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Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years.
Trust region methods
Conn, A. R., Gould, N. I., and Toint, P. L · 2000
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Cubic regularization of newton method and its global performance
Nesterov, Y. and Polyak, B. T · 2006
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Scalable training of L1-regularized log-linear models
Andrew, G. and Gao, J · 2007
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Self-concordant analysis for logistic regression
Bach, F. et al · 2010
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Hogwild!: A lock-free approach to parallelizing stochastic gradient descent
Niu, F., Recht, B., Ré, C., and Wright, S. J · 2011
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Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, S. and Zhang, T · 2013
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Trading computation for communication: Distributed stochastic dual coordinate ascent
Yang, T · 2013
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Communication-efficient distributed dual coordinate ascent
Jaggi, M., Smith, V., Takáč, M., Terhorst, J., Krishnan, S., Hofmann, T., and Jordan, M. I · 2014
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Communication-efficient distributed optimization using an approximate newton-type method
Shamir, O., Srebro, N., and Zhang, T · 2014
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Lee, J. D., Lin, Q., Ma, T., and Yang, T · 2015
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Disco: Distributed optimization for self-concordant empirical loss
Zhang, Y. and Lin, X · 2015
Cited alongside, same era.
Primal-dual rates and certificates
Dünner, C., Forte, S., Takáč, M., and Jaggi, M · 2016
Cited alongside, same era.
Communication-Efficient Parallel Block Minimization for Kernel Machines
Hsieh, C.-J., Si, S., and Dhillon, I. S · 2016
Cited alongside, same era.
Aide: Fast and communication efficient distributed optimization
Reddi, S. J., Konečnỳ, J., Richtárik, P., Póczós, B., and Smola, A · 2016
Cited alongside, same era.
A distributed block coordinate descent method for training l 1 regularized linear classifiers
Mahajan, D., Keerthi, S. S., and Sundararajan, S · 2017
Later among the works it cites.
Distributed coordinate descent for generalized linear models with regularization
Trofimov, I. and Genkin, A · 2017
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Giant: Globally improved approximate newton method for distributed optimization
Wang, S., Roosta-Khorasani, F., Xu, P., and Mahoney, M. W · 2017
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A general distributed dual coordinate optimization framework for regularized loss minimization
Zheng, S., Wang, J., Xia, F., Xu, W., and Zhang, T · 2017
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Global linear convergence of Newton’s method without strong-convexity or Lipschitz gradients
Karimireddy, S. P., Stich, S. U., and Jaggi, M · 2018
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Distributed coordinate descent method for learning with big data
Richtárik, P. and Takáč, M · 2016
Cited alongside, same era.
Hessian-cocoa: a general parallel and distributed framework for non-strongly convex regularizers
Gargiani, M · 2017
Cited alongside, same era.
Distributed block-diagonal approximation methods for regularized empirical risk minimization
Lee, C.-p. and Chang, K.-W · 2017
Cited alongside, same era.
Adaptive cubic regularisation methods for unconstrained optimization. part i: motivation, convergence and numerical results
Cartis, C., Gould, N. I. M., and Toint, P. L
Cited in the paper.
Adaptive cubic regularisation methods for unconstrained optimization. part ii: worst case function and derivative evaluation complexity
Cartis, C., Gould, N. I. M., and Toint, P. L
Cited in the paper.
Lee, C.-p. and Wright, S. J · 2018
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A distributed quasi-newton algorithm for empirical risk minimization with nonsmooth regularization
Lee, C.-p., Lim, C. H., and Wright, S. J · 2018
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CoCoA: A General Framework for Communication-Efficient Distributed Optimization
Smith, V., Forte, S., Ma, C., Takáč, M., Jordan, M. I., and Jaggi, M · 2018
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