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Updating an LU Factorization with Pivoting
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ACM Transactions on Mathematical Software (TOMS) archive
Volume 35 ,  Issue 2  (July 2008) table of contents
Article No. 11  
Year of Publication: 2008
ISSN:0098-3500
Authors
Enrique S. Quintana-Ortí  Universidad Jaime I
Robert A. Van De Geijn  The University of Texas at Austin
Publisher
ACM  New York, NY, USA
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ABSTRACT

We show how to compute an LU factorization of a matrix when the factors of a leading principle submatrix are already known. The approach incorporates pivoting akin to partial pivoting, a strategy we call incremental pivoting. An implementation using the Formal Linear Algebra Methods Environment (FLAME) application programming interface (API) is described. Experimental results demonstrate practical numerical stability and high performance on an Intel Itanium2 processor-based server.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Bientinesi, P. and van de Geijn, R. 2006. Representing dense linear algebra algorithms: A farewell to indices. Tech. Rep. FLAME Working Note 17, CS-TR-2006-10, Department of Computer Sciences, The University of Texas at Austin.
 
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Cwik, T., van de Geijn, R., and Patterson, J. 1994. The application of parallel computation to integral equation models of electromagnetic scattering. J. Optic. Soc. Amer. A 11, 4 (Apr.), 1538--1545.
 
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Demmel, J. and Dongarra, J. 2005. LAPACK 2005 prospectus: Reliable and scalable software for linear algebra computations on high end computers. LAPACK Working Note 164 UT-CS-05-546, University of Tennessee. February.
 
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Geng, P., Oden, J. T., and van de Geijn, R. 1996. Massively parallel computation for acoustical scattering problems using boundary element methods. J. Sound Vibra. 191, 1, 145--165.
 
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Goto, K. 2004. TACC software and tools. http://www.tacc.utexas.edu/resources/software/.
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Joffrain, T., Quintana-Ortí, E. S., and van de Geijn, R. A. 2005. Rapid development of high-performance out-of-core solvers. In Proceedings of the Workshop on Applied Parallel Computing (PARA 2004), J. Dongarra et al., eds. Lecture Notes in Computer Science, vol. 3732. Springer, 413--422.
 
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Kågström, B., Ling, P., and Loan, C. V. 1995. Gemm-Based level 3 blas: High-Performance model, implementations and performance evaluation benchmark. LAPACK Working Note no. 107 CS-95-315, University of Tennessee. November.
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Klimkowski, K. and van de Geijn, R. 1995. Anatomy of an out-of-core dense linear solver. In Proceedings of the International Conference on Parallel Processing. vol. III - Algorithms and Applications, 29--33.
 
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Stewart, G. W. 1998. Matrix Algorithms. Volume I: Basic Decompositions. SIAM, Philadelphia, PA.
 
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Yip, E. L. 1979. Fortran subroutines for out-of-core solutions of large complex linear systems. Tech. Rep. CR-159142, NASA.


Collaborative Colleagues:
Enrique S. Quintana-Ortí: colleagues
Robert A. Van De Geijn: colleagues