Description:

DiffSharp: Differentiable Functional Programming

DiffSharp is a functional 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, differentiation (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) is applied using higher-order functions, that is, functions which take other functions as arguments. Your functions can use the full expressive capability of the language including control flow. DiffSharp allows composition of differentiation using 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 developed 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, .NET Core, 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.

Changes to previous version:

Fixed: Bug fix in LAPACK wrappers ssysv and dsysv in the OpenBLAS backend that caused incorrect solution for linear systems described by a symmetric matrix (fixes #11, thank you @grek142)

Added: Added unit tests covering the whole backend interface

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Windows

Data Formats: Agnostic

Tags: Optimization, Automatic Differentiation, Symbolic Differentiation, Backpropagation, Numerical Differentiation

Archive: download here

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