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A Second-Order Method for Strongly Convex L1-Regularization Problems
Kimon Fountoulakis (K.Fountoulakis Abstract: In this paper a robust second-order method is developed for the solution of strongly convex l1-regularized problems. The main aim is to make the proposed method as inexpensive as possible, while even difficult problems can be efficiently solved. The proposed method is a primal-dual Newton Conjugate Gradients (pdNCG) method. Convergence properties of pdNCG are studied and worst-case iteration complexity is established. Numerical results are presented on a synthetic sparse least-squares problem and two real world machine learning problems. Keywords: L1-regularization, Strongly convex optimization, Second-order methods, Iteration Complexity, Newton Conjugate-Gradients method Category 1: Convex and Nonsmooth Optimization Citation: Technical Report ERGO-13-011 Download: [PDF] Entry Submitted: 06/22/2013 Modify/Update this entry | ||
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