DiffSharp 0.7.7

DiffSharp: Automatic Differentiation Library

DiffSharp is an automatic differentiation (AD) library.

AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow.

Using the DiffSharp library, derivative calculations (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) can be incorporated with minimal change into existing algorithms. Diffsharp supports nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations.

The library is under active development by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth.

DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.

Install-Package DiffSharp -Version 0.7.7
dotnet add package DiffSharp --version 0.7.7
paket add DiffSharp --version 0.7.7
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Release Notes

Please visit

https://github.com/DiffSharp/DiffSharp/releases

for the latest release notes.

Version History

Version Downloads Last updated
0.7.7 1,977 12/25/2015
0.7.6 319 12/15/2015
0.7.5 317 12/6/2015
0.7.4 371 10/13/2015
0.7.3 296 10/6/2015
0.7.2 299 10/4/2015
0.7.1 294 10/4/2015
0.7.0 302 9/29/2015
0.6.3 720 7/18/2015
0.6.2 321 6/6/2015
0.6.1 302 6/2/2015
0.6.0 291 4/26/2015
0.5.10 325 3/27/2015
0.5.9 298 2/26/2015
0.5.8 364 2/23/2015
0.5.7 287 2/17/2015
0.5.6 314 2/13/2015
0.5.5 321 12/15/2014
0.5.4 412 11/23/2014
0.5.3 970 11/7/2014
0.5.2 999 11/4/2014
0.5.1 302 10/27/2014
0.5.0 325 10/2/2014