Library for Schatten-p norm minimization via iteratively reweighted least squares
Project description
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
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