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A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <doi:10.48550/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 |
| Reference manual: | fasta.pdf |
| Package source: | fasta_0.1.0.tar.gz |
| Windows binaries: | r-devel: fasta_0.1.0.zip, r-release: fasta_0.1.0.zip, r-oldrel: fasta_0.1.0.zip |
| macOS binaries: | r-release (arm64): fasta_0.1.0.tgz, r-oldrel (arm64): fasta_0.1.0.tgz, r-release (x86_64): fasta_0.1.0.tgz, r-oldrel (x86_64): fasta_0.1.0.tgz |
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