Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96858
Title: A simple and efficient algorithm for fused lasso signal approximator with convex loss function
Authors: Wang, Lichun
You, Yuan
Lian, Heng
Issue Date: 2012
Source: Wang, L., You, Y.,& Lian, H. (2013). A simple and efficient algorithm for fused lasso signal approximator with convex loss function. Computational Statistics, 28(4), 1699-1714.
Series/Report no.: Computational statistics
Abstract: We consider the augmented Lagrangian method (ALM) as a solver for the fused lasso signal approximator (FLSA) problem. The ALM is a dual method in which squares of the constraint functions are added as penalties to the Lagrangian. In order to apply this method to FLSA, two types of auxiliary variables are introduced to transform the original unconstrained minimization problem into a linearly constrained minimization problem. Each updating in this iterative algorithm consists of just a simple one-dimensional convex programming problem, with closed form solution in many cases. While the existing literature mostly focused on the quadratic loss function, our algorithm can be easily implemented for general convex loss. We also provide some convergence analysis of the algorithm. Finally, the method is illustrated with some simulation datasets.
URI: https://hdl.handle.net/10356/96858
http://hdl.handle.net/10220/13109
DOI: http://dx.doi.org/10.1007/s00180-012-0373-6
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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