The FAµST toolbox provides algorithms and data structures to decompose a given dense matrix into a product of sparse matrices in order to reduce its computational complexity (both for storage and manipulation). FaµST can be used to
- speedup / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems.
- learn dictionaries with an intrinsically efficient implementation
- compute (approximate) fast Fourier transforms on graphs.
A general introduction to FAµST is available in the following paper:
 Le Magoarou L. and Gribonval R., “Flexible multi-layer sparse approximations of matrices and applications”, Journal of Selected Topics in Signal Processing, 2016.
The FAµST toolbox was initially released as a Matlab implementation (versions 1.x). A C++ implementation (versions 2.x), including Matlab wrappers, is now available and the object of further developments. A Python interface is being developed.