The probability that a numerical analysis problem is difficult

Author:
James W. Demmel

Journal:
Math. Comp. **50** (1988), 449-480

MSC:
Primary 65G99; Secondary 15A12, 60D05

MathSciNet review:
929546

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Abstract | References | Similar Articles | Additional Information

Abstract: Numerous problems in numerical analysis, including matrix inversion, eigenvalue calculations and polynomial zerofinding, share the following property: The difficulty of solving a given problem is large when the distance from that problem to the nearest "ill-posed" one is small. For example, the closer a matrix is to the set of non-invertible matrices, the larger its condition number with respect to inversion. We show that the sets of ill-posed problems for matrix inversion, eigenproblems, and polynomial zerofinding all have a common algebraic and geometric structure which lets us compute the probability distribution of the distance from a "random" problem to the set. From this probability distribution we derive, for example, the distribution of the condition number of a random matrix. We examine the relevance of this theory to the analysis and construction of numerical algorithms destined to be run in finite precision arithmetic.

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DOI:
https://doi.org/10.1090/S0025-5718-1988-0929546-7

Article copyright:
© Copyright 1988
American Mathematical Society