Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery MATLAB Code
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IRLS_MatrixCompletion
LMaFit
RiemannianMatrixCompletion_30Jun2014
LICENSE
README.md
get_frob_errors.m
run_MC_algos.m
sample_X0_lowrank.m
sample_phi_MatrixCompletion.m
script_HM_IRLS_Figure3.m
script_HM_IRLS_Figure4.m
script_HM_IRLS_Figure5.m
script_mc_comparisons.m
script_small_example_IRLSvariants.m
visualize_errorcurves_IRLScompare.m
visualize_errorcurves_combined.m

README.md

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

This repository contains MATLAB 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, and to reproduce the experiments described in the paper:

C. Kümmerle, J. Sigl. "Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery", to appear in the Journal of Machine Learning Research (JMLR). Available online: https://arxiv.org/abs/1703.05038

The main file is HM_IRLS.m. See also the example scripts:

  • script_mc_comparisons.m - Comparison script between HM-IRLS and two other algorithms on random matrix completion data
  • script_small_example_IRLSvariants.m - Script illustrating the small example of Section 3 of the paper, comparing HM_IRLS with other IRLS variants for the problem
  • script_HM_IRLS_Figure3.m - Script reproducing experiment of Figure 3 of the paper (convergence rates of HM-IRLS and other IRLS variants for easy problems)
  • script_HM_IRLS_Figure4.m - Script reproducing experiment of Figure 4 of the paper (convergence rates of HM-IRLS and other IRLS variants for hard problems)
  • script_HM_IRLS_Figure5.m - Script reproducing experiment of Figure 5 of the paper (convergence rates of HM-IRLS and other IRLS variants for very hard problems)

Version history

  • Version 1.1, updated 10/01/2018
  • Version 1.0, 3/14/2017

Author

Christian Kümmerle (website)

Acknowledgments

For the purpose of comparison with other popular algorithmic approaches, we included code by

  • Bart Vandereycken ("LRGeomCG"), corresponding to the paper "Low-Rank Matrix Completion by Riemannian Optimization", SIAM J. Optim., 23(2), 1214–1236.
  • Zaiwen Wen, Wotao Yin and Yin Zhang ("LMaFit"), corresponding to the paper "Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm", Math. Prog. Comp. (2012) 4: 333.