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| author | Yuri Victorovich <yuri@FreeBSD.org> | 2018-04-17 22:24:52 +0000 |
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| committer | Yuri Victorovich <yuri@FreeBSD.org> | 2018-04-17 22:24:52 +0000 |
| commit | ab3dbf7f9c950b4a383415ebe8f9be7a6a1cfe2c (patch) | |
| tree | 6094911adefcb6f11bea9496bf4938658801d041 /math/py-algopy/pkg-descr | |
| parent | ff9707fa619a2df8794b7d8de5d645c69565bf6c (diff) | |
| download | ports-ab3dbf7f9c950b4a383415ebe8f9be7a6a1cfe2c.tar.gz ports-ab3dbf7f9c950b4a383415ebe8f9be7a6a1cfe2c.zip | |
New port: math/py-algopy: Algorithmic Differentiation (AD) and Taylor polynomial approximations
Notes
Notes:
svn path=/head/; revision=467660
Diffstat (limited to 'math/py-algopy/pkg-descr')
| -rw-r--r-- | math/py-algopy/pkg-descr | 10 |
1 files changed, 10 insertions, 0 deletions
diff --git a/math/py-algopy/pkg-descr b/math/py-algopy/pkg-descr new file mode 100644 index 000000000000..214144cdf392 --- /dev/null +++ b/math/py-algopy/pkg-descr @@ -0,0 +1,10 @@ +The purpose of AlgoPy is the evaluation of higher-order derivatives in the +forward and reverse mode of Algorithmic Differentiation (AD) of functions +that are implemented as Python programs. Particular focus are functions that +contain numerical linear algebra functions as they often appear in statistically +motivated functions. The intended use of AlgoPy is for easy prototyping at +reasonable execution speeds. More precisely, for a typical program a directional +derivative takes order 10 times as much time as time as the function evaluation. +This is approximately also true for the gradient. + +WWW: https://pythonhosted.org/algopy/ |