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E-raamat: Advanced Linear Algebra

(Drexel University, Philadelphia, Pennsylvania, USA)
  • Formaat: 349 pages
  • Sari: Textbooks in Mathematics
  • Ilmumisaeg: 23-Dec-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498754040
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  • Formaat: 349 pages
  • Sari: Textbooks in Mathematics
  • Ilmumisaeg: 23-Dec-2015
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498754040

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Advanced Linear Algebra features a student-friendly approach to the theory of linear algebra. The authors emphasis on vector spaces over general fields, with corresponding current applications, sets the book apart. He focuses on finite fields and complex numbers, and discusses matrix algebra over these fields. The text then proceeds to cover vector spaces in depth. Also discussed are standard topics in linear algebra including linear transformations, Jordan canonical form, inner product spaces, spectral theory, and, as supplementary topics, dual spaces, quotient spaces, and tensor products.

Written in clear and concise language, the text sticks to the development of linear algebra without excessively addressing applications. A unique chapter on "How to Use Linear Algebra" is offered after the theory is presented. In addition, students are given pointers on how to start a research project. The proofs are clear and complete and the exercises are well designed. In addition, full solutions are included for almost all exercises.

Arvustused

Woerdemans work requires background knowledge of linear algebra. Students should be familiar with matrix computations, solving systems, eigenvalues, eigenvectors, finding a basis for the null space, row and column spaces, determinants, and inverses. This text provides a more general approach to vector spaces, developing these over complex numbers and finite fields. Woerdeman (mathematics, Drexel Univ.) provides a review of complex numbers and some basic results for finite fields. This book will help build on previous knowledge obtained from an earlier course and introduce students to numerous advanced topics. A few of these topics are Jordan canonical form, the Cayley-Hamilton Theorem, nilpotent matrices, functions of matrices, Hermitian matrices, the tensor product, quotient space, and dual space. The last chapter, which discusses how to use linear algebra, illustrates some applications, such as finding roots of polynomials, algorithms based on matrix vector products, RSA public key inscription, and theoretical topics, such as the Riemann hypothesis and the P versus NP problem. Copious exercises are provided, and most give complete solutions. The text will provide a solid foundation for any further work in linear algebra. --R. L. Pour, Emory and Henry College

Preface to the Instructor xi
Preface to the Student xiii
Acknowledgments xv
Notation xvii
List of Figures
xxi
1 Fields and Matrix Algebra
1(26)
1.1 The field Z3
2(1)
1.2 The field axioms
3(2)
1.3 Field examples
5(6)
1.3.1 Complex numbers
7(2)
1.3.2 The finite field Zp, with p prime
9(2)
1.4 Matrix algebra over different fields
11(9)
1.4.1 Reminders about Cramer's rule and the adjugate matrix
17(3)
1.5 Exercises
20(7)
2 Vector Spaces
27(28)
2.1 Definition of a vector space
27(2)
2.2 Vector spaces of functions
29(3)
2.2.1 The special case when X is finite
31(1)
2.3 Subspaces and more examples of vector spaces
32(5)
2.3.1 Vector spaces of polynomials
34(2)
2.3.2 Vector spaces of matrices
36(1)
2.4 Linear independence, span, and basis
37(8)
2.5 Coordinate systems
45(3)
2.6 Exercises
48(7)
3 Linear Transformations
55(14)
3.1 Definition of a linear transformation
55(2)
3.2 Range and kernel of linear transformations
57(4)
3.3 Matrix representations of linear maps
61(4)
3.4 Exercises
65(4)
4 The Jordan Canonical Form
69(40)
4.1 The Cayley--Hamilton theorem
69(2)
4.2 Jordan canonical form for nilpotent matrices
71(4)
4.3 An intermezzo about polynomials
75(3)
4.4 The Jordan canonical form
78(4)
4.5 The minimal polynomial
82(2)
4.6 Commuting matrices
84(3)
4.7 Systems of linear differential equations
87(3)
4.8 Functions of matrices
90(8)
4.9 The resolvent
98(2)
4.10 Exercises
100(9)
5 Inner Product and Normed Vector Spaces
109(38)
5.1 Inner products and norms
109(10)
5.2 Orthogonal and orthonormal sets and bases
119(3)
5.3 The adjoint of a linear map
122(3)
5.4 Unitary matrices, QR, and Schur triangularization
125(3)
5.5 Normal and Hermitian matrices
128(4)
5.6 Singular value decomposition
132(5)
5.7 Exercises
137(10)
6 Constructing New Vector Spaces from Given Ones
147(48)
6.1 The Cartesian product
147(2)
6.2 The quotient space
149(8)
6.3 The dual space
157(9)
6.4 Multilinear maps and functionals
166(2)
6.5 The tensor product
168(11)
6.6 Anti-symmetric and symmetric tensors
179(10)
6.7 Exercises
189(6)
7 How to Use Linear Algebra
195(52)
7.1 Matrices you can't write down, but would still like to use
196(2)
7.2 Algorithms based on matrix vector products
198(5)
7.3 Why use matrices when computing roots of polynomials?
203(6)
7.4 How to find functions with linear algebra?
209(8)
7.5 How to deal with incomplete matrices
217(5)
7.6 Solving millennium prize problems with linear algebra
222(7)
7.6.1 The Riemann hypothesis
223(2)
7.6.2 P vs. NP
225(4)
7.7 How secure is RSA encryption?
229(3)
7.8 Quantum computation and positive maps
232(6)
7.9 Exercises
238(9)
How to Start Your Own Research Project 247(2)
Answers to Exercises 249(74)
Bibliography 323(2)
Index 325
Hugo J. Woerdeman, PhD, professor, Department of Mathematics, Drexel University, Philadelphia, Pennsylvania, USA