Ordering strategies to reduce computational requirements in variance component estimation

Karin Meyer

Ordering strategies to reduce computational requirements in variance component estimation

Karin Meyer

Animal Genetics and Breeding Unit

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

Abstract

Computational requirements for sparse matrix factorisation or inversion are highly dependent on the 'fill-in' created. This can be reduced by judicious re-ordering of equations. It is shown that use of newer ordering strategies, with corresponding computer code available in the public domain, can reduce the time required for ordering and computational requirements of analyses dramatically.

Original language	English
Title of host publication	Proceedings of the Association for the Advancement of Animal Breeding and Genetics
Editors	AAABG: Association for the Advancement of Animal Breeding, Genetics
Place of Publication	Collingwood, Australia
Pages	282-285
Volume	16
Publication status	Published - 2005
Event	AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics - Noosa Lakes, Australia Duration: 25 Sept 2005 → 28 Sept 2005

Conference

Conference	AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics
City	Noosa Lakes, Australia
Period	25/09/05 → 28/09/05

Keywords

Animal Breeding

Access to Document

https://hdl.handle.net/1959.11/4506

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@inproceedings{ab3e053bcbeb455682b288a9496845cd,

title = "Ordering strategies to reduce computational requirements in variance component estimation",

abstract = "Computational requirements for sparse matrix factorisation or inversion are highly dependent on the 'fill-in' created. This can be reduced by judicious re-ordering of equations. It is shown that use of newer ordering strategies, with corresponding computer code available in the public domain, can reduce the time required for ordering and computational requirements of analyses dramatically.",

keywords = "Animal Breeding",

author = "Karin Meyer",

year = "2005",

language = "English",

isbn = "0643092331",

volume = "16",

pages = "282--285",

editor = "\{Association for the Advancement of Animal Breeding\}, AAABG: and Genetics",

booktitle = "Proceedings of the Association for the Advancement of Animal Breeding and Genetics",

note = "AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics ; Conference date: 25-09-2005 Through 28-09-2005",

}

Meyer, K 2005, Ordering strategies to reduce computational requirements in variance component estimation. in AAABG Association for the Advancement of Animal Breeding & Genetics (eds), Proceedings of the Association for the Advancement of Animal Breeding and Genetics. vol. 16, Collingwood, Australia, pp. 282-285, AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics, Noosa Lakes, Australia, 25/09/05. <https://hdl.handle.net/1959.11/4506>

Ordering strategies to reduce computational requirements in variance component estimation. / Meyer, Karin.
Proceedings of the Association for the Advancement of Animal Breeding and Genetics. ed. / AAABG: Association for the Advancement of Animal Breeding; Genetics. Vol. 16 Collingwood, Australia, 2005. p. 282-285.

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

TY - GEN

T1 - Ordering strategies to reduce computational requirements in variance component estimation

AU - Meyer, Karin

PY - 2005

Y1 - 2005

N2 - Computational requirements for sparse matrix factorisation or inversion are highly dependent on the 'fill-in' created. This can be reduced by judicious re-ordering of equations. It is shown that use of newer ordering strategies, with corresponding computer code available in the public domain, can reduce the time required for ordering and computational requirements of analyses dramatically.

AB - Computational requirements for sparse matrix factorisation or inversion are highly dependent on the 'fill-in' created. This can be reduced by judicious re-ordering of equations. It is shown that use of newer ordering strategies, with corresponding computer code available in the public domain, can reduce the time required for ordering and computational requirements of analyses dramatically.

KW - Animal Breeding

UR - http://www.aaabg.org/livestocklibrary/2005/282meyer.pdf

UR - http://trove.nla.gov.au/work/13321110

M3 - Conference contribution

SN - 0643092331

SN - 064309234X

VL - 16

SP - 282

EP - 285

BT - Proceedings of the Association for the Advancement of Animal Breeding and Genetics

A2 - Association for the Advancement of Animal Breeding, AAABG:

A2 - Genetics, null

CY - Collingwood, Australia

T2 - AAABG 2005: 16th Conference of the Association for the Advancement of Animal Breeding and Genetics

Y2 - 25 September 2005 through 28 September 2005

ER -

Ordering strategies to reduce computational requirements in variance component estimation

Abstract

Conference

Keywords

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