SOPT: Sparse OPTimisation

Description

SOPT is a C++ package to perform Sparse OPTimisation. It solves a variety of sparse regularisation problems, including the SARA algorithm. Prototype Matlab implementations of various algorithms are also included.

Contributors

SOPT was initially created by Rafael Carrillo, Jason McEwen and Yves Wiaux but major contributions have since been made by a number of others. The full list of contributors is as follows:

References

When referencing this code, please cite our related papers:

R. E. Carrillo, J. D. McEwen and Y. Wiaux. "Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging", Mon. Not. Roy. Astron. Soc., 426(2):1223-1234, 2012, arXiv:1205.3123
R. E. Carrillo, J. D. McEwen, D. Van De Ville, J.-P. Thiran, and Y. Wiaux. "Sparsity averaging for compressive imaging", IEEE Signal Processing Letters, 20(6):591-594, 2013, arXiv:1208.2330
A. Onose, R. E. Carrillo, A. Repetti, J. D. McEwen, J.-P. Thiran, J.-C. Pesquet, and Y. Wiaux. "Scalable splitting algorithms for big-data interferometric imaging in the SKA era". Mon. Not. Roy. Astron. Soc., 462(4):4314-4335, 2016, arXiv:1601.04026

Webpage

http://basp-group.github.io/sopt/

Installation

C++ pre-requisites and dependencies

CMake: a free software that allows cross-platform compilation
tiff: Tag Image File Format library
OpenMP: Optional. Speeds up some of the operations.
UCL/GreatCMakeCookOff: Collection of cmake recipes. Downloaded automatically if absent.
Eigen 3: Modern C++ linear algebra. Downloaded automatically if absent.
spdlog: Optional. Logging library. Downloaded automatically if absent.
philsquared/Catch: Optional - only for testing. A C++ unit-testing framework. Downloaded automatically if absent.
google/benchmark: Optional - only for benchmarks. A C++ micro-benchmarking framework. Downloaded automatically if absent.

Python pre-requisites and dependencies

numpy: Fundamental package for scientific computing with Python
scipy: User-friendly and efficient numerical routines such as routines for numerical integration and optimization
pandas: library providing high-performance, easy-to-use data structures and data analysis tools
cython: Makes writing C extensions for Python as easy as Python itself. Downloaded automatically if absent.
pytest: Optional - for testing only. Unit-testing framework for python. Downloaded automatically if absent and testing is not disabled.

Installing Sopt

Once the dependencies are present, the program can be built with:

cd /path/to/code
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make

To test everything went all right:

cd /path/to/code/build
ctest .

To install in directory /X, with libraries going to X/lib, python modules to X/lib/pythonA.B/site-packages/sopt, etc, do:

cd /path/to/code/build
cmake -DCMAKE_INSTALL_PREFIX=/X ..
make install

Support

If you have any questions or comments, feel free to contact Rafael Carrillo or Jason McEwen, or add an issue in the issue tracker.

Notes

The code is given for educational purpose. For the matlab version of the code see the folder matlab.

License

SOPT: Sparse OPTimisation package
Copyright (C) 2013 Rafael Carrillo, Jason McEwen, Yves Wiaux

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
General Public License for more details (LICENSE.txt).

You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
02110-1301, USA.

basp-group / sopt

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