@article {17735,
	title = {The decompositional approach to matrix computation},
	journal = {Computing in Science Engineering},
	volume = {2},
	year = {2000},
	month = {2000/02//jan},
	pages = {50 - 59},
	abstract = {The introduction of matrix decomposition into numerical linear algebra revolutionized matrix computations. The article outlines the decompositional approach, comments on its history, and surveys the six most widely used decompositions: Cholesky decomposition; pivoted LU decomposition; QR decomposition; spectral decomposition; Schur decomposition; and singular value decomposition},
	keywords = {Algebra, approach;history;matrix, Cholesky, computation;matrix, computations;pivoted, computing;matrix, decomposition;, decomposition;bibliographies;history;mathematics, decomposition;decompositional, decomposition;numerical, decomposition;QR, decomposition;Schur, decomposition;singular, decomposition;spectral, linear, LU, matrix, revolutionized, value},
	isbn = {1521-9615},
	doi = {10.1109/5992.814658},
	author = {Stewart, G.W.}
}
@article {17727,
	title = {On the stability of sequential updates and downdates},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {43},
	year = {1995},
	month = {1995/11//},
	pages = {2642 - 2648},
	abstract = {The updating and downdating of Cholesky decompositions has important applications in a number of areas. There is essentially one standard updating algorithm, based on plane rotations, which is backward stable. Three downdating algorithms have been treated in the literature: the LINPACK algorithm, the method of hyperbolic transformations, and Chambers{\textquoteright} (1971) algorithm. Although none of these algorithms is backward stable, the first and third satisfy a relational stability condition. It is shown that relational stability extends to a sequence of updates and downdates. In consequence, other things being equal, if the final decomposition in the sequence is well conditioned, it will be accurately computed, even though intermediate decompositions may be almost completely inaccurate. These results are also applied to the two-sided orthogonal decompositions, such as the URV decomposition},
	keywords = {algorithm;Cholesky, algorithm;downdating, algorithm;error, algorithm;URV, algorithms;hyperbolic, analysis;matrix, analysis;sequential, Chambers{\textquoteright}, condition;rounding, decomposition;backward, decomposition;numerical, decomposition;plane, decompositions;LINPACK, decompositions;updating, downdates;sequential, error, errors;sequences;, orthogonal, rotations;relational, stability, stability;roundoff, stable, transformations;matrix, updates;stability;two-sided},
	isbn = {1053-587X},
	doi = {10.1109/78.482114},
	author = {Stewart, G.W.}
}
