A quasi-Newton proximal splitting method

author: Jalal Fadili, Laboratoire GREYC, University of Caen Basse-Normandie
published: Jan. 14, 2013,   recorded: December 2012,   views: 153
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Description

We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the piece-wise linear nature of the dual problem. The second part of the paper applies the previous result to acceleration of convex minimization problems, and leads to an elegant quasi-Newton method. The optimization method compares favorably against state-of-the-art alternatives. The algorithm has extensive applications including signal processing, sparse regression and recovery, and machine learning and classification.

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Download slides icon Download slides: machine_fadili_splitting_method_01.pdf (1.3 MB)

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