Linear Algebra

Chapter 1: Matrices and Gaussian Elimination

1.1: Introduction to Vectors, Matrices, and Linear Equations and Combinations

1.2: Applications – Probability, Graphs and Networks, Economics, Engineering, Computer Graphics, Cryptography, etc

1.3: Lengths and Dot Products

1.4: Matrix Notation and Matrix Multiplication/Rules for Matrix Operations

1.5: Solving Linear Systems using Gaussian and Gauss-Jordan Elimination, Row Echelon and Reduced Row Echelon Form, Rank and Pivot of a Matrix, Rewriting Linear Systems into Ax=B form

1.6: Eliminations = Factorization; A = LU

1.7: Inverse Matrices; Transposes and Permutations

1.8: Triangular Factors and Row Exchanges

Chapter 2: Vector Spaces

2.1: Vector Spaces and Subspaces

2.2: Nullspace of A – Solving Ax=0 and Rx=0; Complete Solution to Ax=b

2.3: Linear Independence, Basis and Dimension

2.4: Dimensions of the Four Fundamental Subspaces

2.5: Graphs and Networks

2.6: Linear Transformations

Chapter 3: Orthogonality

3.1: Vectors and Subspaces; Orthogonality of the Four Subspaces

3.2: Projections (Cosines and Projections onto Lines)

3.3: Projections and Least Squares Approximations

3.4: Orthogonal Bases and Gram-Schmidt

Chapter 4: Determinants

4.1: What are Determinants? Properties, Formulas and Applications

4.2: Permutations and Cofactors

4.3: Cramer’s Rule, Inverses, and Volumes

Chapter 5: Eigenvalues and Eigenvectors

5.1: What are Eigenvalues and Eigenvectors

5.2: Diagonalization of a Matrix

5.3: Systems of Differential Equations

5.4: Symmetric Matrices

5.5: Positive Definite Matrices

Chapter 6: Singular Value Decomposition (SVD)

6.1: Image Processing by Linear Algebra

6.2: Bases and Matrices in the SVD

6.3: Principal Component Analysis (PCA by SVD)

6.4: Geometry of the SVD

Chapter 7: Linear Transformations

7.1: The Idea of a Linear Transformation

7.2: Matrix of a Linear Transformation

7.3: Search for a Good Basis

Chapter 8: Complex Vectors and Matrices

8.1: Complex Numbers

8.2: Hermitian and Unitary Matrices

8.3: Fast Fourier Transform

Chapter 9: Numerical Linear Algebra

9.1: Gaussian Elimination in Practice

9.2: Norms and Condition Numbers

9.3: Iterative Methods and Preconditioners

Extra Practice Problems

Link to Textbook: Linear Algebra & Its Applications by Gilbert Strang