Pathwise coordinate optimization for sparse learning: Algorithm and theory
نویسندگان
چکیده
منابع مشابه
Pathwise Coordinate Optimization for Sparse Learning: Algorithm and Theory
The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. Such a complex algorithmic structure grants superior ...
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The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. Such a complex algorithmic structure grants superior ...
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The pathwise coordinate optimization is one of the most important computational frameworks for solving high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: warm start initialization, active set updating, and strong rule for coordinate preselection. These three features grant superior empirical...
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We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the lterature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorithms are not often used in convex optimization. We show that this algorithm is very competitive with...
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Sparse inverse covariance selection is a fundamental problem for analyzing dependencies in high dimensional data. However, such a problem is difficult to solve since it is NP-hard. Existing solutions are primarily based on convex approximation and iterative hard thresholding, which only lead to sub-optimal solutions. In this work, we propose a coordinate-wise optimization algorithm to solve thi...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2018
ISSN: 0090-5364
DOI: 10.1214/17-aos1547