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Optimizers for 'torch' deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in 'torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) 'adabelief' by Zhuang et al (2020), <doi:10.48550/arXiv.2010.07468>; (b) 'adabound' by Luo et al.(2019), <doi:10.48550/arXiv.1902.09843>; (c) 'adahessian' by Yao et al.(2021) <doi:10.48550/arXiv.2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019), <doi:10.48550/arXiv.1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021), <doi:10.48550/arXiv.2101.11075>; (f) 'nadam' by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by Ma and Yarats(2019), <doi:10.48550/arXiv.1810.06801>; (h) 'radam' by Liu et al. (2019), <doi:10.48550/arXiv.1908.03265>; (i) 'swats' by Shekar and Sochee (2018), <doi:10.48550/arXiv.1712.07628>; (j) 'yogi' by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
| Version: | 0.1.4 |
| Depends: | R (≥ 4.0.0) |
| Imports: | graphics, grDevices, stats, torch |
| Suggests: | testthat |
| Published: | 2023-06-06 |
| Author: | Gilberto Camara [aut, cre], Rolf Simoes [aut], Daniel Falbel [aut], Felipe Souza [aut] |
| Maintainer: | Gilberto Camara <gilberto.camara.inpe at gmail.com> |
| License: | Apache License (≥ 2) |
| URL: | https://github.com/e-sensing/torchopt/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | NEWS |
| CRAN checks: | torchopt results |
| Reference manual: | torchopt.pdf |
| Package source: | torchopt_0.1.4.tar.gz |
| Windows binaries: | r-devel: torchopt_0.1.4.zip, r-release: torchopt_0.1.4.zip, r-oldrel: torchopt_0.1.4.zip |
| macOS binaries: | r-release (arm64): torchopt_0.1.4.tgz, r-oldrel (arm64): torchopt_0.1.4.tgz, r-release (x86_64): torchopt_0.1.4.tgz, r-oldrel (x86_64): torchopt_0.1.4.tgz |
| Old sources: | torchopt archive |
| Reverse suggests: | sits |
Please use the canonical form https://CRAN.R-project.org/package=torchopt to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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