Applied Linear Algebra 3/e
售價
$
940
- 一般書籍
- ISBN：9789867727749
- 作者：Ben Noble, James W. Daniel
- 版次：3
- 年份：1988
- 出版商：Pearson Education
- 頁數/規格：521頁/平裝單色
書籍介紹
本書特色
目錄
Description
This classic volume applies linear algebra to a variety of disciplines—engineering, the physical sciences, social sciences, and business. It motivates the reader with illustrative examples. This is a competitor to Strang.
This classic volume applies linear algebra to a variety of disciplines—engineering, the physical sciences, social sciences, and business. It motivates the reader with illustrative examples. This is a competitor to Strang.
Features
- contains important modern applications such as signal processing and Karmarkar's approach to linear programming.
- uses Gauss reduction and “Gauss-reduced form” as the fundamental theoretical and computational tool.
- includes examples and problems using modern software for matrix computations, and describes properties and sources of software for real applied problems.
- stresses both the theoretical and practical importance of tools such as the singular-value decomposition and generalized (pseudo) inverses, the QR decomposition, Householder transformations/matrices, and orthogonal projections.
- features 1,100 exercises, including optional computer examples and problems.
Table of Contents
1. Matrix Algebra.
2. Some Simple Applications and Questions.
3. Solving Equations and Finding Inverses: Methods.
4. Solving Equations and Finding Inverses: Theory.
5. Vectors and Vector Spaces.
6. Introduction; Geometrical Vectors.
7. Linear Transformations and Matrices.
8. Eigenvalues and Eigenvectors: An Overview.
9. Eigensystems of Symmetric Hermitian, and Normal Matrices, with Applications.
10. Eigensystems of General Matrices, with Applications.
11. Quadratic Forms and Variational Characterizations of Eigenvalues.
12. Linear Programming.
Answers and Aids to Selected Problems.
Bibliography.
Index.
1. Matrix Algebra.
2. Some Simple Applications and Questions.
3. Solving Equations and Finding Inverses: Methods.
4. Solving Equations and Finding Inverses: Theory.
5. Vectors and Vector Spaces.
6. Introduction; Geometrical Vectors.
7. Linear Transformations and Matrices.
8. Eigenvalues and Eigenvectors: An Overview.
9. Eigensystems of Symmetric Hermitian, and Normal Matrices, with Applications.
10. Eigensystems of General Matrices, with Applications.
11. Quadratic Forms and Variational Characterizations of Eigenvalues.
12. Linear Programming.
Answers and Aids to Selected Problems.
Bibliography.
Index.
