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:
- speed up / 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.
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