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Useful Facts, Identities, Inequalities Useful Facts, Identities, Inequalities Linear Algebra Determinants Facts about definite matrices Notation Lemma 1. [Sylvester’s Identity] Assume A is positive definite. Then: Notation Name Comment Let A 2 Mm;n and B 2 Mn;m. Then: •A is always full rank. A a matrix indicated by capitalization and bold •A is invertible and A−1 is also positive definite. font det(Im + AB) = det(In + BA) (3) [A]ij the element from the i-th row and j-th Sometimes denoted Aij for brevity •A is positive definite , there exists an invertible matrix B such that A = BB>. column of A Mn the set of complex-valued matrices e.g., A 2 Mm;n •Aii > 0 with m rows and n columns > M 2 M Determinant Facts •rank(BAB ) = rank(B). n the set of complex-valued square e.g., A n 2 M f g Q For A n Pwith eigenvalues λ(A) = λ1; : : : λn , ≤ matrices n •det(A) i Aii I the identity matrix e.g., I = diag [1; 1;:::; 1] •tr(A) = i=1 λi Q > e vector of ones n •If X 2 Mn;r, with n ≤ r and rank(X) = n, then XX is positive definite. •det(A) = i=1 λi σi(A) i-th singular value of A Typically, we assume sorted order: y f g f g •A positive definite , eig( A+A ) > 0 (and ≥ 0 for A positive-semidefinite). σ1(A) ≥ σ2(A) ≥ ::: •Also, det(exp A ) = exp tr(A) . 2 f g 2 M λi(A) the i-th eigenvalue of A may also write λ(A) = λi i for the Lemma 2 (Block Matrix Determinants). Let M n+m be a square matrix partitioned as: •If B is symmetric, then A − tB is positive definite for sufficiently small t. set of eigenvalues. 2 M fj jg AB ρ(A) the spectral radius of A n ρ(A) = max λi M = (4) Projections 1≤i≤n CD ¯ ¯ A the complex conjugate of A operates entrywise, e.g. Aij = Aij Definition 14 A> the matrix transpose of A where A 2 Mn, D 2 Mm, with B and C being n × m and m × n respectively. y y def ¯ > Let V be a vector space, and U ⊆ V be a subspace. The projection of v 2 V onto some A the conjugate transpose of A e.g., A = (A) If B = 0n;m or C = 0m;n, then − − k·k A 1 the matrix inverse of A e.g., A 1A = I subspace with respect to some norm q is: − A+ the Moore-Penrose pseudoinverse of A sometimes may just use A 1 due to det(M) = det(A) det(D) (5) def k − k sloppy notation Πv = argmin v u q (10) u2U Definitions Furthermore, if A is invertible, then y ¯ > For the (weighted) Euclidean norm, Π can itself be expressed as a matrix. Definition 1 (Complex Conjugate). Let A 2 Mn, then A = (A) . > −1 Definition 2 (Symmetric Matrix). A matrix A such that A = A det(M) = det(A) det D − CA B (6) > −1 > ΠD = X(X DX) X D (11) Definition 3 (Hermitian Matrix). A matrix A such that A = Ay = (A¯ )>. , y y Definition 4 (Normal Matrix). A normal A A = AA A similar identity holds for D invertible. Although sometimes we just write Π instead of ΠD. That is, a normal matrix is one that commutes with its conjugate transpose. Lemma 3 (Block Triangular Matrix Determinants). Consider a matrix M 2 M of the , y y m;n Definition 5 (Unitary Matrix). A unitary I = A A = AA . form: Definition 6 (Orthogonal Matrix). A square matrix A whose columns and rows are orthogonal unit vectors, that is AA> = A>A. 0 1 Projection Facts S11 S12 ··· S1n Definition 7 (Idempotent matrix). A matrix such that AA = A. B ··· C •Projections are idempotent, i.e., Π2 = Π. BS21 S22 S2n C Definition 8 (Nilpotent Matrix). A matrix such that A2 = AA = 0. M = B C (7) @ . .. A •A square matrix Π 2 M is called an orthogonal projection matrix if Π2 = Π = Πy. Definition 9 (Unipotent Matrix). A matrix A such that A2 = I. n Definition 10 (Stochastic Matrix). Consider a matrix, say P with no negative entries. P Sn1 Sn2 ··· Snn •A non-orthogonal projection is called an oblique projection, usually expressed as is row stochastic matrix if each row sums to one; it is column stochastic if each column sums Π = A(B>A)−1B>. × to one. By convention, usually mean row stochastic when using “stochastic” without where each Sij is an m m matrix. •The triangle inequality applies, kxk = k(I − Π)x + Πxk ≤ k(I − Π)xk + kΠxk. qualification. A matrix is doubly stochastic if it is both row and column stochastic. If M is in block triangular form (that is, Sij = 0 for j > i), then Definition 11 (Matrix congruence). Two square matrices A and B over a field are called > Yn Singular Values congruent if there exists an invertible matrix P over the same field such that P AP = B. × det(M) = det(Sii) = det(S11) det(S22) ··· det(Snn) (8) Let A 2 Cm n and r = minfm; ng, with σ(A) = fσ (A)gr , and U; V are subspaces with Definition 12 (Matrix similarity). Two square matrices A and B are called similar if there k k=1 i=1 ⊆ n ⊆ m exists an invertible matrix P such that B = P−1AP. U C , V C . A transformation A 7! P−1AP is called a similarity transformation or conjugation of the k k k k Definiteness σk(A) = max min Ax 2 = min max Ax 2 matrix A. dim(U)=k x2U dim(U)=n−k+1 x2U kxk=1 kxk=1 Special Matrices and Vectors Definition 13: Definiteness > (12) > y Ax kAxk The vector of all ones, 1 = (1; 1;:::; 1) . = max min = max min A square matrix A is dim(U)=k x2U kxk kyk dim(U)=k x2U kxk y 2 2 2 2 0 1 •positive definite if x Ax > 0 8x =6 0. dim(V )=k x V v1 v1 ··· v1 y Bv2 v2 ··· vnC •positive semi-definite if x Ax ≥ 0 8x. > B C jjj jjj v1 = v ⊗ 1 = B C (1) •σ1(A) = A 2 @ . .. A Similar definitions apply for negative (semi-)definiteness and indefiniteness. > y •σi(A) = σi(A ) = σi(A ) = σi(A¯ ). vn vn ··· vn 2 y y Some sources argue that only Hermitian matrices should count as positive definite, while •σi (A) = λi(AA ) = λi(A A). Standard Basis others note that (real) matrices can be positive definite according to definition 13, although this is ultimately due to the symmetric part of the matrix. •For U and V (square) unitary matrices of appropriate dimension, σi(A) = σi(UAV). n f gn 2 n The standard (euclidean) basis for R is denoted ei i=1, with ei R , and ◦ k k k·k This also connects to why A 2 = σ1, because 2 is unitarily invariant, ( k k j j k k y k k Remark 1: non-symmetric positive definite matrices diag(d) = maxi di , so A 2 = UΣV = Σ 2 = σ1(A). def 1 if i = j 2 [ei] = (2) 0 i =6 j > Matrix Inverse If A 2 Mn(R) is a (not necessarily symmetric) real matrix, then x Ax > 0 for all > non-zero real x if the symmetric part of A, defined as A+ = (A + A )=2, is positive Identities General Information definite. −1 −1 −1 > > • (AB) = B A Symmetric Matrices In fact, x Ax = x A+x 8x because • (A + CBC>)−1 = A−1 − A−1C(B−1 + C>A−1C)−1C>A−1 •Any matrix congruent to a symmetric matrix is itself symmetric, i.e. if S is symmetric, > > > > > 1 > > > x Ax = (x Ax) = x A x = x (A + A )x (9) then so is ASA for any matrix A. 2 • •Any symmetric real matrix can be diagonalized by an orthogonal matrix. • Moore-Penrose Pseudoinverse Miscellaneous facts Induced norms 1=2 Definition 17 (Induced Norm). A matrix norm jj|·|jj is said to be induced by the vector Decompositions • kAvk = (v>A>Av)1=2 ≤ A>A (v>v)1=2. 2 2 norm k·k if Symmetric Decomposition • j det(A)j = σ1σ2 ··· σn, where σi is the i-th singular value of A. jjjMjjj = max kMxk kxk=1 (19) A square matrix A can always be written as the sum of a symmetric matrix A+ and an • All eigenvalues of a matrix are smaller in magnitude than the largest singular value, jj|·|jj antisymmetric matrix A−, such that A = A+ + A−. j j j j ≤ k k For an induced norm we have: i.e., σ1(A) > λi(A) . In particular, λ1(A) = ρ(A) σ1(A) = A 2. jjj jjj QR Decomposition I = 1 Matrix Norms jjjAxjjj ≤ jjjAjjjkxk for all matrices A and vectors x A decomposition of a matrix A into the product of an orthogonal matrix Q and an upper jjj jjj ≤ jjj jjjjjj jjj Properties AB A B for all A; B triangular matrix R such that A = QR. For a weighted norm with W a symmetric positive definite matrix, we have jjjαAjjj = jαjjjjAjjj (absolutely homogeneous) p Singular Value Decomposition jjj jjj ≤ jjj jjj jjj jjj A + B A + B (sub-additive) k k def > 1=2 x W = x Wx = W x (20) Rewriting Matrices jjjAjjj ≥ 0 (positive valued) 2 jjjAjjj = 0 ) A = 0 (definiteness) We can rewrite some common expressions using the standard basis e and the single-entry i jjjABjjj ≤ jjjAjjjjjjBjjj (sub-multiplicative) The corresponding induced matrix norm is matrix Jij = e e>: i j Some vector norms, when generalized to matrices, are also matrix norms.
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