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

Access to Document

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.
EPSRC
1/08/09 → 31/07/14
Project: Research

Cite this

@article{f77188e8f31b41ef8ebb74aa7f7907ba,

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.",

author = "Jakub Mare{\v c}ek and Peter Richt{\'a}rik and Martin Tak{\'a}{\v c}",

year = "2017",

month = jan,

doi = "10.1016/j.ejor.2016.07.014",

language = "English",

volume = "256",

pages = "35--43",

journal = "European Journal of Operational Research",

issn = "0377-2217",

publisher = "Elsevier B.V.",

number = "1",

}

TY - JOUR

T1 - Matrix Completion under Interval Uncertainty

AU - Mareček, Jakub

AU - Richtárik, Peter

AU - Takáč, Martin

PY - 2017/1

Y1 - 2017/1

N2 - 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.

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.

U2 - 10.1016/j.ejor.2016.07.014

DO - 10.1016/j.ejor.2016.07.014

M3 - Article

VL - 256

SP - 35

EP - 43

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 1

ER -

Matrix Completion under Interval Uncertainty

Abstract

Access to Document

Fingerprint

Projects

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

Cite this