A Python implementation of the TNT-NN method for Non-negative Least Squares problems.
Derived from Erich Frahm and Joseph Myre's Matlab implementation available at https://github.com/ProfMyre/tnt-nn.
Just copy and paste the subdirectory pytntnn (not the repository root!) to where the script from which it will be imported resides.
The main function can then be imported as from pytntnn import tntnn. See tntnn_x_sklearn_simple_comparison.py for an example.
This work is released under the GNU GPL v3.0 License.
If you use this software, please cite the papers by the original authors of the TNT-NN method:
TNT-NN: A Fast Active Set Method for Solving Large Non-Negative Least Squares Problems
@article{myre2017tnt,
title={TNT-NN: A Fast Active Set Method for Solving Large Non-Negative Least Squares Problems},
author={Myre, Joseph M and Frahm, E and Lilja, David J and Saar, Martin O},
journal={Procedia Computer Science},
volume={108},
pages={755--764},
year={2017},
publisher={Elsevier}
}TNT: A Solver for Large Dense Least-Squares Problems that Takes Conjugate Gradient from Bad in Theory, to Good in Practice
@inproceedings{myre2018tnt,
title={TNT: A Solver for Large Dense Least-Squares Problems that Takes Conjugate Gradient from Bad in Theory, to Good in Practice},
author={Myre, Joseph M and Frahm, Erich and Lilja, David J and Saar, Martin O},
booktitle={2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
pages={987--995},
year={2018},
organization={IEEE}
}