Smoothed analysis of the condition numbers and growth factors of matrices
A Sankar, DA Spielman, SH Teng - SIAM Journal on Matrix Analysis and …, 2006 - SIAM
A Sankar, DA Spielman, SH Teng
SIAM Journal on Matrix Analysis and Applications, 2006•SIAMLet Å be an arbitrary matrix and let A be a slight random perturbation of Å. We prove that it is
unlikely that A has a large condition number. Using this result, we prove that it is unlikely that
A has large growth factor under Gaussian elimination without pivoting. By combining these
results, we show that the smoothed precision necessary to solve A x= b, for any b, using
Gaussian elimination without pivoting is logarithmic. Moreover, when Å is an all-zero square
matrix, our results significantly improve the average-case analysis of Gaussian elimination …
unlikely that A has a large condition number. Using this result, we prove that it is unlikely that
A has large growth factor under Gaussian elimination without pivoting. By combining these
results, we show that the smoothed precision necessary to solve A x= b, for any b, using
Gaussian elimination without pivoting is logarithmic. Moreover, when Å is an all-zero square
matrix, our results significantly improve the average-case analysis of Gaussian elimination …
Let Å be an arbitrary matrix and let A be a slight random perturbation of Å. We prove that it is unlikely that A has a large condition number. Using this result, we prove that it is unlikely that A has large growth factor under Gaussian elimination without pivoting. By combining these results, we show that the smoothed precision necessary to solve Ax = b, for any b, using Gaussian elimination without pivoting is logarithmic. Moreover, when Å is an all-zero square matrix, our results significantly improve the average-case analysis of Gaussian elimination without pivoting performed by Yeung and Chan (SIAM J. Matrix Anal. Appl., 18 (1997), pp. 499-517).
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