Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.
This Julia package provides fast operations with symmetric and non-symmetric tensors of order 1, 2 and 4. The Tensors are allocated on the stack which means that there is no need to preallocate output results for performance. Unicode infix operators are provided such that the tensor expression in the source code is similar to the one written with mathematical notation. When possible, symmetry of tensors is exploited for better performance. Supports Automatic Differentiation to easily compute first and second order derivatives of tensorial functions.
The package is registered in
METADATA.jl and so can be installed with
The package is tested against Julia
0.6-dev on Linux, OS X, and Windows.
Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems.
If you are interested in contributing to Tensors.jl, here are a few topics that can get you started:
Both the packages below provide a convenience macro to provide einstein summation notation for standard Julia
about 1 month ago