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In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates.
Foundations and Trends in Machine Learning, vol. 3, pp. 1–122, 2010
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers · 2010
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Journal of Scientific Computing, pp. 1–28, 2012
W. Deng and W. Yin, On the global and linear convergence of the generalized alternating direction method of multipliers · 2012
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Mathematical Programming, pages 1–38, 2012
P. Richtárik and M. Takáč, Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function · 2012
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P. Richtárik and M. Takáč, Distributed coordinate descent method for learning with big data · 2013
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In Advances in NIPS 27, pp. 3068–3076, 2014
M. Jaggi, V. Smith, M. Takáč, J. Terhorst, S. Krishnan, T. Hofmann, and M. I. Jordan, Coordinate descent algorithms · 2014
Cited alongside, same era.
International Journal of Electrical Power and Energy Systems, vol. 60, pp. 126–140, 2014
P. Tufekci, Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods · 2014
Cited alongside, same era.
In ICML, pp. 362–370, 2015
Y. Zhang and L. Xiao, DiSCO: Distributed optimization for self-concordant empirical loss · 2015
Cited alongside, same era.
Mathematical Programming, vol. 151, no. 1, pp. 3–34, 2015
S. J. Wright, Coordinate descent algorithms · 2015
Cited alongside, same era.
S. Zhu, M. Hong, and B. Chen, “Quantized consensus admm for multi-agent distributed optimization,” in Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing
2016
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R. Tappenden, P. Richtárik, and J. Gondzio, “Inexact coordinate descent: complexity and preconditioning,” Journal of Optimization Theory and Applications
2016
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H. Mania, X. Pan, D. Papailiopoulos, B. Recht, K. Ramchandran, and M. I. Jordan, Perturbed iterate analysis for asynchronous stochastic optimization · 2016
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