Computation of generalized matrix inverses/ Ivan Stanimirovic
Publication details: Canada: Apple Academic Press, 2018Description: vi, 280 pages; 24 cmISBN:- 9781771886222
- 23rd 512.9434 St784
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Books | ISI Library, Kolkata | 512.9434 St784 (Browse shelf(Opens below)) | Available | 138668 |
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512.9434 SMSG.A Mathematics for high school : introduction to matrix algebra | 512.9434 SMSG.A.R Mathematics for high school | 512.9434 Sr774 Exact order matrices and the linear complimentarity problem | 512.9434 St784 Computation of generalized matrix inverses/ | 512.9434 St794 Lectures on analytic S-matrix theory | 512.9434 St813 Problems and solutions in introductory and advanced matrix calculus | 512.9434 St849 Introduction to matrix computations |
Includes bibliography and index
Introduction -- Computing generalised inverses of matrices -- Generalised inverses of polynomial and rational matrices -- Applications -- Conclusion
offers a gradual exposition to matrix theory as a subject of linear algebra. It presents both the theoretical results in generalized matrix inverses and the applications. The book is as self-contained as possible, assuming no prior knowledge of matrix theory and linear algebra.
The book first addresses the basic definitions and concepts of an arbitrary generalized matrix inverse with special reference to the calculation of {i,j,...,k} inverse and the Moore-Penrose inverse. Then, the results of LDL* decomposition of the full rank polynomial matrix are introduced, along with numerical examples. Methods for calculating the Moore-Penrose’s inverse of rational matrix are presented, which are based on LDL* and QDR decompositions of the matrix. A method for calculating the A(2)T;S inverse using LDL ? decomposition using methods is derived as well as the symbolic calculation of A(2)T;S inverses using QDR factorization.
The text then offers several ways on how the introduced theoretical concepts can be applied in restoring blurred images and linear regression methods, along with the well-known application in linear systems. The book also explains how the computation of generalized inverses of matrices with constant values is performed, and covers several methods, such as methods based on full-rank factorization, Leverrier-Faddeev method, method of Zhukovski, and variations of partitioning method.
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