Linear Algebra with Applications by Bretscher

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SUMMARY

Otto Bretscher's "Linear Algebra with Applications" is a comprehensive textbook that covers essential topics such as linear equations, matrix algebra, linear transformations, and eigenvalues. The book is praised for its clear writing style, engaging examples, and historical context, making complex concepts accessible. Some readers have criticized the notation used in the book, but others appreciate its inventive visual approach. Overall, it serves as an effective resource for learning linear algebra.

PREREQUISITES
  • Understanding of linear equations and systems
  • Familiarity with matrices and vectors
  • Basic knowledge of linear transformations
  • Concepts of eigenvalues and eigenvectors
NEXT STEPS
  • Explore the Gram-Schmidt Process and QR Factorization
  • Learn about the properties and geometrical interpretations of determinants
  • Study the concepts of orthogonal projections and orthonormal bases
  • Investigate linear differential equations and their applications
USEFUL FOR

This discussion is beneficial for students and educators in mathematics, particularly those studying or teaching linear algebra, as well as anyone interested in applying linear algebra concepts in various fields.

For those who have used this book

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  • Lightly don't Recommend

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  • Strongly don't Recommend

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  • Total voters
    1
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Table of Contents:
Code:
[LIST]
[*] Text Features
[*] Preface
[*] Linear Equations
[LIST]
[*] Introduction to Linear Systems
[*] Matrices, Vectors, and Gauss-Jordan Elimination
[*] On the Solutions of Linear Systems; Matrix Algebra
[/LIST]
[*] Linear Transformations
[LIST]
[*] Introduction to Linear Transformations and Their Inverses
[*] Linear Transformations in Geometry
[*] Matrix Products
[*] The Inverse of a Linear Transformation
[/LIST]
[*] Subspaces of Mn and Their Dimensions
[LIST]
[*] Image and Kernel of a Linear Transformation
[*] Subspaces of R^n; Bases and Linear Independence
[*] The Dimension of a Subspace of R^n
[*] Coordinates
[/LIST]
[*] Linear Spaces
[LIST]
[*] Introduction to Linear Spaces
[*] Linear Transformations and Isomorphisms
[*] The Matrix of a Linear Transformation
[/LIST]
[*] Orthogonality and Least Squares
[LIST]
[*] Orthogonal Projections and Orthonormal Bases
[*] Gram-Schmidt Process and QR Factorization
[*] Orthogonal Transformations and Orthogonal Matrices
[*] Least Squares and Data Fitting
[*] Inner Product Spaces
[/LIST]
[*] Determinants
[LIST]
[*] Introduction to Determinants
[*] Properties of the Determinant
[*] Geometrical Interpretations of the Determinant; Cramer’s Rule
[/LIST]
[*] Eigenvalues and Eigenvectors
[LIST]
[*] Dynamical Systems and Eigenvectors: An Introductory Example
[*] Finding the Eigenvalues of a Matrix
[*] Finding the Eigenvectors of a Matrix
[*] Diagonalization
[*] Complex Eigenvalues
[*] Stability
[/LIST]
[*] Symmetric Matrices and Quadratic Forms
[LIST]
[*] Symmetric Matrices
[*] Quadratic Forms
[*] Singular Values
[/LIST]
[*] Linear Differential Equations
[LIST]
[*] An Introduction to Continuous Dynamical Systems
[*] The Complex Case: Euler’s Formula
[*] Linear Differential Operators and Linear Differential Equations
[/LIST]
[*] Appendix: Vectors
[*] Answers to Odd-Numbered Exercises
[*] Subject Index 
[*] Name Index
[/LIST]
 
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Using this book in my linear algebra course right now and I love the writing style, very clear examples and approach. There's even very cool historical examples, and splashes of wit/humor, but tastefully done.

It's been criticized by some for some of it's notation, which people seem not to like or find weird, but which I actually find cool, but I'm a fan of inventive visual notation.

-Dave K
 

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