Functional Models and Algorithms for Sparse Signal Processing
Quick Start¶
An overview of the library.
This library aims to provide XLA/JAX based Python implementations for various models and algorithms related to:
Wavelet transforms
Efficient linear operators
Iterative methods for sparse linear systems
Redundant dictionaries
Sparse approximations on redundant dictionaries
Greedy methods
Convex optimization based methods
Shrinkage methods
Sparse recovery from compressive sensing based measurements
Greedy methods
Convex optimization based methods
The library also provides
Various simple dictionaries and sensing matrices
Sample data generation utilities
Framework for evaluation of sparse recovery algorithms
Examples¶
Some micro-benchmarks are reported here. Jupyter notebooks for these benchmarks are in the companion repository.
See the examples gallery for an extensive set of examples. Here is a small selection of examples:
A more extensive collection of example notebooks is available in the companion repository.
Platform Support¶
cr-sparse can run on any platform supported by JAX.
We have tested cr-sparse on Mac and Linux platforms and Google Colaboratory.
JAX is not officially supported on Windows platforms at the moment.
Although, it is possible to build it from source using Windows Subsystems for Linux.
Installation¶
Installation from PyPI:
python -m pip install cr-sparse
Directly from our GITHUB repository:
python -m pip install git+https://github.com/carnotresearch/cr-sparse.git
Citing cr-sparse¶
To cite this repository:
@software{crsparse2021github,
author = {Shailesh Kumar},
title = {{cr-sparse}: Functional Models and Algorithms for Sparse Signal Processing},
url = {https://cr-sparse.readthedocs.io/en/latest/},
version = {0.1.6},
year = {2021},
doi={10.5281/zenodo.5322044},
}
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