Comments on Efficient Singular Value Thresholding Computation
Optimization and Control
2020-11-16 v1
Abstract
We discuss how to evaluate the proximal operator of a convex and increasing function of a nuclear norm, which forms the key computational step in several first-order optimization algorithms such as (accelerated) proximal gradient descent and ADMM. Various special cases of the problem arise in low-rank matrix completion, dropout training in deep learning and high-order low-rank tensor recovery, although they have all been solved on a case-by-case basis. We provide an unified and efficiently computable procedure for solving this problem.
Cite
@article{arxiv.2011.06710,
title = {Comments on Efficient Singular Value Thresholding Computation},
author = {Zhengyuan Zhou and Yi Ma},
journal= {arXiv preprint arXiv:2011.06710},
year = {2020}
}