Package: madgrad 0.2.0

madgrad: 'MADGRAD' Method for Stochastic Optimization

A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization algorithm. MADGRAD is a 'best-of-both-worlds' optimizer with the generalization performance of stochastic gradient descent and at least as fast convergence as that of Adam, often faster. A drop-in optim_madgrad() implementation is provided based on Defazio et al (2020) <doi:10.48550/arXiv.2101.11075>.

Authors:Daniel Falbel [aut, cre, cph], Posit Software, PBC [cph], MADGRAD original implementation authors. [cph]

madgrad_0.2.0.tar.gz
madgrad_0.2.0.zip(r-4.7)madgrad_0.2.0.zip(r-4.6)madgrad_0.2.0.zip(r-4.5)
madgrad_0.2.0.tgz(r-4.6-any)madgrad_0.2.0.tgz(r-4.5-any)
madgrad_0.2.0.tar.gz(r-4.7-any)madgrad_0.2.0.tar.gz(r-4.6-any)
madgrad_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
madgrad/json (API)
NEWS

# Install 'madgrad' in R:
install.packages('madgrad', repos = c('https://dfalbel.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 8 scripts 455 downloads 1 exports 23 dependencies

Last updated from:99ee7b02df. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK127
source / vignettesOK203
linux-release-x86_64OK149
macos-release-arm64OK113
macos-oldrel-arm64OK75
windows-develOK115
windows-releaseOK82
windows-oldrelOK75
wasm-releaseOK102

Exports:optim_madgrad

Dependencies:bitbit64callrclicorodescfarvergluejsonlitelabelinglifecyclemagrittrprocessxpsR6RColorBrewerRcpprlangsafetensorsscalestorchviridisLitewithr