# SOPT: Sparse OPTimisation
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.
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:
- Rafael E. Carrillo
- Jason D. McEwen
- Yves Wiaux
- Vijay Kartik
- Mayeul d'Avezac
- Luke Pratley
- David Perez-Suarez
## 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
### 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
If you have any questions or comments, feel free to contact Rafael Carrillo or Jason McEwen, or add an issue in the issue tracker.
The code is given for educational purpose. For the matlab version of the code see the folder matlab.
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.