On optimal low-rank approximation of non-negative matrices

Christian Grussler; Anders Rantzer

doi:10.1109/CDC.2015.7403045

On optimal low-rank approximation of non-negative matrices

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

For low-rank Frobenius-norm approximations of matrices with non-negative entries, it is shown that the Lagrange dual is computable by semi-definite programming. Under certain assumptions the duality gap is zero. Even when the duality gap is non-zero, several new insights are provided.

Original language	English
Title of host publication	54rd IEEE Conference on Decision and Control,CDC 2015
Pages	5278-5283
Number of pages	6
ISBN (Electronic)	9781479978861
DOIs	https://doi.org/10.1109/CDC.2015.7403045
State	Published - 8 Feb 2015
Externally published	Yes
Event	54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan Duration: 15 Dec 2015 → 18 Dec 2015

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	54rd IEEE Conference on Decision and Control,CDC 2015

Conference

Conference	54th IEEE Conference on Decision and Control, CDC 2015
Country/Territory	Japan
City	Osaka
Period	15/12/15 → 18/12/15

Keywords

Computational modeling
Conferences
Context
Convex functions
Image analysis
Programming
Standards

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2015.7403045

Cite this

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title = "On optimal low-rank approximation of non-negative matrices",

abstract = "For low-rank Frobenius-norm approximations of matrices with non-negative entries, it is shown that the Lagrange dual is computable by semi-definite programming. Under certain assumptions the duality gap is zero. Even when the duality gap is non-zero, several new insights are provided.",

keywords = "Computational modeling, Conferences, Context, Convex functions, Image analysis, Programming, Standards",

author = "Christian Grussler and Anders Rantzer",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 54th IEEE Conference on Decision and Control, CDC 2015 ; Conference date: 15-12-2015 Through 18-12-2015",

year = "2015",

month = feb,

day = "8",

doi = "10.1109/CDC.2015.7403045",

language = "الإنجليزيّة",

series = "Proceedings of the IEEE Conference on Decision and Control",

pages = "5278--5283",

booktitle = "54rd IEEE Conference on Decision and Control,CDC 2015",

}

Grussler, C & Rantzer, A 2015, On optimal low-rank approximation of non-negative matrices. in 54rd IEEE Conference on Decision and Control,CDC 2015., 7403045, Proceedings of the IEEE Conference on Decision and Control, vol. 54rd IEEE Conference on Decision and Control,CDC 2015, pp. 5278-5283, 54th IEEE Conference on Decision and Control, CDC 2015, Osaka, Japan, 15/12/15. https://doi.org/10.1109/CDC.2015.7403045

On optimal low-rank approximation of non-negative matrices. / Grussler, Christian; Rantzer, Anders.
54rd IEEE Conference on Decision and Control,CDC 2015. 2015. p. 5278-5283 7403045 (Proceedings of the IEEE Conference on Decision and Control; Vol. 54rd IEEE Conference on Decision and Control,CDC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rantzer, Anders

PY - 2015/2/8

Y1 - 2015/2/8

N2 - For low-rank Frobenius-norm approximations of matrices with non-negative entries, it is shown that the Lagrange dual is computable by semi-definite programming. Under certain assumptions the duality gap is zero. Even when the duality gap is non-zero, several new insights are provided.

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KW - Conferences

KW - Context

KW - Convex functions

KW - Image analysis

KW - Programming

KW - Standards

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DO - 10.1109/CDC.2015.7403045

M3 - منشور من مؤتمر

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EP - 5283

BT - 54rd IEEE Conference on Decision and Control,CDC 2015

T2 - 54th IEEE Conference on Decision and Control, CDC 2015

Y2 - 15 December 2015 through 18 December 2015

ER -

On optimal low-rank approximation of non-negative matrices

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

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