A

• Absorbtion spectrometry
• Affine function
• Affine set
• Angle between vectors
• Auto-regressive models for time-series prediction

B

• Backward substitution method for solving triangular systems of linear equations
• Bag-of-word representation of text
• Basis of a subspace
• Beer-Lambert law

C

• CAT scan imaging
• Cauchy-Schwartz inequality
• Cardinality of a vector
• Cardinality minimization, see also l_1-norm
• Circuit design via Geometric Programming
• Combinational logic in circuit design
• Componentwise inequality between vectors
• Condition number of a matrix
• Convex function
• Convex optimization problem
• Covariance matrix
• CVX

D

• Determinant of a square matrix
• Dimension of an affine set
• Domain of a function
• Duality: weak, strong
• Dual norm
• Dual problem
• Dual function
• Dual norm, see also norm
• Dyad

E

• Eigenvalue decomposition (EVD) of a square, symmetric matrix
• Epigraph of a function
• Expected value of a random variable

F

• Feasible point, feasible set
• First-order approximation
• Frobenius norm of a matrix
• Function
• Fundamental theorem of linear algebra

G

• Geometric program (GP)
• Global optimum
• Gradient of a function
• Gradient methods, for solving convex programs
• Gram matrix
• Gram-Schmidt (GS) procedure
• Graph of a function

H

• Half-space
• Hessian of a function
• Hyperplane

I

• Image compression
• Incidence matrix of a graph
• Independent set of vectors
• Interior-point methods, for solving convex programs
• Inverse of a matrix

J

• Jacobian matrix of a non-linear map

K

• Kernel matrix, kernel trick

L

• Laplacian of a graph
• Lagragian of an optimization problem
• Laplace formula for the determinant of a matrix
• Largest singular value (LSV) norm of a matrix
• Least-squares
• Leibnitz formula for the determinant of a matrix
• Left inverse of a matrix
• Linear function
• Linear matrix inequality
• Linear program (LP)
• Linear regression
• Local optimum
• Log-return of a financial asset
• Log-sum-exp function
• norms of a vector:   norm, -norm (also called Euclidean norm),   norm.
• norm, or cardinality, of a vector.

M

• Maps
• Matrix
• Matrix-vector product
• Sample Mean
• Minimax inequality

N

• Norms: general definition, for vectors, for matrices; see also dual norm
• Nullspace of a matrix

O

• Optimal point, optimal value, optimal set
• Orthogonal: vectors, matrices

P

• Permutation matrix
• Point-wise maximum of functions
• Polyhedron
• Polyhedral function
• Positive-definite
• Power laws
• Principal Component Analysis (PCA)
• Probability simplex
• Projection: on a line
• Pseudo-inverse of a matrix

Q

• Quadratic program (QP)
• Quadratic form, quadratic function

R

• Range of a matrix
• Rank of a matrix
• Rate of return of a financial asset
• Rayleigh quotient
• Regression: Linear
• Right inverse of a matrix
• Robust linear program

S

• Sample mean, sample standard deviation, sample covariance matrix
• Scalar product: for vectors, for matrices
• Second-order approximation of a function
• Second-order cone
• Second-order cone program (SOCP)
• Semidefinite program (SDP)
• Singular value of a general matrix
• Singular value decomposition (SVD)
• Slater’s condition for strong duality
• Spectral theorem, or symmetric eigenvalue decomposition (SED) theorem
• Symmetric matrix

T

• Tomography
• Trace of a matrix
• Triangle inequality
• Triangular matrices
• Triangular systems of linear equations, see also backward substitution algorithm

U

• Unconstrained optimization
• Unitary matrix (see also Orthogonal matrix)

V

• Vectors
• Sample Variance

W

X

Y

Z