| A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z ## A Absorbtion spectrometry Affine function Affine set Angle between vectors Auto-regressive models for time-series prediction ## B Backward substition 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 -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