Matrix Completion under Interval Uncertainty

Jakub Mareček; Peter Richtárik; Martin Takáč

doi:10.1016/j.ejor.2016.07.014

Matrix Completion under Interval Uncertainty

Jakub Mareček, Peter Richtárik, Martin Takáč

School of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

Matrix completion under interval uncertainty can be cast as a matrix completion problem with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes on a single personal computer.

Original language	English
Pages (from-to)	35-43
Number of pages	28
Journal	European Journal of Operational Research
Volume	256
Issue number	1
Early online date	14 Jul 2016
DOIs	https://doi.org/10.1016/j.ejor.2016.07.014
Publication status	Published - Jan 2017

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10.1016/j.ejor.2016.07.014

1-s2.0-S0377221716305513-mainAccepted author manuscript, 2.99 MB

http://linkinghub.elsevier.com/retrieve/pii/S0377221716305513

Science and Innovation: Numerical Algorithms and Intelligent Software for the Evolving HPC Platform
Leimkuhler, B. (Principal Investigator)
EPSRC
1/08/09 → 31/07/14
Project: Research

Cite this

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title = "Matrix Completion under Interval Uncertainty",

abstract = "Matrix completion under interval uncertainty can be cast as a matrix completion problem with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes on a single personal computer.",

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AB - Matrix completion under interval uncertainty can be cast as a matrix completion problem with element-wise box constraints. We present an efficient alternating-direction parallel coordinate-descent method for the problem. We show that the method outperforms any other known method on a benchmark in image in-painting in terms of signal-to-noise ratio, and that it provides high-quality solutions for an instance of collaborative filtering with 100,198,805 recommendations within 5 minutes on a single personal computer.

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Matrix Completion under Interval Uncertainty

Abstract

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Projects

Science and Innovation: Numerical Algorithms and Intelligent Software for the Evolving HPC Platform

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