Linear Algebra: Foundations to Frontiers by M.E. Myers, P.M. van de Geijn, R.A. van de Geijn
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About this book :-
This course combines theory and practical computing, helping students understand both the math behind linear algebra and how to apply it using computers. With many video lectures and exercises, it offers a hands-on way to learn important topics and real-world applications. The course helps students learn the math of linear algebra and how to use it on computers. Van de Geijn focuses on high-performance computing and algorithm design, while Myers brings expertise in statistics and math education, making their course both thorough and accessible.
The book is great for students and essential for mathematicians, engineers, scientists, and anyone working with large datasets. It explains things step by step and includes examples using Python. It connects hand calculations, mathematical abstractions, and computer programming. It encourages you to develop the mathematical theory of linear algebra by posing questions rather than outright stating theorems and their proofs.
Book Detail :-
This book has following details information.
Title:
Linear Algebra: Foundations to Frontiers by M.E. Myers, P.M. van de Geijn, R.A. van de Geijn by NA
Publisher:
ulaff.net
Series:
FreeComp
Year:
2014
Pages:
905
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.
0. Notes on Setting Up
1. Notes on Simple Vector and Matrix Operations
2. Notes on Vector and Matrix Norms
3. Notes on Orthogonality and the Singular Value Decomposition
4. Notes on Gram-Schmidt QR Factorization
5. Notes on the FLAME APIs Algorithms
6. Notes on Householder QR Factorization
7. Notes on Rank Revealing Householder QR Factorization
8. Notes on Solving Linear Least-Squares Problems
9. Notes on the Condition of a Problem
10. Notes on the Stability of an Algorithm
11. Notes on Performance
12. Notes on Gaussian Elimination and LU Factorization
13. Notes on Cholesky Factorization
14. Notes on Eigenvalues and Eigenvectors
15. Notes on the Power Method and Related Methods
16. Notes on the QR Algorithm and other Dense Eigensolvers
17. Notes on the Method of Relatively Robust Representations (MRRR)
18. Notes on Computing the SVD of a Matrix
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