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We study a Newton-like method for the minimization of an objective function that is the sum of a smooth convex function and an l-1 regularization term.
Iterative Solution of Nonlinear Equations in Several Variables
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Representations of quasi-newton matrices and their use in limited memory methods
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Nonlinear Programming and Variational Inequality Problems, a Unified Approach
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Cost approximation: a unified framework of descent algorithms for nonlinear programs
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J. Nocedal and S. J. Wright · 1999
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Convex optimization techniques for fitting sparse Gaussian graphical models
O. Banerjee, L. El Ghaoui, A. d’Aspremont, and G. Natsoulis · 2006
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Gaussian Markov random fields: theory and applications
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An inexact interior point method for L1-regularized sparse covariance selection
L. Li and K. C. Toh · 2010
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A semismooth newton method with multi-dimensional filter globalization for l1-optimization
Optimization for Machine Learning
S. Sra, S. Nowozin, and S.J. Wright · 2011
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Sample size selection in optimization methods for machine learning
Richard H Byrd, Gillian M Chin, Jorge Nocedal, and Yuchen Wu · 2012
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A family of second-order methods for convex L1 regularized optimization
Byrd, R., G. M Chin, J. Nocedal and F. Oztoprak · 2012
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Proximal newton-type methods for minimizing composite functions
Jason D Lee, Yuekai Sun, and Michael A Saunders · 2012
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Newton-like methods for sparse inverse covariance estimation
Peder Olsen, Figen Oztoprak, Jorge Nocedal, and Steven Rennie · 2012
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Inexact and accelerated proximal point algorithms
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