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

author: Ryota Tomioka, Toyota Technological Institute at Chicago
published: Jan. 19, 2010,   recorded: December 2009,   views: 7403


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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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