Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion
Machine Learning
2020-08-13 v1 Numerical Analysis
Numerical Analysis
Machine Learning
Abstract
We study alternating minimization for matrix completion in the simplest possible setting: completing a rank-one matrix from a revealed subset of the entries. We bound the asymptotic convergence rate by the variational characterization of eigenvalues of a reversible consensus problem. This leads to a polynomial upper bound on the asymptotic rate in terms of number of nodes as well as the largest degree of the graph of revealed entries.
Keywords
Cite
@article{arxiv.2008.04988,
title = {Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion},
author = {Rui Liu and Alex Olshevsky},
journal= {arXiv preprint arXiv:2008.04988},
year = {2020}
}
Comments
6 pages, 4 figures