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

^*Corresponding author for this work

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

10 Scopus citations

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 (US)
Pages (from-to)	35-43
Number of pages	9
Journal	European Journal of Operational Research
Volume	256
Issue number	1
DOIs	https://doi.org/10.1016/j.ejor.2016.07.014
State	Published - Jan 1 2017
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2016 The Authors

Keywords

Collaborative filtering
Coordinate descent
Large-scale optimization
Matrix completion
Robust optimization

ASJC Scopus subject areas

General Computer Science
Modeling and Simulation
Management Science and Operations Research
Information Systems and Management

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10.1016/j.ejor.2016.07.014

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.",

keywords = "Collaborative filtering, Coordinate descent, Large-scale optimization, Matrix completion, Robust optimization",

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

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year = "2017",

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

language = "English (US)",

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AU - Mareček, Jakub

AU - Richtárik, Peter

AU - Takáč, Martin

PY - 2017/1/1

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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.

KW - Collaborative filtering

KW - Coordinate descent

KW - Large-scale optimization

KW - Matrix completion

KW - Robust optimization

UR - http://www.scopus.com/inward/record.url?scp=84990240104&partnerID=8YFLogxK

U2 - 10.1016/j.ejor.2016.07.014

DO - 10.1016/j.ejor.2016.07.014

M3 - Article

AN - SCOPUS:84990240104

SN - 0377-2217

VL - 256

SP - 35

EP - 43

JO - European Journal of Operational Research

JF - European Journal of Operational Research

IS - 1

ER -

Matrix completion under interval uncertainty

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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