cr-sparse 0.2.1

Accelerated sparse representations and compressive sensing

Functional Models and Algorithms for Sparse Signal Processing


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Quick Start
=========================

An `overview <https://cr-sparse.readthedocs.io/en/latest/intro.html>`_ of the library.

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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 <https://github.com/carnotresearch/cr-sparse/blob/master/paper/paper.md#runtime-comparisons>`_.
Jupyter notebooks for these benchmarks are in the `companion repository <https://github.com/carnotresearch/cr-sparse-companion>`_.


See the `examples gallery <https://cr-sparse.readthedocs.io/en/latest/gallery/index.html>`_ for an
extensive set of examples. Here is a small selection of examples:

* `Sparse recovery using Truncated Newton Interior Points Method <https://cr-sparse.readthedocs.io/en/latest/gallery/rec_l1/spikes_l1ls.html>`_
* `Sparse recovery with ADMM <https://cr-sparse.readthedocs.io/en/latest/gallery/rec_l1/partial_wh_sensor_cosine_basis.html>`_
* `Compressive sensing operators <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/cs_operators.html>`_
* `Image deblurring with LSQR and FISTA algorithms <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/deblurring.html>`_
* `Deconvolution of the effects of a Ricker wavelet <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/deconvolution.html>`_
* `Wavelet transform operators <https://cr-sparse.readthedocs.io/en/latest/gallery/lop/wt_op.html>`_
* `CoSaMP step by step <https://cr-sparse.readthedocs.io/en/latest/gallery/pursuit/cosamp_step_by_step.html>`_


A more extensive collection of example notebooks is available in the `companion repository <https://github.com/carnotresearch/cr-sparse-companion>`_.


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:

.. code:: shell

python -m pip install cr-sparse

Directly from our GITHUB repository:

.. code:: shell

python -m pip install git+https://github.com/carnotresearch/cr-sparse.git


Citing cr-sparse
------------------------


To cite this repository:

.. code:: tex

@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},
}




`Documentation <https://carnotresearch.github.io/cr-sparse>`_ |
`Code <https://github.com/carnotresearch/cr-sparse>`_ |
`Issues <https://github.com/carnotresearch/cr-sparse/issues>`_ |
`Discussions <https://github.com/carnotresearch/cr-sparse/discussions>`_ |
`Examples <https://github.com/carnotresearch/cr-sparse/blob/master/notebooks/README.rst>`_ |
`Experiments <https://github.com/carnotresearch/cr-sparse/blob/master/notebooks/experiments/README.rst>`_ |
`Sparse-Plex <https://sparse-plex.readthedocs.io>`_


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