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A quasi-Newton proximal splitting method
Stephen Becker · Jalal Fadili

Tue Dec 04 03:30 PM -- 03:34 PM (PST) @ Harveys Convention Center Floor, CC

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.

Author Information

Stephen Becker (Paris-6/CNRS)
Jalal Fadili (CNRS-ENSICAEN-Univ. Caen)

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