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Gérard Meurant Error Norm Estimation in the Conjugate G (Paperback) (UK IMPORT)

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
Book Title
Error Norm Estimation in the Conjugate Gradient Algorithm
Title
Error Norm Estimation in the Conjugate Gradient Algorithm
EAN
9781611977851
ISBN
9781611977851
Genre
Science Nature & Math
Topic
Technology & Engineering
Release Year
2024
Release Date
02/29/2024
Country/Region of Manufacture
US
Category

About this product

Product Identifiers

Publisher
Society for Industrial AND Applied Mathematics
ISBN-10
1611977851
ISBN-13
9781611977851
eBay Product ID (ePID)
18064414609

Product Key Features

Number of Pages
127 Pages
Publication Name
Error Norm Estimation in the Conjugate Gradient Algorithm
Language
English
Publication Year
2024
Subject
General
Type
Textbook
Subject Area
Mathematics
Author
Petr Tichý, Gérard Meurant
Series
Siam Spotlights Ser.
Format
Trade Paperback

Dimensions

Item Height
0.4 in
Item Weight
11.3 Oz
Item Length
10 in
Item Width
6.7 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2023-048404
Dewey Edition
23/eng/20231120
Series Volume Number
6
Dewey Decimal
511/.8
Synopsis
The conjugate gradient (CG) algorithm is almost always the iterative method of choice for solving linear systems with symmetric positive definite matrices. This book describes and analyses techniques based on Gauss quadrature rules to cheaply compute bounds on norms of the error. The techniques can be used to derive reliable stopping criteria., The conjugate gradient (CG) algorithm is almost always the iterative method of choice for solving linear systems with symmetric positive definite matrices. This book describes and analyzes techniques based on Gauss quadrature rules to cheaply compute bounds on norms of the error. The techniques can be used to derive reliable stopping criteria. Computation of estimates of the smallest and largest eigenvalues during CG iterations is also shown. The algorithms are illustrated by many numerical experiments, and they can be easily incorporated into existing CG codes.
LC Classification Number
QA218.M479 2024

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