DESCRIPTION

Type: Package
Package: torchopt
Title: Advanced Optimizers for Torch
Version: 0.1.2
Authors@R: c(
    person("Gilberto", "Camara", , "gilberto.camara@inpe.br", role = c("aut", "cre")),
    person("Rolf", "Simoes", , "rolf.simoes@inpe.br", role = "aut"),
    person("Daniel", "Falbel", , "daniel.falbel@gmail.com", role = "aut"),
    person("Felipe", "Souza", , "felipe.carvalho@inpe.br", role = "aut"),
    person("Alber", "Sanchez", , "alber.ipia@inpe.br", role = "aut")
  )
Maintainer: Gilberto Camara <gilberto.camara@inpe.br>
Description: 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),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (d) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (e) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (f) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (g) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (h) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (i) 'yogi' by Zaheer et al.(2019),
    <https:://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>. 
License: Apache License (>= 2)
URL: https://github.com/e-sensing/torchopt/
Depends: 
    R (>= 4.0.0)
Imports:
    graphics,
    grDevices,
    stats,
    torch
Suggests:
    testthat
ByteCompile: true
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2