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Linear Algebra Math 221: LINEAR ALGEBRA Chapter 8. Orthogonality §8-3. Positive Definite Matrices Le Chen1 Emory University, 2020 Fall (last updated on 11/10/2020) Creative Commons License (CC BY-NC-SA) 1 Slides are adapted from those by Karen Seyffarth from University of Calgary. Positive Definite Matrices Cholesky factorization – Square Root of a Matrix Definition An n × n matrix A is positive definite if it is symmetric and has positive eigenvalues, i.e., if λ is a eigenvalue of A, then λ > 0. Theorem If A is a positive definite matrix, then det(A) > 0 and A is invertible. Proof. Let λ1; λ2; : : : ; λn denote the (not necessarily distinct) eigenvalues of A. Since A is symmetric, A is orthogonally diagonalizable. In particular, A ∼ D, where D = diag(λ1; λ2; : : : ; λn). Similar matrices have the same determinant, so det(A) = det(D) = λ1λ2 ··· λn: Since A is positive definite, λi > 0 for all i, 1 ≤ i ≤ n; it follows that det(A) > 0, and therefore A is invertible. Positive Definite Matrices Theorem If A is a positive definite matrix, then det(A) > 0 and A is invertible. Proof. Let λ1; λ2; : : : ; λn denote the (not necessarily distinct) eigenvalues of A. Since A is symmetric, A is orthogonally diagonalizable. In particular, A ∼ D, where D = diag(λ1; λ2; : : : ; λn). Similar matrices have the same determinant, so det(A) = det(D) = λ1λ2 ··· λn: Since A is positive definite, λi > 0 for all i, 1 ≤ i ≤ n; it follows that det(A) > 0, and therefore A is invertible. Positive Definite Matrices Definition An n × n matrix A is positive definite if it is symmetric and has positive eigenvalues, i.e., if λ is a eigenvalue of A, then λ > 0. Proof. Let λ1; λ2; : : : ; λn denote the (not necessarily distinct) eigenvalues of A. Since A is symmetric, A is orthogonally diagonalizable. In particular, A ∼ D, where D = diag(λ1; λ2; : : : ; λn). Similar matrices have the same determinant, so det(A) = det(D) = λ1λ2 ··· λn: Since A is positive definite, λi > 0 for all i, 1 ≤ i ≤ n; it follows that det(A) > 0, and therefore A is invertible. Positive Definite Matrices Definition An n × n matrix A is positive definite if it is symmetric and has positive eigenvalues, i.e., if λ is a eigenvalue of A, then λ > 0. Theorem If A is a positive definite matrix, then det(A) > 0 and A is invertible. Positive Definite Matrices Definition An n × n matrix A is positive definite if it is symmetric and has positive eigenvalues, i.e., if λ is a eigenvalue of A, then λ > 0. Theorem If A is a positive definite matrix, then det(A) > 0 and A is invertible. Proof. Let λ1; λ2; : : : ; λn denote the (not necessarily distinct) eigenvalues of A. Since A is symmetric, A is orthogonally diagonalizable. In particular, A ∼ D, where D = diag(λ1; λ2; : : : ; λn). Similar matrices have the same determinant, so det(A) = det(D) = λ1λ2 ··· λn: Since A is positive definite, λi > 0 for all i, 1 ≤ i ≤ n; it follows that det(A) > 0, and therefore A is invertible. Proof. Since A is symmetric, there exists an orthogonal matrix P so that T P AP = diag(λ1; λ2; : : : ; λn) = D; where λ1; λ2; : : : ; λn are the (not necessarily distinct) eigenvalues of A. Let n T ~x 2 R , ~x 6= ~0, and define ~y = P ~x. Then ~xTA~x = ~xT(PDPT)~x = (~xTP)D(PT~x) = (PT~x)TD(PT~x) = ~yTD~y: T Writing ~y = y1 y2 ··· yn , 2 y1 3 6 y2 7 ~xTA~x = y y ··· y diag(λ ; λ ; : : : ; λ ) 6 7 1 2 n 1 2 n 6 . 7 4 . 5 yn 2 2 2 = λ1y1 + λ2y2 + ··· λnyn: Theorem A symmetric matrix A is positive definite if and only if ~xTA~x > 0 for all n ~x 2 R , ~x 6= ~0. Theorem A symmetric matrix A is positive definite if and only if ~xTA~x > 0 for all n ~x 2 R , ~x 6= ~0. Proof. Since A is symmetric, there exists an orthogonal matrix P so that T P AP = diag(λ1; λ2; : : : ; λn) = D; where λ1; λ2; : : : ; λn are the (not necessarily distinct) eigenvalues of A. Let n T ~x 2 R , ~x 6= ~0, and define ~y = P ~x. Then ~xTA~x = ~xT(PDPT)~x = (~xTP)D(PT~x) = (PT~x)TD(PT~x) = ~yTD~y: T Writing ~y = y1 y2 ··· yn , 2 y1 3 6 y2 7 ~xTA~x = y y ··· y diag(λ ; λ ; : : : ; λ ) 6 7 1 2 n 1 2 n 6 . 7 4 . 5 yn 2 2 2 = λ1y1 + λ2y2 + ··· λnyn: T (() Conversely, if ~x A~x > 0 whenever ~x 6= ~0, choose ~x = P~ej, where ~ej is th the j column of In. Since P is invertible, ~x 6= ~0, and thus T T ~y = P ~x = P (P~ej) = ~ej: Thus yj = 1 and yi = 0 when i 6= j, so 2 2 2 λ1y1 + λ2y2 + ··· λnyn = λj; T i.e., λj = ~x A~x > 0. Therefore, A is positive definite. Proof. (continued) n T ()) Suppose A is positive definite, and ~x 2 R , ~x 6= ~0. Since P is T ~ 2 invertible, ~y = P ~x 6= 0, and thus yj 6= 0 for some j, implying yj > 0 for 2 some j. Furthermore, since all eigenvalues of A are positive, λiyi ≥ 0 for all 2 T i; in particular λjyj > 0. Therefore, ~x A~x > 0. Proof. (continued) n T ()) Suppose A is positive definite, and ~x 2 R , ~x 6= ~0. Since P is T ~ 2 invertible, ~y = P ~x 6= 0, and thus yj 6= 0 for some j, implying yj > 0 for 2 some j. Furthermore, since all eigenvalues of A are positive, λiyi ≥ 0 for all 2 T i; in particular λjyj > 0. Therefore, ~x A~x > 0. T (() Conversely, if ~x A~x > 0 whenever ~x 6= ~0, choose ~x = P~ej, where ~ej is th the j column of In. Since P is invertible, ~x 6= ~0, and thus T T ~y = P ~x = P (P~ej) = ~ej: Thus yj = 1 and yi = 0 when i 6= j, so 2 2 2 λ1y1 + λ2y2 + ··· λnyn = λj; T i.e., λj = ~x A~x > 0. Therefore, A is positive definite. Proof. n Let ~x 2 R , ~x 6= ~0. Then ~xTA~x = ~xT(UTU)~x = (~xTUT)(U~x) = (U~x)T(U~x) = jjU~xjj2: Since U is invertible and ~x 6= ~0,U~x 6= ~0, and hence jjU~xjj2 > 0, i.e., T 2 ~x A~x = jjU~xjj > 0. Therefore, A is positive definite. Theorem (Constructing Positive Definite Matrices) Let U be an n × n invertible matrix, and let A = UTU. Then A is positive definite. Theorem (Constructing Positive Definite Matrices) Let U be an n × n invertible matrix, and let A = UTU. Then A is positive definite. Proof. n Let ~x 2 R , ~x 6= ~0. Then ~xTA~x = ~xT(UTU)~x = (~xTUT)(U~x) = (U~x)T(U~x) = jjU~xjj2: Since U is invertible and ~x 6= ~0,U~x 6= ~0, and hence jjU~xjj2 > 0, i.e., T 2 ~x A~x = jjU~xjj > 0. Therefore, A is positive definite. Lemma If A is an n × n positive definite matrix, then each principal submatrix of A is positive definite. Definition (r) Let A = aij be an n × n matrix. For 1 ≤ r ≤ n, A denotes the r × r submatrix in the upper left corner of A, i.e., (r) A = aij ; 1 ≤ i; j ≤ r: (1)A; (2)A;:::; (n)A are called the principal submatrices of A. Definition (r) Let A = aij be an n × n matrix. For 1 ≤ r ≤ n, A denotes the r × r submatrix in the upper left corner of A, i.e., (r) A = aij ; 1 ≤ i; j ≤ r: (1)A; (2)A;:::; (n)A are called the principal submatrices of A. Lemma If A is an n × n positive definite matrix, then each principal submatrix of A is positive definite. Proof. Suppose A is an n × n positive definite matrix. For any integer r, 1 ≤ r ≤ n, write A in block form as (r)AB A = ; CD where B is an r × (n − r) matrix, C is an (n − r) × r matrix, and D is an 2 y1 3 6 y2 7 2 y 3 6 7 1 6 . 7 y 6 . 7 6 2 7 6 7 (n − r) × (n − r) matrix. Let ~y = 6 . 7 6= ~0 and let ~x = 6 yr 7. Then 6 . 7 6 7 4 . 5 6 0 7 6 7 yr 6 . 7 4 . 5 0 ~x 6= ~0, and by the previous theorem, ~xTA~x > 0. Proof. (continued) But 2 y1 3 6 . 7 6 . 7 (r) 6 7 T AB 6 yr 7 T (r) ~x A~x = y1 ··· yr 0 ··· 0 6 7 = ~y A ~y; CD 6 0 7 6 7 6 . 7 4 . 5 0 and therefore ~yT (r)A ~y > 0. Then (r)A is positive definite again by the previous theorem. 4 = 2 × 2T 2 4 12 −163 2 2 0 03 22 6 −83 4 12 37 −435 = 4 6 1 05 40 1 5 5 −16 −43 98 −8 5 3 0 0 3 Theorem Let A be an n × n symmetric matrix. Then the following conditions are equivalent. 1. A is positive definite. 2. det((r)A) > 0 for r = 1; 2;:::; n. 3. A = UTU where U is upper triangular and has positive entries on its main diagonal.
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