./wip/py-algopy, Taylor Arithmetic Computation and Algorithmic Differentiation

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Branch: CURRENT, Version: 0.5.0, Package name: py27-algopy-0.5.0, Maintainer: jihbed.research

LGOPY is a tool for Algorithmic Differentiation (AD) and
Taylor polynomial approximations. ALGOPY makes it possible to perform
computations on scalar and polynomial matrices. It is designed to be as
compatible to numpy as possible. I.e. views, broadcasting and most functions
of numpy can be performed on polynomial matrices. Exampels are dot,trace,qr,
solve, inv,eigh. The reverse mode of AD is also supported by a simple
code evaluation tracer.


Required to run:
[devel/py-setuptools] [math/py-numpy]

Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 205a0a589daf8942e07b28f11d53eab5298f36dc
RMD160: aa1a4f68ef57dc3e3d0112fd770152c9494b20f2
Filesize: 133.005 KB

Version history: (Expand)


CVS history: (Expand)


   2013-09-01 00:13:21 by Kamel Derouiche | Files touched by this commit (3) | Package updated
Log message:
UPDATE PACKAGE

   2012-10-06 19:13:24 by Aleksej Saushev | Files touched by this commit (44)
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2012-09-30 11:55:06 by ndb | Files touched by this commit (8)
Log message:
distinfo issues.
   2012-09-11 01:23:10 by Kamel Derouiche | Files touched by this commit (2) | Package updated
Log message:

	UPDATE VERSION
   2012-01-18 13:44:56 by ndb | Files touched by this commit (1)
Log message:
corrected distinfo.
   2011-04-25 23:00:36 by Kamel Derouiche | Files touched by this commit (2) | Package updated
Log message:

  update to 0.3.0
	o Makefile
	o PLIST
   2011-03-21 00:59:05 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import py26-algopy-0.2.2 as wip/py-algopy.

LGOPY is a tool for Algorithmic Differentiation (AD) and
Taylor polynomial approximations. ALGOPY makes it possible to perform
computations on scalar and polynomial matrices. It is designed to be as
compatible to numpy as possible. I.e. views, broadcasting and most functions
of numpy can be performed on polynomial matrices. Exampels are dot,trace,qr,
solve, inv,eigh. The reverse mode of AD is also supported by a simple
code evaluation tracer.