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dev-R/RcppEnsmallen: unbundle ensmallen
Signed-off-by: Alessandro Barbieri <lssndrbarbieri@gmail.com>
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@@ -1,25 +1,25 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
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<pkgmetadata>
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<longdescription>
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Header-Only C++ Mathematical Optimization Library for 'Armadillo'
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// 'Ensmallen' is a templated C++ mathematical optimization
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library (by the 'MLPACK' team) that provides a simple set of
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abstractions for writing an objective function to optimize.
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Provided within are various standard and cutting-edge
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optimizers that include full-batch gradient descent techniques,
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small-batch techniques, gradient-free optimizers, and
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constrained optimization. The 'RcppEnsmallen' package includes
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the header files from the 'Ensmallen' library and pairs the
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appropriate header files from 'armadillo' through the
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'RcppArmadillo' package. Therefore, users do not need to
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install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'.
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Note that 'Ensmallen' is licensed under 3-Clause BSD,
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'Armadillo' starting from 7.800.0 is licensed under Apache
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License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to
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'Armadillo') is licensed under the GNU GPL version 2 or later.
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Thus, 'RcppEnsmallen' is also licensed under similar terms.
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Note that 'Ensmallen' requires a compiler that supports 'C++11'
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and 'Armadillo' 8.400 or later.
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</longdescription>
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<!-- maintainer-needed -->
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<upstream>
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<bugs-to>https://github.com/coatless/rcppensmallen/issues</bugs-to>
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<doc lang="en">https://cran.r-project.org/web/packages/RcppEnsmallen/RcppEnsmallen.pdf</doc>
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<changelog>https://cran.r-project.org/web/packages/RcppEnsmallen/ChangeLog</changelog>
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<maintainer>
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<name>James Joseph Balamuta</name>
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<email>balamut2@illinois.edu</email>
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</maintainer>
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<remote-id type="github">coatless/rcppensmallen</remote-id>
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</upstream>
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<longdescription lang="en">
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Header-Only C++ Mathematical Optimization Library for 'Armadillo'
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// 'Ensmallen' is a templated C++ mathematical optimization
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library (by the 'MLPACK' team) that provides a simple set of
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abstractions for writing an objective function to optimize.
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Provided within are various standard and cutting-edge
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optimizers that include full-batch gradient descent techniques,
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small-batch techniques, gradient-free optimizers, and
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constrained optimization.
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</longdescription>
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</pkgmetadata>
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