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In this paper a robust second-order method is developed for the solution of strongly convex l1-regularized problems.
Analysis of bounded variation penalty methods for ill-posed problems
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Iterative Methods for Linear and Nonlinear Equations
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Convergence of a block coordinate descent method for nondifferentiable minimization
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Optimization for Machine Learning
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Feature engineering and classifier ensemble for kdd cup 2010
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CoSaMP and OMP for sparse recovery
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Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
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Cosamp: Iterative signal recovery from incomplete and inaccurate samples
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A coordinate gradient descent method for nonsmooth separable minimization
P. Tseng and S. Yun · 2009
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A practical guide to support vector classification
C.-W. Hsu, C.-C. Chang, and C.-J. Lin · 2010
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Nesta: A fast and accurate first-order method for sparse recovery
S. R. Becker, J. Bobin, and E. J. Candès · 2011
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Templates for convex cone problems with applications to sparse signal recovery
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Real-sim: Real vs. Simulated data for binary classification problem
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P. Richtárik and M. Takáč · 2012
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Parallel coordinate descent methods for big data optimization
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Efficiency of coordinate descent methods on huge-scale optimization problems
P. Tseng · 2012
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Accelerated block-coordinate relaxation for regularized optimization
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Recent advances of large-scale linear classification
G. X. Yuan, C. H. Ho, and C. J. Lin · 2012
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