NIPS Proceedingsβ

A quasi-Newton proximal splitting method

Part of: Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Abstract

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.