I - Linear Algebra

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Linear Algebra is one of the most foundational areas of modern mathematics, widely valued by scientists and engineers for its diverse and powerful applications. Rather than focusing solely on manual computations—something that first-time learners may emphasize, as they often have in other math classes—linear algebra encourages a deep understanding of concepts and structures. While solving problems by hand is a useful exercise, large-scale computations are typically handled by computers. This conceptual approach is crucial for leveraging linear algebra in both practical applications and theoretical explorations. Moreover, linear algebra serves as a gateway to modern algebra, also known as abstract algebra, which provides a "common language" across various branches of mathematics. For enthusiasts of pure mathematics, studying linear algebra offers an excellent introduction to the broader world of contemporary mathematical thought.

Ax=b

Part 1: Linear Equations

System of linear equations Reduced row echelon form Linear combination Span Matrix equation Homogeneous & nonhomogeneous system Linear independence & dependence Parametric vector form
T

Part 2: Linear Transformation

Interactive Demo Linear transformation Linearity Onto One-to-one Matrix multiplication
A

Part 3: Matrix Algebra

Diagonal matrix Identity matrix Transpose of a matrix Invertible matrix Singular matrix Elementary matrix Partitioned Matrix LU Factorization
|A|

Part 4: Determinants

Determinant Cofactor expansion Cramer's rule Adjugate Inverse formula Invertible matrix
V

Part 5: Vector Spaces

Vector space Subspace Null space Kernel Column space Row space Basis Spanning set Coordinate systems Dimention Rank
λ

Part 6: Eigenvalues & Eigenvectors

Eigenvalues Eigenvectors Eigenspace Characteristic equation Similarity Diagonalization Complex eigenvalues & eigenvectors

Part 7: Orthogonality

Interactive Demo Inner product Euclidean norm Orthogonality Orthogonal complement Orthogonal & Orthonormal set Orthogonal projection Orthogonal matrix Gram-Schmidt algorithm QR factorization
min

Part 8: Least-Squares Problems

Interactive Demo Least-squares solution Normal equation Least-quares error Linear regression
S

Part 9: Symmetry

Interactive Demo Symmetric matrix Orthogonally diagonalizable matrix Spectrum Quadratic form Singular value decomposition(SVD)
Tr

Part 10: Trace and Norms

Trace Frobenius norm Nuclear norm Induced norm Spectral norm p-norm Manhattan norm Maximum norm Normalization Regularization Metric space Normed vector space Inner product space Euclidean space

Part 11: Kronecker Product & Tensor

Vectorization Kronecker product Tensor Tensor Product
W

Part 12: Woodbury Matrix Identity

Woodbury matrix identity Sherman-Morrison formula
P

Part 13: Stochastic Matrix

Stochastic matrix Column-stochastic matrix Row-stochastic matrix Probability vector Markov chain Steady-state vector Spectral radius Doubly stochastic matrix