Search Results for "automatic differentiation"
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Showing 13 open source projects for "automatic differentiation"
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JAX
Composable transformations of Python+NumPy programs
...But JAX also lets you just-in-time compile your own Python functions into XLA-optimized kernels using a one-function API, jit. Compilation and automatic differentiation can be composed arbitrarily, so you can express sophisticated algorithms and get maximal performance without leaving Python. -
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REDUCE
A Portable General-Purpose Computer Algebra System
REDUCE is an interactive system for general algebraic computations of interest to mathematicians, scientists and engineers. It can be used interactively for simple calculations but also provides a flexible and expressive user programming language. The development of the REDUCE computer algebra system was started in the 1960s by Anthony C. Hearn. Since then, many scientists from all over the world have contributed to its development. REDUCE has a long and distinguished place in the... -
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Kotlingrad
Shape-Safe Symbolic Differentiation with Algebraic Data Types
Kotlin∇ is a type-safe automatic differentiation framework written in Kotlin. It allows users to express differentiable programs with higher-dimensional data structures and operators. We attempt to restrict syntactically valid constructions to those which are algebraically valid and can be checked at compile-time. By enforcing these constraints in the type system, it eliminates certain classes of runtime errors that may occur during the execution of a differentiable program. ... -
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AdaAutoDiff
C++ Templates and Ada Package for Automatic Differentiation
Operators are overloaded so that a normal looking function definition provides access to not only evaluations of itself, but to evaluations of any of its analytic derivatives. Automatic differentiation means the user does not need to define the analytic expressions for all the various partial derivatives. It also means that those complex expressions are essentially calculated at compile time, and merely evaluated at runtime. First order derivatives only, forward accumulation. Choose "files" and either the "initial_submission" directory for the Ada version, or the other directory "cpp_AD" for the C++ version. -
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A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.
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ADiGator
A MATLAB Automatic Differentiation Tool
ADiGator is a source transformation via operator overloading tool for the automatic differentiation of mathematical functions written in MATLAB. Given a user written file, together with information on the inputs of said file, ADiGator uses forward mode automatic differentiation to generate a new file which contains the calculations required to compute the numeric derivatives of the original user function. Furthermore, these calculations are written entirely in the native MATLAB language, and thus the process may be repeated to obtain nth order derivative files. ... -
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autodiff
Python class for automatic differentiation
Python automatic differentiation class for forward mode automatic differentiation using operator overloading and reimplemented math functions. Single and partial derivatives are supported. Supported operators: +, -, *, /, **, +=, -=, *=, /=, **= Available functions: sin, cos, tan, asin, acos, atan, sqrt, exp, log, log10, sinh, cosh, tanh, asinh, acosh, atanh See README file for usage examples. -
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GADfit
Global nonlinear optimization with automatic differentiation
...Global fitting refers to fitting many datasets simultaneously with some parameters shared among the datasets. The fitting procedure is very fast and accurate thanks to the use of automatic differentiation. The model curves (fitting functions) can be of essentially arbitrary complexity. This includes any nonlinear combination of elementary and special functions, single and/or double integrals, and any control flow statement allowed by the programming language. See the latest user guide under Files. -
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TIDES
Taylor series Integrator for Differential Equations
Taylor series Integrator for Differential EquationS. This software is developed by Profs. A. Abad, R. Barrio, F. Blesa and M. Rodriguez, (GME, University of Zaragoza, Spain). It consists on a C (Fortran) library, libTIDES, and a Mathematica package, MathTIDES. (MathTIDES requires Mathematica version >= 7.0) . Basic references: * A. Abad, R. Barrio, F. Blesa, M. Rodriguez, 2012. Algorithm 924: TIDES, a Taylor series Integrator for Differential EquationS, ACM TOMS. 39, no. 1, art. 5.... -
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ANGEL-Automatic differentiation Nested Graph Elimination Library is a template library using the Boost Graph Library and the Standard C++ Library; it provides sparse representations of c-graphs their dual line graphs and vertex, edge and face elimina
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GPDC
Gravity Potential Derivatives Calculator
GPDC is a C file to compute partial derivatives of a gravity potential up to any order by means of an automatic differentiation method. -
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A 2-D inviscid flow and adjoint solver on unstructured triangular grids. It makes use of a vertex-centroid finite volume scheme which is second order accurate. The adjoint solver is developed using the automatic differentiation tool called TAPENADE. Code has been moved to https://github.com/cpraveen/euler2d
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ADMC++ -- An Automatic Differentiation Package for MATLAB and C++ ADMC++ is an automatic differentiation package designed for MATLAB and C++. Automatic differentiation is a technique for computing derivatives of functions.
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