Sylvester's criterion states that a Hermitian matrix M is positive-definite if and only if all the following matrices have a positive determinant:
- the upper left 1-by-1 corner of ,
- the upper left 2-by-2 corner of ,
- the upper left 3-by-3 corner of ,
In other words, all of the leading principal minors must be positive.
Positive Definite or Semidefinite Matrix: A symmetric matrix whose eigenvalues are positive (λ>0) is called positive definite, and when the eigenvalues are just nonnegative (λ≥0), is said to be positive semidefinite.
Theorem I: A real-symmetric matrix has nonnegative eigenvalues if and only if can be factored as , and all eigenvalues are positive if and only if is nonsingular.
Forward implication: If A ∈ Rnxn is symmetric, then, by the Spectral theorem, there is an orthogonal matrix P such that A = PDPT , where D = diag (λ1, λ2, . . . , λn) is real diagonal matrix with entries - eigenvalues of A and P is such that its columns are the eigenvectors of A. If λi ≥ 0 for each i, then D1/2 exists, so A = PDPT = PD1/2D1/2PT = BTB for B = D1/2PT, and λi > 0 for each i if and only if B is nonsingular.
Reverse implication: Conversely, if A can be factored as A = BTB, then all eigenvalues of A are nonnegative because for any eigenpair (λ, x):
Theorem II (The Cholesky decomposition): The symmetric matrix A possesses positive pivots if and only if A can be uniquely factored as A = RTR, where R is an upper-triangular matrix with positive diagonal entries. This is known as the Cholesky decomposition of A, and R is called the Cholesky factor of A.
Forward implication: If A possesses positive pivots (therefore A possesses an LU factorization: A=L.U' ), then, it has an LDU factorization A = LDU=LDLT in which D = diag (u11, u22, . . . , unn) is the diagonal matrix containing the pivots uii > 0.
By a uniqueness property of the LDU decomposition, the symmetry of A yields: U=LT, consequently A=LDU=LDLT. Setting R = D1/2LT where D1/2 = diag() yields the desired factorization, because A = LD1/2D1/2LT = RTR, and R is upper triangular with positive diagonal entries.
Reverse implication: Conversely, if A = RRT , where R is lower triangular with a positive diagonal, then factoring the diagonal entries out of R is as follows:
R = LD, where L is lower triangular with a unit diagonal and D is the diagonal matrix whose diagonal entries are the rii ’s. Consequently, A = LD2LT is the LDU factorization for A, and thus the pivots must be positive because they are the diagonal entries in D2.
Theorem III: Let Ak be the k × k leading principal submatrix of An×n. If A has an LU factorization A = LU, then det(Ak) = u11u22 · · · ukk, and the k-th pivot is ukk =det(A1) = a11 for k = 1, ukk=det(Ak)/det(Ak−1) for k = 2, 3, . . . , n.
Combining Theorem II with Theorem III yields:
Statement I: If the symmetric matrix A can be factored as A=RTR where R is an upper-triangular matrix with positive diagonal entries, then all the pivots of A are positive (by Theorem II), therefore all the leading principal minors of A are positive (by Theorem III).
Statement II: If the nonsingular symmetric matrix A can be factored as , then the QR decomposition (closely related to Gram-Schmidt process) of B (B=QR) yields: , where Q is orthogonal matrix and R is upper triangular matrix.
Namely Statement II requires the non-singularity of the symmetric matrix A.
Combining Theorem I with Statement I and Statement II yields:
Statement III: If the real-symmetric matrix A is positive definite then A possess factorization of the form A=BTB, where B is nonsingular (Theorem I), the expression A=BTB implies that A possess factorization of the form A=RTR where R is an upper-triangular matrix with positive diagonal entries (Statement II), therefore all the leading principal minors of A are positive (Statement I).
In other words, Statement III states:
Sylvester's Criterion: The real-symmetric matrix A is positive definite if and only if all the leading principal minors of A are positive.
The sufficiency and necessity conditions automatically hold because they were proven for each of the above theorems.
- Carl D. Meyer, Matrix Analysis and Applied Linear Algebra. See chapter 7.6 Positive Definite Matrices, page 566
- Carl D. Meyer, Matrix Analysis and Applied Linear Algebra. See chapter 7.6 Positive Definite Matrices, page 558
- Carl D. Meyer, Matrix Analysis and Applied Linear Algebra. See chapter 3.10 The LU Factorization, Example 3.10.7, page 154
- Carl D. Meyer, Matrix Analysis and Applied Linear Algebra. See chapter 6.1 Determinants, Exercise 6.1.16, page 474
- Gilbert, George T. (1991), "Positive definite matrices and Sylvester's criterion", The American Mathematical Monthly (Mathematical Association of America) 98 (1): 44–46, doi:10.2307/2324036, ISSN 0002-9890, JSTOR 2324036.
- Horn, Roger A.; Johnson, Charles R. (1985), Matrix Analysis, Cambridge University Press, ISBN 978-0-521-38632-6. See Theorem 7.2.5.
- Carl D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, ISBN 0-89871-454-0.