Scientific computing requires handling large composed or structured matrices. Fastmat is a framework for handling large composed or structured matrices. It allows expressing and using them in a mathematically intuitive way while storing and handling them internally in an efficient way. This approach allows huge savings in computational time and memory requirements compared to using dense matrix representations. Follow the development on the github project page. The documentation can be found at readthedocs.
- Python >= 2.7 or >=3.4
- Numpy >= 1.7
- Scipy >= 1.0
- Cython >= 0.29
- soft dependencies:
- matplotlib: for demos and tools that make use of plotting functions
- Sebastian Semper | sebastian.semper@tu-ilmenau.de Technische Universität Ilmenau, Institute for Mathematics, EMS Group
- Christoph Wagner | christoph.wagner@tu-ilmenau.de Technische Universität Ilmenau, Institute for Information Technology, EMS Group
- https://www.tu-ilmenau.de/it-ems/
If you use fastmat, or parts of it, for commercial purposes you are required to acknowledge the use of fastmat visibly to all users of your work and put a reference to the project and the EMS Group at TU Ilmenau.
If you use fastmat for your scientific work you are required to mention the EMS Group at TU Ilmenau and cite the following publication affiliated with the project:
C. Wagner and S. Semper, Fast Linear Transforms in Python, arXiV:1710.09578, 2017