Fetching the paper…
Reading the bibliography…
We consider minimizing the sum of three convex functions, where the first one F is smooth, the second one is nonsmooth and proximable and the third one is the composition of a nonsmooth proximable function with a linear operator L.
Revue Française d’Automatique, Informatique et Recherche Opérationnelle 9
Glowinski, R., Marrocco, A.: Sur l’approximation par éléments finis d’ordre un, et la résolution par pénalisation-dualité d’une classe de problèmes de Dirichlet non linéaires · 1975
Earlier work this paper cites.
Computers & Mathematics with Applications 2
Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximation · 1976
Earlier work this paper cites.
SIAM J. Numer. Anal. 16
Lions, P.L., Mercier, B.: Splitting algorithms for the sum of two nonlinear operators · 1979
Earlier work this paper cites.
Math. Program. 55
Eckstein, J., Bertsekas, D.P.: On the Douglas–Rachford splitting method and the proximal point algorithm for maximal monotone operators · 1992
Earlier work this paper cites.
Phys. D 60
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms · 1992
Earlier work this paper cites.
Cambridge University Press (2004)
Boyd, S., Vandenberghe, L.: Convex Optimization · 2004
Earlier work this paper cites.
Math. Program. 111
Eckstein, J., Svaiter, B.F.: A family of projective splitting methods for the sum of two maximal monotone operators · 2008
Earlier work this paper cites.
Cambridge University Press (2009)
Palomar, D.P., Eldar, Y.C. (eds.): Convex Optimization in Signal Processing and Communications · 2009
Earlier work this paper cites.
SIAM J. Imaging Sci. 3
Bredies, K., Kunisch, K., Pock, T.: Total generalized variation · 2010
Earlier work this paper cites.
In: H.H. Bauschke, R. Burachik, P.L. Combettes, V. Elser, D.R. Luke, H. Wolkowicz (eds.) Fixed-Point Algorithms for Inverse Problems in Science and Engineering. Springer-Verlag, New York (2010)
Combettes, P.L., Pesquet, J.C.: Proximal splitting methods in signal processing · 2010
Earlier work this paper cites.
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010)
2010
Earlier work this paper cites.
Cambridge University Press (2010)
Starck, J.L., Murtagh, F., Fadili, J.: Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity · 2010
Earlier work this paper cites.
Found. Trends Mach. Learn. 3
Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers · 2011
Earlier work this paper cites.
J. Math. Imaging Vision 40
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging · 2011
Earlier work this paper cites.
ACM Transactions on Intelligent Systems and Technology (TIST) 2
Chang, C.C., Lin, C.J.: LibSVM: A library for support vector machines · 2011
Earlier work this paper cites.
Inverse Problems 27
Loris, I., Verhoeven, C.: On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty · 2011
Earlier work this paper cites.
SIAM J. Control Optim. 49
Svaiter, B.F.: On weak convergence of the Douglas–Rachford method · 2011
Earlier work this paper cites.
Found. Trends Mach. Learn. 4
Bach, F., Jenatton, R., Mairal, J., Obozinski, G.: Optimization with sparsity-inducing penalties · 2012
Earlier work this paper cites.
Set-Val. Var. Anal. 20
Combettes, P.L., Pesquet, J.C.: Primal–dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum type monotone operators · 2012
Earlier work this paper cites.
Inverse Problems 29
Chen, P., Huang, J., Zhang, X.: A primal–dual fixed point algorithm for convex separable minimization with applications to image restoration · 2013
Earlier work this paper cites.
J. Optim. Theory Appl. 158
Condat, L.: A primal-dual splitting method for convex optimization involving Lipschitzian, proximable and linear composite terms · 2013
Earlier work this paper cites.
SIAM J. Imaging Sci. 6
Couprie, C., Grady, L., Najman, L., Pesquet, J.C., Talbot, H.: Dual constrained TV-based regularization on graphs · 2013
Earlier work this paper cites.
In: C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Weinberger (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 315–323. Curran Associates, Inc. (2013)
Johnson, R., Zhang, T.: Accelerating stochastic gradient descent using predictive variance reduction · 2013
Earlier work this paper cites.
Adv. Comput. Math. 38
Vũ, B.C.: A splitting algorithm for dual monotone inclusions involving cocoercive operators · 2013
Earlier work this paper cites.
In: C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Weinberger (eds.) Advances in Neural Information Processing Systems, vol. 26. Curran Associates, Inc. (2013)
Zhang, L., Mahdavi, M., Jin, R.: Linear convergence with condition number independent access of full gradients · 2013
Earlier work this paper cites.
SIAM Journal on Optimization 24
Alotaibi, A., Combettes, P.L., Shahzad, N.: Solving coupled composite monotone inclusions by successive Fejér approximations of their Kuhn–Tucker set · 2014
Earlier work this paper cites.
In: P.M. Pardalos, T.M. Rassias (eds.) Mathematics Without Boundaries: Surveys in Interdisciplinary Research, pp. 57–99. Springer New York (2014)
Boţ, R.I., Csetnek, E.R., Hendrich, C.: Recent developments on primal–dual splitting methods with applications to convex minimization · 2014
Earlier work this paper cites.
In: Proc. of IEEE ICIP. Paris, France (2014)
Combettes, P.L., Condat, L., Pesquet, J.C., Vũ, B.C.: A forward–backward view of some primal–dual optimization methods in image recovery · 2014
Cited alongside, same era.
IEEE Signal Process. Lett. 21
Condat, L.: A generic proximal algorithm for convex optimization—Application to total variation minimization · 2014
Cited alongside, same era.
In: Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, K. Weinberger (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014)
Defazio, A., Bach, F., Lacoste-Julien, S.: Saga: A fast incremental gradient method with support for non-strongly convex composite objectives · 2014
Cited alongside, same era.
Foundations and Trends in Optimization 3
Parikh, N., Boyd, S.: Proximal algorithms · 2014
Cited alongside, same era.
SIAM Journal on Optimization 24
Xiao, L., Zhang, T.: A proximal stochastic gradient method with progressive variance reduction · 2014
Cited alongside, same era.
SIAM J. Imaging Sci. 12
Combettes, P.L., Glaudin, L.E.: Proximal activation of smooth functions in splitting algorithms for convex image recovery · 2019
Later among the works it cites.
In: K. Chaudhuri, R. Salakhutdinov (eds.) Proc. of 36th Int. Conf. Machine Learning (ICML), vol. PMLR 97, pp. 5200–5209 (2019)
Gower, R.M., Loizou, N., Qian, X., Sailanbayev, A., Shulgin, E., Richtárik, P.: SGD: General analysis and improved rates · 2019
Later among the works it cites.
SIAM Journal on Optimization 29
Johnstone, P.R., Eckstein, J.: Convergence rates for projective splitting · 2019
Later among the works it cites.
In: K. Chaudhuri, M. Sugiyama (eds.) Proc. of Int. Conf. Artif. Intell. Stat. (AISTATS), vol. PMLR 89, pp. 1–10 (2019)
Pedregosa, F., Fatras, K., Casotto, M.: Proximal splitting meets variance reduction · 2019
Later among the works it cites.
IEEE Trans. Automat. Contr. (2019)
Salim, A., Bianchi, P., Hachem, W.: Snake: a stochastic proximal gradient algorithm for regularized problems over large graphs · 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…
Drori, Y., Sabach, S., Teboulle, M.: A simple algorithm for a class of nonsmooth convex concave saddle-point problems · 2015
Cited alongside, same era.
In: C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, R. Garnett (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 2305–2313. Curran Associates, Inc. (2015)
Hofmann, T., Lucchi, A., Lacoste-Julien, S., McWilliams, B.: Variance reduced stochastic gradient descent with neighbors · 2015
Cited alongside, same era.
IEEE Signal Process. Mag. 32
Komodakis, N., Pesquet, J.C.: Playing with duality: An overview of recent primal–dual approaches for solving large-scale optimization problems · 2015
Cited alongside, same era.
Statist. Sci. 30
Polson, N.G., Scott, J.G., Willard, B.T.: Proximal algorithms in statistics and machine learning · 2015
Cited alongside, same era.
SIAM J. Optim. 25
Shi, W., Ling, Q., Wu, G., Yin, W.: EXTRA: An exact first-order algorithm for decentralized consensus optimization · 2015
Cited alongside, same era.
Math. Program. 151
Wright, S.J.: Coordinate descent algorithms · 2015
Cited alongside, same era.
Acta Numerica 25
Chambolle, A., Pock, T.: An introduction to continuous optimization for imaging · 2016
Cited alongside, same era.
IEEE Journal on Selected Areas in Information Theory 1
Basu, D., Data, D., Karakus, C., Diggavi, S.N.: Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations · 2020
Closest in time.
In: S. Chiappa, R. Calandra (eds.) Proc. of Int. Conf. Artif. Intell. Stat. (AISTATS), vol. PMLR 108, pp. 680–690 (2020)
Gorbunov, E., Hanzely, F., Richtárik, P.: A unified theory of SGD: Variance reduction, sampling, quantization and coordinate descent · 2020
Closest in time.
Proc. of the IEEE 108
Gower, R.M., Schmidt, M., Bach, F., Richtárik, P.: Variance-reduced methods for machine learning · 2020
Closest in time.
In: A. Kontorovich, G. Neu (eds.) Proc. of Int. Conf. Algo. Learn. Theory (ALT), vol. PMLR 117, pp. 451–467 (2020)
Kovalev, D., Horváth, S., Richtárik, P.: Don’t jump through hoops and remove those loops: SVRG and Katyusha are better without the outer loop · 2020
Closest in time.
In: H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 18342–18352. Curran Associates, Inc. (2020)
Kovalev, D., Salim, A., Richtárik, P.: Optimal and practical algorithms for smooth and strongly convex decentralized optimization · 2020
Closest in time.
Springer Cham (2020)
Lan, G.: First-order and Stochastic Optimization Methods for Machine Learning · 2020
Closest in time.
SIAM J. Optim. 30
Li, H., Lin, Z.: Revisiting EXTRA for smooth distributed optimization · 2020
Closest in time.
IEEE Signal Processing Magazine 3
Li, T., Sahu, A.K., Talwalkar, A., Smith, V.: Federated learning: Challenges, methods, and future directions · 2020
Closest in time.
Math. Program. 79
O’Connor, D., Vandenberghe, L.: On the equivalence of the primal-dual hybrid gradient method and Douglas–Rachford splitting · 2020
Closest in time.
Math. Program. 182
Ryu, E.K.: Uniqueness of DRS as the 2 operator resolvent-splitting and impossibility of 3 operator resolvent-splitting · 2020
Closest in time.
IEEE Trans. Neural Networks and Learning Systems 31
Sattler, F., Wiedemann, S., K.-R. Müller, Samek, W.: Robust and communication-efficient federated learning from non-i.i.d. data · 2020
Closest in time.
arXiv preprint arXiv:2002.11534 (2020)
Xu, J., Tian, Y., Sun, Y., Scutari, G.: Distributed algorithms for composite optimization: Unified and tight convergence analysis · 2020
Closest in time.
IEEE Transactions on Automatic Control 66
Alghunaim, S.A., Ryu, E.K., Yuan, K., Sayed, A.H.: Decentralized proximal gradient algorithms with linear convergence rates · 2021
Closest in time.
IEEE Transactions on Signal Processing 69
Combettes, P.L., Pesquet, J.C.: Fixed point strategies in data science · 2021
Closest in time.
Computational Optimization and Applications 78
Johnstone, P.R., Eckstein, J.: Single-forward-step projective splitting: exploiting cocoercivity · 2021
Closest in time.
Can J Statistics (2021)
Tay, J.K., Friedman, J., Tibshirani, R.: Principal component-guided sparse regression · 2021
Closest in time.
In: Proc. of 41st IEEE Int. Conf. Distributed Computing Systems (ICDCS), pp. 561–572 (2021)
Xu, H., Ho, C.Y., Abdelmoniem, A.M., Dutta, A., Bergou, E.H., Karatsenidis, K., Canini, M., Kalnis, P.: GRACE: A compressed communication framework for distributed machine learning · 2021
Closest in time.
SIAM Review (2022)
Condat, L., Kitahara, D., Contreras, A., Hirabayashi, A.: Proximal splitting algorithms for convex optimization: A tour of recent advances, with new twists · 2022
Closest in time.
Frontiers in Signal Processing 1
Condat, L., Malinovsky, G., Richtárik, P.: Distributed proximal splitting algorithms with rates and acceleration · 2022
Closest in time.
Math. Program. 191
Johnstone, P.R., Eckstein, J.: Projective splitting with forward steps · 2022
Closest in time.
In: G. Camps-Valls, F.J.R. Ruiz, I. Valera (eds.) Proc. of Int. Conf. Artif. Intell. Stat. (AISTATS), vol. PMLR 151, pp. 4482–4498 (2022)
Salim, A., Condat, L., Kovalev, D., Richtárik, P.: An optimal algorithm for strongly convex minimization under affine constraints · 2022
Closest in time.