A second-order method for convex L1-regularized optimization with active-set prediction
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We describe an active-set method for the minimization of an objective function phi that is the sum of a smooth convex function f and an l(1)-regularization term. A distinctive feature of the method is the way in which active-set identification and second-order subspace minimization steps are integrated to combine the predictive power of the two approaches. At every iteration, the algorithm selects a candidate set of free and fixed variables, performs an (inexact) subspace phase, and then assesses the quality of the new active set. If it is not judged to be acceptable, then the set of free variables is restricted and a new active-set prediction is made. We establish global convergence for our approach under the assumptions of Lipschitz-continuity and strong-convexity of f, and compare the new method against state-of-the-art codes.
Açıklama
Anahtar Kelimeler
L(1)-Minimization, Second-Order, Active-Set Prediction, Active-Set Correction, Subspace-Optimization, 49m, 65k, 65h, 90c, Linear Inverse Problems, Thresholding Algorithm, Logistic-Regression, Coordinate Descent, Minimization, Shrinkage
Kaynak
Optimization Methods & Software
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
31
Sayı
3
