Efficient Implementations of the Generalized Lasso Dual Path Algorithm
Abstract
We consider efficient implementations of the generalized lasso dual path algorithm of Tibshirani and Taylor (2011). We first describe a generic approach that covers any penalty matrix D and any (full column rank) matrix X of predictor variables. We then describe fast implementations for the special cases of trend filtering problems, fused lasso problems, and sparse fused lasso problems, both with X=I and a general matrix X. These specialized implementations offer a considerable improvement over the generic implementation, both in terms of numerical stability and efficiency of the solution path computation. These algorithms are all available for use in the genlasso R package, which can be found in the CRAN repository.
 Publication:

arXiv eprints
 Pub Date:
 May 2014
 DOI:
 10.48550/arXiv.1405.3222
 arXiv:
 arXiv:1405.3222
 Bibcode:
 2014arXiv1405.3222A
 Keywords:

 Statistics  Computation;
 Computer Science  Machine Learning;
 Statistics  Machine Learning