Introduction to Linear Algebra
Systems of Linear Equations
- Introduction
 - Linear systems
 - Vectors
 - Linear combinations
 - Matrices
 - Planes in ℝ³
 - Row operations
 - Gaussian elimination
 - Reduced Row-Echelon Form
 - Equation A x = b
 - Sensitivity of solutions
 - Iterative methods
 - Linear independence
 - Plane transformations
 - Space transformations
 - Rotations
 - Linear transformations
 - Affine maps
 - Exercises
 - Answers
 
Matrix Algebra
- Introduction
 - Manipulation of matrices
 - Partitioned matrices
 - Block matrices Matrix operators
 - Determinants
 - Cofactors
 - Cramer's rule
 - Elementary matrices
 - Inverse matrices
 - Equivalent matrices
 - Rank
 - Elimination: A = L U
 - PLU factorization
 - Reflection
 - Givens rotation
 - Special matrices
 - Exercises
 - Answers
 
Vector Spaces
- Introduction
 - Motivation
 - Vector spaces
 - Bases
 - Dimension
 - Coordinate systems
 - Change of basis
 - Linear transformations
 - Matrix transformations
 - Compositions
 - Isomorphisms
 - Dual spaces
 - Dual transformations
 - Subspaces
 - Direct sums
 - Quotient spaces
 - Vector products
 - Cross products
 - Matrix spaces
 - Rank
 - Solving A x = b
 - Exercises
 - Answers
 
Eigenvalues, Eigenvectors
- Introduction
 - Characteristic polynomials
 - Companion matrix
 - Algebraic and geometric multiplicities
 - Minimal polynomials
 - Eigenspaces
 - Where are eigenvalues?
 - Eigenvalues of A B and B A
 - Generalized eigenvectors
 - Similar matrices
 - Diagonalizability
 - Self-adjoint operators
 
Euclidean Spaces
- Introduction
 - Dot product
 - Bilinear transformations
 - Inner product
 - Norm and distance
 - Matrix norms
 - Dual norms
 - Dual transformations
 - Orthogonality
 - Gram--Schmidt process
 - Orthogonal sets
 - Self-adjoint Matrices
 - Unitary matrices
 - Projection operators
 - QR-decomposition
 - Least Square Approximation
 - Quadratic forms
 - Exercises
 - Answers
 
Matrix Decompositions
- Introduction
 - Symmetric matrices
 - LU-decomposition
 - QR-decomposition
 - Cholesky decomposition
 - Schur decomposition
 - Jordan decomposition
 - Positive matrices
 - Roots
 - Polar factorization
 - Spectral decomposition
 - Singular values
 - SVD <
 - Pseudoinverse
 - Exercises
 - Answers
 
Applications
- GPS problem
 - Poisson equation
 - Graph theory
 - Error correcting codes
 - Electric circuits
 - Markov chains
 - Cryptography
 - Wave-length transfer matrix
 - Computer graphics
 - Linear Programming
 - Hill's determinant
 - Fibonacci matrices
 - Discrete dynamic systems
 - Discrete Fourier transform
 - Fast Fourier transform
 - Curve fitting
 
Functions of Matrices
- Introduction
 - Diagonalization
 - Sylvester formula
 - The Resolvent method
 - Polynomial interpolation
 - Positive matrices
 - Roots <
 - Pseudoinverse
 - Exercises
 - Answers
 
Miscellany
- Circles along curves
 - TNB frames
 - Tensors
 - Tensors in ℝ³
 - Tensors & Mechanics
 - Differential forms
 - Calculus
 - Vector Representations
 - Matrix Representations
 - Change of Basis
 - Orthonormal Diagonalization
 - Generalized Inverse
 - Differential forms
 
Preliminaries
- Complex Number Operations
 - Sets
 - Polynomials
 - Polynomials and Matrices
 - Computer solves Systems of Linear Equations
 - Location of Eigenvalues
 - Power Method
 - Iterative Method
 - Similarity and Diagonalization
 
Glossary
Reference
        This Book is licensed under Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License
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  Similarity
Change-of-Basis
Let α = [e₁, e₂, … , en] and β = [b₁, b₂, … , bn] be two ordered bases of n-dimensional vector space V and let T : V ⇾ V be a linear transformation. Then any vector v can be expanded in uniae way with respect to each basis:
\begin{align*} 
\mathbf{v} &= x_1 {\bf e}_1 +x_2 {\bf e}_2 + \cdots +  x_n {\bf e}_n 
\\ 
&= y_1 {\bf b}_1 + y_2 {\bf b}_2 + \cdots +  y_n {\bf b}_n . 
\end{align*} 
 
Properties of similar transformations
- Anton, Howard (2005), Elementary Linear Algebra (Applications Version) (9th ed.), Wiley International
 - Beezer, R.A., A First Course in Linear Algebra, 2017.
 - Fitzpatrick, S., Linear Algebra: A second course, featuring proofs and Python. 2023.