| CARVIEW |
b45ch1 / algopy
- Source
- Commits
- Network (0)
- Issues (5)
- Downloads (0)
- Wiki (1)
- Graphs
-
Branch:
master
-
Branches (2)
- dtype
- master ✓
- Tags (0)
Pledgie Donations
Once activated, we'll place the following badge in your repository's detail box:
AlgoPy is a Research Prototype for Algebraic Differentation in Python — Read more
| name | age | message | |
|---|---|---|---|
 |
.gitignore | Loading commit data...  |
|
| |
README.rst | ||
 |
algopy/ | ||
| |
deprecated/ | ||
| |
documentation/ | ||
| |
run_tests.py |
ALGOPY, a library for Automatic Differentation (AD) in Python
ALGOPY is a research prototype striving to provide state of the art algorithms. It is not (yet) geared towards end users. The ultimative goal is to provide high performance algorithms that can be used to differentiate dynamic systems (ODEs, DAEs, PDEs) and static systems (linear/nonlinear systems of equations).
ALGOPY focuses on the algebraic differentiation of elementary operations, e.g. C = dot(A,B) where A,B,C are matrices, y = sin(x), z = x*y, etc. to compute derivatives of functions composed of such elementary functions.
In particular, ALGOPY offers:
Univariate Taylor Propagation:
- Univariate Taylor Propagation on Scalars (UTPS) Implementation in: ./algopy/utp/utps.py
- Univariate Taylor Propagation on Matrices (UTPM) Implemenation in: ./algopy/utp/utpm.py
- Exact Interpolation of Higher Order Derivative Tensors: (Hessians, etc.)
Reverse Mode:
ALGOPY also features functionality for convenient differentiation of a given algorithm. For that, the sequence of operation is recorded by tracing the evaluation of the algorithm. Implementation in: ./algopy/tracer.py
ALGOPY aims to provide algorithms in a clean and accessible way allowing quick understanding of the underlying algorithms. Therefore, it should be easy to port to other programming languages, take code snippets. If optimized algorithms are wanted, they should be provided in a subclass derived from the reference implementation.
- numpy
- pyadolc
- scipy
- Nose
If you are looking for a robust tool for AD in Python you should try:
BSD style using https://www.opensource.org/licenses/bsd-license.php template as it was on 2009-01-24 with the following substutions:
- <YEAR> = 2008-2009
- <OWNER> = Sebastian F. Walter, sebastian.walter@gmail.com
- <ORGANIZATION> = contributors' organizations
- In addition, "Neither the name of the contributors' organizations" was changed to "Neither the names of the contributors' organizations"
Copyright (c) 2008-2009, Seastian F. Walter All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the names of the contributors' organizations nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
