# Linear Algebra Study Guide Boost Your grades with this illustrated quick-study guide. You will use it from college all the way to graduate school and beyond. FREE chapters on Linear equations, Determinant, and more in the trial version.

Clear and concise explanations
Difficult concepts are explained in simple terms
Illustrated with graphs and diagrams

I. Linear equations

System of linear equations
Determinant
Minor
Cauchy-Binet formula

Cramer''s rule
Gaussian elimination
Gauss-Jordan elimination
Strassen algorithm

II. Matrices

Matrix multiplication
Basis transformation matrix
Characteristic polynomial, Characteristic Equation
Trace
Eigenvalue, eigenvector and eigenspace
Cayley-Hamilton theorem
Symbolic Computation of Matrix Eigenvalues

Jordan normal form
Rank
Matrix inversion,
Pseudoinverse

Transpose
Dot product
Symmetric matrix
Matrix congruence

Congruence relation

Orthogonal matrix
Skew-symmetric matrix
Conjugate transpose

Unitary matrix
matrix, Antihermitian

Positive definite: matrix, function, bilinear form
Identity matrix
Pfaffian
Projection
Diagonal matrix, main diagonal
Diagonalizable matrix

Similar matrix
Tridiagonal matrix
Hessenberg matrix
Triangular matrix
Spectral theorem
Stochastic matrix
Toeplitz matrix
Circulant matrix

Hankel matrix
Vandermonde matrix
Block matrix
(0,1)-matrix
Normal Matrix
Sparse matrix
Woodbury matrix identity
Perron-Frobenius theorem
List of Matrices

III. Matrix decompositions

Block LU Decomposition
Cholesky decomposition
LU decomposition
QR decomposition
Spectral theorem
Singular value decomposition
Schur decomposition
Schur complement

IV. Computations

Transformation Matrix
Householder transformation
Least squares, linear least squares
Gram-Schmidt process

V. Vectors

Unit Vector
Pseudovector
Normal Vector
Tangential and Normal Components
Scalar multiplication
Linear combination
Linear span
Linear independence
Basis

VI. Vector spaces

Basis=Hamel basis
Dimension theorem for vector spaces=Hamel dimension
Examples of vector spaces
Linear map
Galilean transformation, Lorentz transformation

Row and Column space
Null space
Rank-nullity theorem
Dual space
Linear function
Linear functional

Orthogonality
Orthogonal complement
Orthogonal projection

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