Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method

Christian Kümmerle; Claudio M. Verdun

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arXiv · 2009.02905

Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method

Christian Kümmerle·Claudio M. Verdun

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Abstract

We propose an iterative algorithm for low-rank matrix completion that can be interpreted as both an iteratively reweighted least squares (IRLS) algorithm and a saddle-escaping smoothing Newton method applied to a non-convex rank surrogate objective. It combines the favorable data efficiency of previous IRLS approaches with an improved scalability by several orders of magnitude. Our method attains a local quadratic convergence rate already for a number of samples that is close to the information theoretical limit. We show in numerical experiments that unlike many state-of-the-art approaches, our approach is able to complete very ill-conditioned matrices with a condition number of up to $10^{10}$ from few samples.

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BibTeX RIS

Christian Kümmerle, Claudio M. Verdun. 2020-09-07. Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method. https://arxiv.org/abs/2009.02905

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