Regularization of Ill-Conditioned Linear Systems

Sheima M. E. Abueldahab

ISBN 10: 6200548757 / ISBN 13: 9786200548757

Published by LAP LAMBERT Academic Publishing Jan 2020, 2020

About this Item

Neuware -Ill-conditioned linear systems arise in many applications, for example, in the solution of integral equations, and in the solution of non-linear programming problems. In many application of linear algebra, the need arises to find a good approximation matrix (bx) to a vector (x) satisfying an approximating equation Ax ¿ b with ill-conditioned matrix (A) , given matrix (b). Straightforward the computed solution (bx) is usually meaningless approximation to ( x ) due to the error in the righthand side ( b ) and the severe ill-conditioning of the matrix ( A).In order to avoid this difficulty, one typically replaces the linear systems Ax = b, by a nearby system that is less sensitive to the error in (b) and considers the computed solution of the latter system an approximation of (x). This replacement is known as regularization. This work examines various regularization methods for computing stable solution to ill-conditioned linear systems. 52 pp. Englisch.

Seller Inventory # 9786200548757

Bibliographic Details

• Title: Regularization of Ill-Conditioned Linear Systems
• Publisher: LAP LAMBERT Academic Publishing Jan 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 6200548757
• ISBN 13: 9786200548757
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 96 grams

About this title

Synopsis

Ill-conditioned linear systems arise in many applications, for example, in the solution of integral equations, and in the solution of non-linear programming problems. In many application of linear algebra, the need arises to find a good approximation matrix (bx) to a vector (x) satisfying an approximating equation Ax ≈ b with ill-conditioned matrix (A) , given matrix (b). Straightforward the computed solution (bx) is usually meaningless approximation to ( x ) due to the error in the righthand side ( b ) and the severe ill-conditioning of the matrix ( A).In order to avoid this difficulty, one typically replaces the linear systems Ax = b, by a nearby system that is less sensitive to the error in (b) and considers the computed solution of the latter system an approximation of (x). This replacement is known as regularization. This work examines various regularization methods for computing stable solution to ill-conditioned linear systems.