Computation of Generalized Matrix Inverses and Applications
暫譯: 廣義矩陣逆的計算與應用

Ivan Stanimirović

  • 出版商: Apple Academic Press
  • 出版日期: 2017-12-13
  • 售價: $5,240
  • 貴賓價: 9.5$4,978
  • 語言: 英文
  • 頁數: 292
  • 裝訂: Hardcover
  • ISBN: 1771886226
  • ISBN-13: 9781771886222
  • 海外代購書籍(需單獨結帳)

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商品描述

This volume 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. It covers several methods, such as methods based on full-rank factorization, Leverrier–Faddeev method, method of Zhukovski, and variations of the partitioning method.

商品描述(中文翻譯)

本卷提供了矩陣理論作為線性代數的一個主題的逐步介紹。它展示了廣義矩陣逆的理論結果及其應用。這本書儘可能自成一體,假設讀者對矩陣理論和線性代數沒有先前的知識。

本書首先討論了任意廣義矩陣逆的基本定義和概念,特別提到 {i,j,...,k} 逆和 Moore–Penrose 逆的計算。接著,介紹了全秩多項式矩陣的 LDL* 分解結果,並附上數值範例。提出了計算有理矩陣的 Moore–Penrose 逆的方法,這些方法基於矩陣的 LDL* 和 QDR 分解。還推導出使用 LDL* 分解計算 A(2)T;S 逆的方法,以及使用 QDR 因式分解進行 A(2)T;S 逆的符號計算。

接下來,文本提供了幾種如何將所介紹的理論概念應用於恢復模糊圖像和線性回歸方法的方式,以及在線性系統中的著名應用。本書還解釋了如何計算具有常數值的矩陣的廣義逆。它涵蓋了幾種方法,例如基於全秩因式分解的方法、Leverrier–Faddeev 方法、Zhukovski 方法以及分區方法的變體。