Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparse Learning

author: Ryota Tomioka, Fraunhofer Institute for Intelligent Analysis and Information Systems
published: Jan. 19, 2010,   recorded: December 2009,   views: 657
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Description

We analyze the convergence behaviour of a recently proposed algorithm for sparse learning called Dual Augmented Lagrangian (DAL). We theoretically analyze under some conditions that DAL converges super-linearly in a non-asymptotic and global sense. We experimentally confirm our analysis in a large scale ℓ1-regularized logistic regression problem and compare the efficiency of DAL algorithm to existing algorithms.

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