Distributed coordinate descent method for learning with big data

Journal of Machine Learning Research

Volumn 17, Issue , 2016, Pages

  Richtárik, Peter ^a   Takáč, Martin ^b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b Lehigh University   (United States)

Author keywords

Boosting; Distributed algorithms; Parallel coordinate descent; Stochastic methods

Indexed keywords

ITERATIVE METHODS; PARALLEL ALGORITHMS; STOCHASTIC SYSTEMS;

BOOSTING; CLOSED-FORM FORMULAE; COORDINATE DESCENT METHODS; HYBRID COORDINATE; NUMBER OF ITERATIONS; NUMERICAL EXPERIMENTS; PARALLEL COORDINATES; STOCHASTIC METHODS;

BIG DATA;

EID: 84979902257     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. J. Bradley, A. Kyrola, D. Bickson, and C. Guestrin. Parallel coordinate descent for l1-regularized loss minimization. In ICML, 2011.
2. O. Fercoq. Parallel coordinate descent for the AdaBoost problem. In ICMLA, 2013.
3. O. Fercoq and P. Richtárik. Smooth minimization of nonsmooth functions with parallel coordinate descent methods. arXiv:1309.5885, 2013.
4. O. Fercoq and P. Richtárik. Accelerated, parallel and proximal coordinate descent. SIAM Journal on Optimization, 25(4):1997-2023, 2015.
5. C-J. Hsieh, K-W. Chang, C-J. Lin, S.S. Keerthi,, and S. Sundarajan. A dual coordinate descent method for large-scale linear SVM. In ICML, 2008.
6. Libsvm. Datasets. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html.
7. Z. Lu and L. Xiao. On the complexity analysis of randomized block-coordinate descent methods. Mathematical Programming, 152(1):615-642, 2015.
8. I. Mukherjee, Y. Singer, R. Frongillo, and K. Canini. Parallel boosting with momentum. In ECML, 2013.
9. I. Necoara, Yu. Nesterov, and F. Glineur. Efficiency of randomized coordinate descent methods on optimization problems with linearly coupled constraints. Technical report, 2012.
10. Yu. Nesterov. Efficiency of coordinate descent methods on huge-scale optimization problems. SIAM Journal on Optimization, 22(2):341-362, 2012.
11. Yu. Nesterov. Gradient methods for minimizing composite objective function. Mathematical Programming, 140(1):125-161, 2013.
12. P. Richtárik and M. Takáč. Parallel coordinate descent methods for big data optimization. Mathematical Programming, pages 1-52, 2015.
13. P. Richtárik and M. Takáč. Efficient serial and parallel coordinate descent methods for huge-scale truss topology design. In Operations Research Proceedings, pages 27-32. Springer, 2012.
14. P. Richtárik and M. Takáč. Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function. Mathematical Programming, 144(2):1-38, 2014.
15. 1 regularized loss minimization. In ICML, 2009.
16. S. Shalev-Shwartz and T. Zhang. Accelerated mini-batch stochastic dual coordinate ascent. In NIPS, pages 378-385, 2013a.
17. S. Shalev-Shwartz and T. Zhang. Stochastic dual coordinate ascent methods for regularized loss minimization. Journal of Machine Learning Research, 14:567-599, 2013b.
18. S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter. Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1):3-30, 2011.
19. M. Takáč, A. Bijral, P. Richtárik, and N. Srebro. Mini-batch primal and dual methods for SVMs. In ICML, 2013.
20. R. Tappenden, P. Richtárik, and J. Gondzio. Inexact coordinate descent: complexity and preconditioning. arXiv:1304.5530, 2013.
21. R. Tappenden, P. Richtárik, and B. Büke. Separable approximations and decomposition methods for the augmented Lagrangian. Optimization Methods and Software, 30(3):643-668, 2015.