CHARACTERIZATIONS. is pd/psd. Possible for all pd/psd matrices! Generating a pd/psd matrix: Choose any B Mn, then

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1 LECTURE 6: POSITIVE DEFINITE MATRICES Definition: A Hermitian matrix A Mn is positive definite (pd) if x Ax > 0 x C n,x 0 A is positive semidefinite (psd) if x Ax 0. Definition: A Mn is negative (semi)definite if A is pd (or psd). If neither holds: A Mn is indefinite. Generating a pd/psd matrix: Choose any B Mn, then A = B B is pd/psd. Possible for all pd/psd matrices! KTH Signal Processing 1 Magnus Jansson/Bhavani Shankar/Emil Björnson POSITIVE DEFINITE CONE Property: Positive linear combination of pd matrices is pd. Conclusion: Set of pd matrices is a positive cone in the vector space. Example: A = A T M2(R). Pd iff a11 > 0, a22 > 0 and a12 2 <a11a22 PROPERTIES OF POSITIVE DEFINITE MATRICES As x Ax > 0 x 0,wehave: Full rank of pd matrices. Any principal sub-matrix of a pd matrix is pd. Diagonal elements of a pd matrix are positive. If A Mn is pd and C Mn,m, then C AC is psd and rank(c AC) =rank(c) C AC is pd if and only if rank(c) =m n. KTH Signal Processing 3 Magnus Jansson/Bhavani Shankar/Emil Björnson KTH Signal Processing 2 Magnus Jansson/Bhavani Shankar/Emil Björnson CHARACTERIZATIONS How to check if a given matrix is pd / psd? Based on Eigenvalues: A Hermitian matrix A Mn is pd if and only if λi(a) > 0 for all i. It is psd iff λi(a) 0. Based on Determinants: Let Ai Mi denote the leading principal submatrix of a matrix A Mn. IfA Mn is Hermitian, then A is pd iff det(ai) > 0 for all i =1,...,n. Note: We may permute rows and columns before applying the result. KTH Signal Processing 4 Magnus Jansson/Bhavani Shankar/Emil Björnson

2 MATRIX ROOTS Assume: A Mn is psd and k is a positive integer. Theorem: There exists a unique psd matrix B such that B k = A. It also holds that BA = AB and B = p(a) for some polynomial p(t). rank(b) =rank(a) B is real if A is real. Example: If k =2, then B is the unique square root of A. KTH Signal Processing 5 Magnus Jansson/Bhavani Shankar/Emil Björnson CONGRUENCE AND DIAGONALIZATION Recall (Similarity): A, B Mn are simultaneously diagonalizable if it exists nonsingular S Mn such that S 1 AS and S 1 BS are diagonal. Implication: AB = BA. Can something less strict exist? Theorem: Suppose A, B Mn are Hermitian and there exists a linear combination of A and B which is pd. Then there is a nonsingular C Mn such that C AC and C BC are diagonal. Important: C AC or C BC need not be the eigenvalue decomposition. KTH Signal Processing 7 Magnus Jansson/Bhavani Shankar/Emil Björnson CHOLESKY FACTORIZATION Corollary: A matrix A Mn is pd iff there exists a lower triangular matrix L Mn with positive diagonal elements such that A = LL Properties: L is called the Cholesky factor If A is real then L can be taken to be real. Enables solving a linear system of equations by back substitution. KTH Signal Processing 6 Magnus Jansson/Bhavani Shankar/Emil Björnson APPLICATION: CONCAVITY OF log det Definition: A function is strictly concave if f(αa +(1 α)b) αf(a)+(1 α)f(b) for α (0, 1) with equality iff A = B. Theorem: The function f(a) =log det(a) is a strictly concave function on the convex set of pd matrices in Mn. Proof exploits that there exist a nonsingular C Mn such that C AC and C BC are diagonal. KTH Signal Processing 8 Magnus Jansson/Bhavani Shankar/Emil Björnson

3 FURTHER APPLICATIONS OF log det Theorem: Let X Mn be pd. Then f(x) = tr(x) log det(x) n, with equality iff X = I. Example: Typical ML criterion to be minimized: V (θ) = log det(r 1 (θ) ˆR) + tr(r 1 (θ) ˆR) = log det( ˆR 1/2 R 1 (θ) ˆR 1/2 ) + tr( ˆR 1/2 R 1 (θ) ˆR 1/2 ) If R(θ) is any pd matrix, this is a convex problem solved by R = ˆR (if ˆR = ˆR1/2 ˆR1/2 is pd). KTH Signal Processing 9 Magnus Jansson/Bhavani Shankar/Emil Björnson PRODUCTS A, B are pd matrices, then AB is pd if and only if they commute. Can we say something more about AB? Theorem: Let A Mn be pd and B Mn be Hermitian. Then 1. AB is diagonalizable. 2. AB has the same number of positive, negative and zero eigenvalues as B. KTH Signal Processing 11 Magnus Jansson/Bhavani Shankar/Emil Björnson FURTHER APPLICATIONS OF log det (CONT D) Theorem: If A =[aij] Mn is pd, then det(a) n aii i=1 with equality iff A is diagonal. Example: Typical Capacity expression for Multiple Input Multiple Output (MIMO) systems to be maximized: C(H) = max log det(i + Q:tr(Q) p HQH ). Convex problem solved by Q that diagonalizes HQH (since det(i + HQH ) n i=1 (1+[HQH ]ii)). KTH Signal Processing 10 Magnus Jansson/Bhavani Shankar/Emil Björnson THE SCHUR PRODUCT THEOREM Definition: The Schur-Hadamard product of two matrices A, B Mm,n is A B =[aijbij] Mm,n Also called elementwise multiplication. Theorem: Let A and B be psd. A B is psd. If A is pd and all diagonal elements of B are positive,then A B is pd. If A and B are pd,then A B is pd. KTH Signal Processing 12 Magnus Jansson/Bhavani Shankar/Emil Björnson

4 POSITIVE SEMIDEFINITE ORDERING Observe: Hermitian matrices generalizes real numbers. Observe: Positive definite matrix generalizes positive real numbers. How to order the matrices? Definition: We write A B if A B is psd, A>Bif A B is pd. This defines a partial ordering of Hermitian matrices. Theorem: If A, B are pd, then 1. A B B 1 A 1 2. If A B, then det(a) det(b) and tr(a) tr(b) 3. If A B, then λk(a) λk(b) for all k (ordered eigenvalues) KTH Signal Processing 13 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD: SINGULAR VALUE DECOMPOSITION Theorem: Any A Mm,n can be decomposed as A = V ΣW V Mm: Unitary with columns being eigenvectors of AA. W Mn: Unitary with columns being eigenvectors of A A. Σ=[σij] Mm,n has σij =0, i j Suppose rank(a) =k and q =min{m, n}, then σ11 σkk >σk+1,k+1 = = σqq =0 σii σi square roots of non-zero eigenvalues of AA (or A A) Unique : σi, Non-unique : V,W If A is real then V and W can be taken to be real. KTH Signal Processing 15 Magnus Jansson/Bhavani Shankar/Emil Björnson SCHUR COMPLEMENTS Consider a Hermitian matrix partitioned as [ A B B C ] where A and C are square matrices. Theorem: This matrix is pd iff A>0 and C B A 1 B>0. Definition: C B A 1 B is the Schur complement of A. Useful to rewrite optimization constraints. KTH Signal Processing 14 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD CONT D Observation: SVD relies on eigendecompositions of AA and A A Consequence: Many results for eigenvalues of Hermitian matrices can be converted to results for the singular values. Examples: 1. Perturbations: Small error in A results in small error in singular values Well conditioned for computation. 2. Interlacing: A Mm,n is given and  is obtained by deleting any one column of A. Denote the singular values of A by σi, the singular values of  by ˆσi, and set q = min{m, n}: σ1 ˆσ1 σ2 ˆσ2 ˆσq 1 σq 0 KTH Signal Processing 16 Magnus Jansson/Bhavani Shankar/Emil Björnson

5 INEQUALITIES Example: If A, B Mn and σi() are the singular values, then ( ) Re tr(ab ) n i=1 σi(a)σi(b). with equality iff SVDs are A = V ΣAW and B = V ΣBW. Many more examples: Inequalities by Marshall, Olkin, and Arnold. KTH Signal Processing 17 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD APPLICATIONS: MINIMAL DIFFERENCE Problem: Let A, B Mm,n, calculate min A B F rank(b)=k Answer: Let A = V ΣW be the SVD of A. Choose B = VkΣkW k where Vk is the matrix with the k left singular vectors corresponding to the k largest singular values etc.. If σl are the singular values of A, then, min rank(b)=k A B 2 F = n l=k+1 σ 2 l These results can be generalized to all unitarily invariant norms. KTH Signal Processing 19 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD APPLICATIONS: PERTURBATION Problem: Find smallest (in e.g. Frobenius norm) perturbation E to the nonsingular matrix A Mn such that A + E is singular. Answer: Let A = V ΣW be the SVD of A. Choose E = vnσnw n where σ n is the smallest singular value of A and vn,wn are the corresponding left and right singular vectors, respectively. KTH Signal Processing 18 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD APPLICATIONS: LEAST SQUARES/CURVE FITTING Problem: Let A Mm,n, b C m, and x C n. Solve min Ax b 2 x Answer: Let A = V ΣW be the SVD of A and define Σ = transpose of Σ in which σl > 0 is replaced by 1/σl A = W Σ V (A is called the Moore-Penrose pseudo inverse of A) One solution is x = A b. It is the unique solution if rank(a) =n. If rank(a) <n, it is the solution with minimum (Euclidean) norm. KTH Signal Processing 20 Magnus Jansson/Bhavani Shankar/Emil Björnson

6 SVD APPLICATIONS: PROCRUSTES PROBLEM Problem: Let A, B Mm,n be given. Find a unitary matrix U Mm such that A UB F is minimized. Answer: Let AB = V ΣW be the SVD of AB. Then the minimum is obtained by letting U = VW. KTH Signal Processing 21 Magnus Jansson/Bhavani Shankar/Emil Björnson THE POLAR DECOMPOSITION Theorem: Let A Mm,n with m n. Then A may be factored as A = PU where P Mm is psd (and hence Hermitian), rank(p )=rank(a) U has orthonormal rows (UU = I) Observation: Always unique P =(AA ) 1/2. If A has full rank, then U = P 1 A is unique. KTH Signal Processing 23 Magnus Jansson/Bhavani Shankar/Emil Björnson SVD APPLICATIONS: TOTAL LEAST SQUARES (TLS) Let A, E Mm,n and B,R Mm,k. Find X that solves the linear system of equations (A + E)X = B + R when E and R are as small as possible. More precisely, solve min [E,R] F E,R subject to range(b + R) range(a + E). If[E0,R0] is a solution, then X is a TLS solution if it solves (A + E0)X = B + R0 Solved using SVD; see Matrix Computations by Golub & Van Loan. KTH Signal Processing 22 Magnus Jansson/Bhavani Shankar/Emil Björnson

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