By Nicholas J. Higham
Accuracy and balance of Numerical Algorithms offers an intensive, updated remedy of the habit of numerical algorithms in finite precision mathematics. It combines algorithmic derivations, perturbation idea, and rounding mistakes research, all enlivened by way of historic standpoint and informative quotations.
This moment variation expands and updates the assurance of the 1st variation (1996) and comprises a number of advancements to the unique fabric. new chapters deal with symmetric indefinite platforms and skew-symmetric structures, and nonlinear structures and Newton's technique. Twelve new sections comprise insurance of extra mistakes bounds for Gaussian removal, rank revealing LU factorizations, weighted and limited least squares difficulties, and the fused multiply-add operation chanced on on a few sleek laptop architectures.
An elevated therapy of Gaussian removing contains rook pivoting, besides an intensive dialogue of the alternative of pivoting technique and the results of scaling. The book's particular descriptions of floating aspect mathematics and of software program concerns mirror the truth that IEEE mathematics is now ubiquitous.
Although now not designed in particular as a textbook, this re-creation is an acceptable reference for a sophisticated direction. it might even be utilized by teachers in any respect degrees as a supplementary textual content from which to attract examples, historic viewpoint, statements of effects, and workouts. With its thorough indexes and huge, up to date bibliography, the booklet presents a mine of knowledge in a with no trouble obtainable shape.
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Additional info for Accuracy and Stability of Numerical Algorithms
The absolute and relative errors of are called forward errors, to distinguish them from the backward error. 1 illustrates these concepts. The process of bounding the backward error of a computed solution is called backward error analysis, and its motivation is twofold. First, it interprets rounding errors as being equivalent to perturbations in the data. The data frequently contains uncertainties due to previous computations or errors committed in storing numbers on the computer. If the backward error is no larger than these uncertainties then the computed solution can hardly be criticized-it may be the solution we are seeking, for all we know.
4) where the sample mean Computing from this formula requires two passes through the data, one to compute and the other to accumulate the sum of squares. A two-pass computation is undesirable for large data sets or when the sample variance is to be computed as the data is generated. 10 SOLVING LINEAR EQUATIONS 13 This formula is very poor in the presence of rounding errors because it computes the sample variance as the difference of two positive numbers, and therefore can suffer severe cancellation that leaves the computed answer dominated by roundoff.
6 PRINCIPLES OF FINITE PRECISION COMPUTATION or, if the data is itself the solution to another problem, it may be the result of errors in an earlier computation. The effects of errors in the data are generally easier to understand than the effects of rounding errors committed during a computation, because data errors can be analysed using perturbation theory for the problem at hand, while intermediate rounding errors require an analysis specific to the given method. This book contains perturbation theory for most of the problems considered, for example, in Chapters 7 (linear systems), 19 (the least squares problem), and 20 (underdetermined systems).