catalyst for first-order optimization , Advances in neural information processing systems, 2015, pp. 3384–3392
2015
Cited alongside, same era.
Brendan O’Donoghue and Emmanuel Candes, Adaptive restart for accelerated gradient schemes , Foundations of computational mathematics 15
2015
Cited alongside, same era.
Laurent Condat, Fast projection onto the simplex and the l 1 ball , Mathematical Programming 158
2016
Cited alongside, same era.
Balamurugan Palaniappan and Francis Bach, Stochastic variance reduction methods for saddle-point problems , Advances in Neural Information Processing Systems, 2016, pp. 1416–1424
2016
Cited alongside, same era.
Mark Schmidt, Nicolas Le Roux, and Francis Bach, Minimizing finite sums with the stochastic average gradient , Mathematical Programming 162
2017
Cited alongside, same era.
Antonin Chambolle, Matthias J Ehrhardt, Peter Richtárik, and Carola-Bibiane Schonlieb, Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications , SIAM Journal on Optimization 28
2018
Cited alongside, same era.
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng, Training GANs with optimism , International Conference on Learning Representations, 2018
2018
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2020
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2020
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Aryan Mokhtari, Asuman Ozdaglar, and Sarath Pattathil, A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach , International Conference on Artificial Intelligence and Statistics, 2020
2020
Later among the works it cites.
Sebastian Pokutta, Restarting algorithms: Sometimes there is free lunch , International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Springer, 2020, pp. 22–38
2020
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Vincent Roulet and Alexandre d’Aspremont, Sharpness, restart, and acceleration , SIAM Journal on Optimization 30
2020
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2020
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2021
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2021
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2021
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2021
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Olivier Fercoq, Quadratic error bound of the smoothed gap and the restarted averaged primal-dual hybrid gradient , (2021)
2021
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Tianyi Lin, Shiqian Ma, Yinyu Ye, and Shuzhong Zhang, An admm-based interior-point method for large-scale linear programming , Optimization Methods and Software 36
2021
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2022
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2022
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Chaobing Song, Cheuk Yin Lin, Stephen Wright, and Jelena Diakonikolas, Coordinate linear variance reduction for generalized linear programming , Advances in Neural Information Processing Systems 35
2022
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