fasta: Fast Adaptive Shrinkage/Thresholding Algorithm

A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

Version: 0.1.0
Published: 2018-04-10
Author: Eric C. Chi [aut, cre, trl, cph], Tom Goldstein [aut] (MATLAB original, https://www.cs.umd.edu/~tomg/projects/fasta/), Christoph Studer [aut], Richard G. Baraniuk [aut]
Maintainer: Eric C. Chi <ecchi1105 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Citation: fasta citation info
CRAN checks: fasta results

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Reference manual: fasta.pdf
Package source: fasta_0.1.0.tar.gz
Windows binaries: r-devel: fasta_0.1.0.zip, r-devel-gcc8: fasta_0.1.0.zip, r-release: fasta_0.1.0.zip, r-oldrel: fasta_0.1.0.zip
OS X binaries: r-release: fasta_0.1.0.tgz, r-oldrel: fasta_0.1.0.tgz

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