Metadata-Version: 2.1
Name: hmirls
Version: 0.1.0
Summary: Library for Schatten-p norm minimization via iteratively reweighted least squares
License: MIT
Author: Kristof Schroeder
Author-email: kristof_schroeder@web.de
Requires-Python: >=3.8,<3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: numpy (>=1.24.1,<2.0.0)
Requires-Dist: scipy (>=1.10.0,<2.0.0)
Description-Content-Type: text/markdown

# Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery

This repository contains python code to implement a basic variant of the Harmonic Mean Iteratively Reweighted Least Squares (HM-IRLS) algorithm for low-rank matrix recovery, in particular for the low-rank matrix completion problem, described in the paper:

> C. Kümmerle, J. Sigl.
> "Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery", Journal of Machine Learning Research (JMLR) volume 19, number 47, pages 1-49, 2018.
> Available online: https://jmlr.org/papers/volume19/17-244/17-244.pdf

## Version history
* Version 0.0.1, 10/25/2020

## Author
Kristof Schröder

## Documentation
> https://hmirls.readthedocs.io/en/latest/
