Applied and Computational Linear Algebra: A First Course by Charles L. Byrne
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About this book :-
This is a practical textbook designed for students new to linear algebra. This text is intended for a graduate course that focuses on applications of linear algebra and on the algorithms used to solve the problems that arise in those applications. It explains important ideas like vectors, matrices, and eigenvalues, and shows how these concepts are used in real-world problems, especially in fields like engineering, computer science, and data science. The book focuses on both understanding the math and learning how to solve problems using computer tools like MATLAB or Python. It includes clear examples and exercises to help students practice and apply what they’ve learned. It’s a great choice for anyone looking to learn both the theory and practical uses of linear algebra.
Book Detail :-
This book has following details information.
Title:
Applied and Computational Linear Algebra: A First Course by Charles L. Byrne by NA
Publisher:
University of Massachusetts Lowell
Series:
FreeComp
Year:
2013
Pages:
504
Type:
PDF
Language:
English
ISBN-10 #:
N\A
ISBN-13 #:
N\A
Country:
Pakistan
License:
N\A
Get this book from Amazon
About Author :-
The author NA
NA
Book Contents :-
conver the following topics.
Part-I Preliminaries
1. Introduction
2. An Overview of Applications
3. A Little Matrix Theory
4. The ART, MART and EM-MART
Part-II Algebra
5. More Matrix Theory
6. Metric Spaces and Norms
7. Under-Determined Systems of Linear Equations
8. Convex Sets
9. Linear Inequalities
Part-III Algorithms
10. Fixed-Point Methods
11. Jacobi and Gauss-Seidel Methods
12. A Tale of Two Algorithms
13. Block-Iterative Methods I
14. The Split Feasibility Problem
15. Conjugate-Direction Methods
16. Regularization
17. Transmission Tomography I
18. Transmission Tomography II
19. Emission Tomography
20. Magnetic Resonance Imaging
21. Intensity Modulated Radiation Therapy
22. Appendix: Linear Algebra
23. Appendix: Even More Matrix Theory
24. Appendix: More ART and MART
25. Appendix: Constrained Iteration Methods
26. Appendix: Block-Iterative Methods II
27. Appendix: Eigenvalue Bounds
28. Appendix: List-Mode Reconstruction in PET
29. Appendix: A Little Optimization
30. Appendix: Geometric Programming and the MART
31. Appendix: Fourier Transforms and the FFT
32. Appendix: Hermitian and Normal Linear Operators
33. Appendix: Sturm-Liouville Problems
34. Appendix: Hermite’s Equations and Quantum Mechanics
35. Appendix: The BLUE and The Kalman Filter
36. Appendix: Matrix and Vector Differentiation
37. Appendix: Signal Detection and Estimation
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