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Linear Algebra
Solving Linear Systems
01Row Picture, Column Picture & Matrix Form02Row Picture vs Column Picture in 3D03Singular vs Non-Singular Matrices04Two Ways to Compute AxProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
01Gaussian Elimination & Back Substitution02Elimination Matrices03Permutation Matrices and InversesProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
015 Views of Matrix Multiplication02Matrix Inverses and Singularity03Gauss-Jordan Elimination for InversesProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01Product Inverse & Transpose Rules02A = LU Factorization03Gaussian Elimination: ⅓n³ Operation Count04Permutation Matrices: P⁻¹ = PᵀProblem set0/10Problem set 20/10MIT problem set0/6Practice∞
01Permutation Matrices and PA = LU02Why RᵀR Is Always Symmetric03Vector Spaces and Subspaces04The Column Space of a MatrixProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
Vector Spaces & Subspaces
01Vector Spaces and Subspaces02Column Space of a Matrix03Null Space of a MatrixProblem set0/10Problem set 20/10Practice∞
01Rank, Pivots, and Free Variables02Special Solutions of the Null Space03Reading the Null Space from RREFProblem set0/10Problem set 20/10MIT problem set0/3Practice∞
01Solvability of Ax = b02Particular Solutions and the Null Space03Rank and the Four Cases of Ax = bProblem set0/10Problem set 20/10MIT problem set0/8Practice∞
01Linear Independence & the Null Space02Basis and Dimension03The Rank-Nullity TheoremProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01Rank and the Four Fundamental Subspaces02Four Bases from One Row Reduction03Matrices as Vector SpacesProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
01The Dimension Formula for Subspaces02Differential Equations as Linear Algebra03Rank-One Matrices and Outer Products04The Four Fundamental SubspacesProblem set0/10Problem set 20/10MIT problem set0/1Practice∞
01The Incidence Matrix as Difference Operator02The Incidence Matrix of a Graph03The Equilibrium Equation AᵀCAx = fProblem set0/10Problem set 20/10MIT problem set0/1Practice∞
Orthogonality & Least Squares
01Orthogonal Subspaces02Null Space as Orthogonal Complement03The Normal EquationsProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
01The Projection Matrix P = aaᵀ/aᵀa02Projection onto Subspaces03Projection and Least SquaresProblem set0/10Problem set 20/10MIT problem set0/6Practice∞
01Complementary Projections: P and I−P02Least Squares and the Normal Equations03Least Squares as Projection04Invertibility of AᵀAProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01Orthogonal Matrices02Projections onto Orthonormal Bases03The Gram-Schmidt Process04QR Factorization from Gram-SchmidtProblem set0/10Problem set 20/10MIT problem set0/3Practice∞
Determinants
01The Three Axioms of the Determinant02Determinant Properties from Three Axioms03Determinants via Elimination04Multiplicative and Transpose PropertiesProblem set0/10Problem set 20/10MIT problem set0/2Practice∞
01The Permutation Formula for Determinants02Cofactor Expansion03Periodic Determinants of Tridiagonal MatricesProblem set0/10Problem set 20/10MIT problem set0/3Practice∞
01The Cofactor Formula for Matrix Inverses02Cramer's Rule03Determinants as VolumeProblem set0/10Problem set 20/10MIT problem set0/4Practice∞
Eigenvalues & Dynamics
01Eigenvalues and Eigenvectors02Finding Eigenvalues and Eigenvectors03Complex Eigenvalues and Defective MatricesProblem set0/10Problem set 20/10MIT problem set0/2Practice∞
01Diagonalization: A = SΛS⁻¹02Matrix Powers and the Stability Theorem03Defective Matrices and Multiplicity04Eigenvalues and the Fibonacci SequenceProblem set0/10Problem set 20/10MIT problem set0/3Practice∞
01Solving du/dt = Au with Eigenvalues02Eigenvalue Stability in the Complex Plane03The Matrix Exponential e^(At)04The Companion MatrixProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01Why 1 Is Always an Eigenvalue of Markov Matrices02Markov Chain Steady States via Eigenvalues03Markov Matrices and Steady States04Orthonormal Bases and Fourier CoefficientsProblem set0/10Problem set 20/10MIT problem set0/7Practice∞
Symmetric Matrices & the SVD
01The Spectral Theorem02Why Symmetric Matrices Have Real Eigenvalues03Sylvester's Law of Inertia04Testing Positive DefinitenessProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01The Conjugate Transpose02Inverse of the Fourier Matrix03The FFT FactorizationProblem set0/10Problem set 20/10Practice∞
01Positive Definite Matrices02Positive Definiteness via Pivots03The Hessian and Positive Definiteness04Principal Axis Theorem and the EllipsoidProblem set0/10Problem set 20/10MIT problem set0/6Practice∞
01Closure Properties of Positive Definite Matrices02Matrix Similarity and Invariant Eigenvalues03Why Eigenvalues Don't Classify Similarity04Jordan Canonical FormProblem set0/10Problem set 20/10MIT problem set0/5Practice∞
01The Singular Value Decomposition02Reducing SVD to A^T A03Computing the SVD: Two Worked Examples04SVD and the Four Fundamental SubspacesProblem set0/10Problem set 20/10MIT problem set0/3Practice∞
Linear Transformations & Applications
01Defining Linear Transformations02Basis, Coordinates, and Linear Maps03How a Linear Map Becomes a MatrixProblem set0/10Problem set 20/10Practice∞
01Image Compression as a Change of Basis02Choosing a Basis: JPEG, Fourier, and Wavelets03Change of Basis and Image Compression04Change of Basis and DiagonalizationProblem set0/10Problem set 20/10Practice∞
01Matrix Inverses and the Four Subspaces02One-Sided Inverses and Projections03The Pseudo-Inverse04The Pseudo-Inverse via the SVDProblem set0/10Problem set 20/10Practice∞

Matrices as Vector Spaces

The set of all n × n matrices forms a vector space under addition and scalar multiplication, and familiar subspace concepts like basis, dimension, and intersection apply naturally.


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Your summary note

    1. 1

      Vector space axioms satisfied by 3×33 \times 33×3 matrices

      Record that any set closed under addition and scalar multiplication with the axioms holding is a vector space, and check the 3×33 \times 33×3 matrices with the zero matrix as identity.

    2. 2

      Diagonal matrices as an intersection of subspaces

      List the upper triangular, symmetric and diagonal subspaces, record the diagonal matrices as the intersection of the first two, and write out the basis of three matrices giving dimension 333.

    Attempt 1 of 2