Entrywise Low-Rank Approximation and Matrix $p \rightarrow q$ Norms via Global Correlation Rounding
Prashanti Anderson, Ainesh Bakshi, Samuel B. Hopkins

TL;DR
This paper introduces polynomial-time approximation schemes for entrywise low-rank matrix approximation under p-norms for p > 2, and for matrix p→q norms, using convex hierarchies, advancing beyond prior methods.
Contribution
It develops the first polynomial-time approximation schemes for p-norm low-rank approximation when p > 2, and introduces new additive approximation algorithms for matrix p→q norms.
Findings
First polynomial-time approximation scheme for p > 2
New additive approximation algorithms for matrix p→q norms
Blueprint for using convex hierarchies in continuous optimization
Abstract
Given a matrix , the goal of the entrywise low-rank approximation problem is to find over all rank- matrices , where is the entrywise norm. When this well-studied problem is solved by the singular value decomposition, but for the problem becomes computationally challenging. For every even and every fixed , we give the first polynomial-time approximation scheme for this problem, improving on the approximation of Ban, Bhattiprolu, Bringmann, Kolev, Lee, and Woodruff, the bi-criteria approximation of Woodruff and Yasuda, and the additive approximation scheme of Anderson, Bakshi, and Hopkins. Prior algorithmic approaches based on sketching and column selection, which yielded a polynomial-time approximation scheme in the setting, face concrete barriers when .…
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