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An efficient algorithm for sparse matrix computations

Authors:

S. C. Park,

J. P. Draayer,

S.-Q. ZhengAuthors Info & Claims

SAC '92: Proceedings of the 1992 ACM/SIGAPP symposium on Applied computing: technological challenges of the 1990's

Pages 919 - 926

https://doi.org/10.1145/130069.130108

Published: 01 March 1992 Publication History

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References

[1]

V. Strassen, "The Asymptotic Spectrum of Tensors and the Exponent of Matrix Multiplication," IEEE, p49, 1986.

Google Scholar

[2]

G.H. Golub and C.F. Van Loan, "Matrix Computations," Second Ed., The Johns Hopkins University Press, Baltimore, MD, 1989.

Google Scholar

[3]

J.K. Cullum and R.A. Willoughby, "Lanczos Algorithms for Large Symmetric Eigenvalue Computations," Vol. 1, Birkhauser Boston, Germany, 1985

Digital Library

Google Scholar

[4]

J.W. Neg, ele and H. Orland, "Quantum Many-Particle Systems, Addison-Wesley, Reading, MA, 1988.

Google Scholar

[5]

J. Neter, W. Wasserman and M.H. Kumer, "Applied Linear Regression Models," Second, Irwin, Homewood, iL, 1989.

Google Scholar

[6]

E. Horowitz and S. Sahni, "Fundamentals of Data Structures," Computer Science Press, Rockville, MD, 1983.

Digital Library

Google Scholar

[7]

M.J. Quinn, "Designing E~cient Algorithms for Parallel Computers," McGraw-Hill, NY, 1987.

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Cited By

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Belgin MBack GRibbens C(2010)Applicability of Pattern-based sparse matrix representation for real applicationsProcedia Computer Science10.1016/j.procs.2010.04.0231:1(203-211)Online publication date: May-2010
https://doi.org/10.1016/j.procs.2010.04.023
Belgin MBack GRibbens C(2010)A Library for Pattern-based Sparse Matrix Vector MultiplyInternational Journal of Parallel Programming10.1007/s10766-010-0145-239:1(62-87)Online publication date: 3-Jul-2010
https://doi.org/10.1007/s10766-010-0145-2
Belgin MBack GRibbens CGschwind MNicolau ASalapura VMoreira J(2009)Pattern-based sparse matrix representation for memory-efficient SMVM kernelsProceedings of the 23rd international conference on Supercomputing10.1145/1542275.1542294(100-109)Online publication date: 8-Jun-2009
https://dl.acm.org/doi/10.1145/1542275.1542294
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Index Terms

An efficient algorithm for sparse matrix computations
1. Computing methodologies
  1. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Linear algebra algorithms
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

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Symmetric Nonnegative Matrix Factorization (symNMF) is a special case of the standard Nonnegative Matrix Factorization (NMF) method which is the most popular linear dimensionality reduction technique for analyzing nonnegative data. ...
Fast Structured Matrix Computations: Tensor Rank and Cohn---Umans Method

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Published In

SAC '92: Proceedings of the 1992 ACM/SIGAPP symposium on Applied computing: technological challenges of the 1990's

March 1992

1296 pages

ISBN:089791502X

DOI:10.1145/130069

Editors:
Hal Berghel
Univ. of Arkansas, Fayetteville
,
George Hedrick
Oklahoma State Univ., Stillwater
,
Ed Deaton
San Diego State Univ., San Diego, CA
,
David Roach
Univ. of Arkansas, Fayetteville
,
Roger Wainwright
Univ. of Tulsa, Tulsa, OK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 1992

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SAC92

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SIGAPP

SAC92: 1992 ACM/SIGAPP Symposium on Applied Computing

Missouri, Kansas City, USA

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Belgin MBack GRibbens C(2010)Applicability of Pattern-based sparse matrix representation for real applicationsProcedia Computer Science10.1016/j.procs.2010.04.0231:1(203-211)Online publication date: May-2010
https://doi.org/10.1016/j.procs.2010.04.023
Belgin MBack GRibbens C(2010)A Library for Pattern-based Sparse Matrix Vector MultiplyInternational Journal of Parallel Programming10.1007/s10766-010-0145-239:1(62-87)Online publication date: 3-Jul-2010
https://doi.org/10.1007/s10766-010-0145-2
Belgin MBack GRibbens CGschwind MNicolau ASalapura VMoreira J(2009)Pattern-based sparse matrix representation for memory-efficient SMVM kernelsProceedings of the 23rd international conference on Supercomputing10.1145/1542275.1542294(100-109)Online publication date: 8-Jun-2009
https://dl.acm.org/doi/10.1145/1542275.1542294
Willcock JLumsdaine AEgan GMuraoka Y(2006)Accelerating sparse matrix computations via data compressionProceedings of the 20th annual international conference on Supercomputing10.1145/1183401.1183444(307-316)Online publication date: 28-Jun-2006
https://dl.acm.org/doi/10.1145/1183401.1183444

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Efficient algorithm for sparse symmetric nonnegative matrix factorization

Fast Structured Matrix Computations: Tensor Rank and Cohn---Umans Method

Predicting Structure in Sparse Matrix Computations