

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:
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@@ -37,4 +41,3 @@ about:
- learn dictionaries with an intrinsically efficient implementation,
- compute (approximate) fast Fourier transforms on graphs.
long_description="""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,
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:
- learn dictionaries with an intrinsically efficient implementation,
- speedup / reduce the memory footprint of iterative algorithms commonly used for solving high dimensional linear inverse problems,
- compute (approximate) fast Fourier transforms on graphs.
- learn dictionaries with an intrinsically efficient implementation,
- compute (approximate) fast Fourier transforms on graphs.
""",
long_description_content_type='text/markdown',
classifiers=['License :: OSI Approved :: BSD License',