Introduction to Linear Algebra
Systems of Linear Equations
- Introduction
- Linear systems
- Vectors
- Linear combinations
- Matrices
- Planes in ℝ³
- Reduced Row-Echelon Form
- Equation A x = b
- Sensitivity of solutions
- Linear independence
- Plane transformations
- Space transformations
- Linear transformations
- Affine maps
- Exercises
- Answers
Matrix Algebra
- Introduction
- Manipulation of matrices
- Partitioned matrices
- Block matrices
- Matrix operators
- Determinants
- Cofactors
- Cramer's rule
- Equivalent matrices
- Elimination: A = L U
- PLU factorization
- Reflection
- Givens rotation
- Special matrices
- Exercises
- Answers
Vector Spaces
- Introduction
- Motivation
- Vector spaces
- Bases
- Dimension
- Coordinate systems
- Linear transformations
- Change of basis
- Matrix transformations
- Compositions
- Isomorphisms
- Dual transformations
- Quotient spaces
- Wedge products
- Rotors
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
- Similarity
- Diagonalizability
- Self-adjoint operators
- Exercises
- Answers
Euclidean Spaces
- Introduction
- Dot product
- Euclidean space
- Bilinear transformations
- Norm and distance
- Matrix norms
- Dual norms
- Dual transformations
- Examples of transformations
- Orthogonality
- Gram--Schmidt Process
- Orthogonal matrices
- Self-adjoint matrices
- Unitary matrices
- Projection operators
- QR-decomposition
- Least Square Approximation
- Quadratic forms
- Exercises
- Answers
Matrix Decompositions
- Introduction
- 2D decomposition
- Projectors
- Direct-sum decompositions
- Cyclic decompositions
- Symmetric matrices
- Symmetric matrices
- Pseudoinverse
- URV-decomposition
- LU-decomposition
- QR-decomposition
- Cholesky decomposition
- Schur decomposition
- Positive matrices
- Roots
- Polar factorization
- Spectral decomposition
- CUR decomposition
- Exercises
- Answers
Tensors
- Introduction
- Circles along curves
- TNB frames
- Tensors
- Tensors in ℝ³
- Tensors & Mechanics
- Differential forms
- Calculus
- Answers
Applications
- Introduction
- GPS problem
- Poisson equation
- Graph theory
- Error correcting codes
- Electric circuits
- FSA
- 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
- Answers
Functions of Matrices
- Introduction
- Diagonalization
- Sylvester formula
- The Resolvent method
- Polynomial interpolation
- Positive matrices
- Roots <
- Pseudoinverse
- Exercises
- Answers
Miscellany
- Introduction
- Circles along curves
- TNB frames
- 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
In previous sections we discussed projections on a line or plane in physical vector space. Its generalization for arbitrary vector space over field 𝔽 (which is either ℂ, or ℝ, or ℚ) is considered in this section.
Projectors
An interesting class of endomorphisms that can always be diagonalized are projectors. Special projectors in R n have already been considered in Example 9.1 and Applica- tion 9.1. Projectors have many applications and are worth a closer look. We start with their abstract definition.
An endomorphism p ∈ End(V) in vector space V with p ◦ p = p is called a projector.
- Beezer, R.A., A First Course in Linear Algebra, 2017.