Sylvester's criterion

In mathematics, Sylvester’s criterion is a necessary and sufficient criterion to determine whether a Hermitian matrix is positive-definite. It is named after James Joseph Sylvester.

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 $M$ ,
the upper left 2-by-2 corner of $M$ ,
the upper left 3-by-3 corner of $M$ ,
...
$M$ itself.

In other words, all of the leading principal minors must be positive.

Proof

The proof is only for nonsingular Hermitian matrix with coefficients in $\mathbb{R}^{n \times n}$ , therefore only for nonsingular real-symmetric matrices

Positive Definite or Semidefinite Matrix: A symmetric matrix $A$ whose eigenvalues are positive (λ>0) is called positive definite, and when the eigenvalues are just nonnegative (λ≥0), $A$ is said to be positive semidefinite.

Theorem I: A real-symmetric matrix $A$ has nonnegative eigenvalues if and only if $A$ can be factored as $A = B^TB$ , and all eigenvalues are positive if and only if $B$ is nonsingular.^[1]

Proof:

Necessity: If A ∈ R^nxn is symmetric, then, by the Spectral theorem, there is an orthogonal matrix P such that A = PDP^T , where D = diag (λ₁, λ₂, . . . , λ_n) is real diagonal matrix with entries - eigenvalues of A and P is such that it columns are the eigenvectors of A. If λ_i ≥ 0 for each i, then D^1/2 exists, so A = PDP^T = PD^1/2D^1/2P^T = B^TB for B = D^1/2P^T, and λ_i > 0 for each i if and only if B is nonsingular.

Sufficiency: Conversely, if A can be factored as A = B^TB, then all eigenvalues of A are nonnegative because for any eigenpair (λ, x):

λ= ${\frac{x^TAx}{x^Tx}}={\frac{x^TB^TBx}{x^Tx}}={\frac{||Bx||^2}{||x||^2}}$ ≥0.

Theorem II (The Cholesky decomposition): The symmetric matrix A possesses positive pivots if and only if A can be uniquely factored as A = R^TR, 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.^[2]

Proof:

Necessity: If A possesses positive pivots (therefore A possesses an LU factorization: A=L.U' ), then, it has an LDU factorization A = LDU=LDL^T in which D = diag (u₁₁, u₂₂, . . . , u_nn) is the diagonal matrix containing the pivots u_ii > 0.

$\mathbf{A} =LU'= \begin{bmatrix} 1 & 0 & . & 0\\ l_{12} & 1 & . & 0 \\ . & . & . & . \\ l_{1n} & l_{2n} & . & 1 \end{bmatrix}$ x $\begin{bmatrix} u_{11} & u_{12} & . & u_{1n}\\ 0 & u_{22} & . & u_{2n} \\ . & . & . & . \\ 0 & 0 & . & u_{nn} \end{bmatrix} =LDU= \begin{bmatrix} 1 & 0 & . & 0\\ l_{12} & 1 & . & 0 \\ . & . & . & . \\ l_{1n} & l_{2n} & . & 1 \end{bmatrix}$ x $\begin{bmatrix} u_{11} & 0 & . & 0\\ 0 & u_{22} & . & 0 \\ . & . & . & . \\ 0 & 0 & . & u_{nn} \end{bmatrix}$ x $\begin{bmatrix} 1 & u_{12}/u_{11} & . & u_{1n}/u_{11}\\ 0 & 1 & . & u_{2n}/u_{22} \\ . & . & . & . \\ 0 & 0 & . & 1 \end{bmatrix}$

By a uniqueness property of the LDU decomposition, the symmetry of A yields: U=L^T, consequently A=LDU=LDL^T. Setting R = D^1/2L^T where D^1/2 = diag( $\scriptstyle\sqrt{u_{11}},\scriptstyle\sqrt{u_{22}},...,\scriptstyle\sqrt{u_{11}}$ ) yields the desired factorization, because A = LD^1/2D^1/2L^T = R^TR, and R is upper triangular with positive diagonal entries.

Sufficiency: Conversely, if A = RR^T , where R is lower triangular with a positive diagonal, then factoring the diagonal entries out of R is as follows:

$\mathbf{R} =LD= \begin{bmatrix} 1 & 0 & . & 0\\ r_{12}/r_{11} & 1 & . & 0 \\ . & . & . & . \\ r_{1n}/r_{11} & r_{2n}/r_{22} & . & 1 \end{bmatrix}$ x $\begin{bmatrix} r_{11} & 0 & . & 0\\ 0 & r_{22} & . & 0 \\ . & . & . & . \\ 0 & 0 & . & r_{nn} \end{bmatrix}$

R = LD, where L is lower triangular with a unit diagonal and D is the diagonal matrix whose diagonal entries are the r_ii ’s. Consequently, A = LD²L^T is the LDU factorization for A, and thus the pivots must be positive because they are the diagonal entries in D².

Theorem III: Let A_k be the k × k leading principal submatrix of A_n×n. If A has an LU factorization A = LU, then det(A_k) = u₁₁u₂₂ · · · u_kk, and the k-th pivot is u_kk =det(A₁) = a₁₁ for k = 1, u_kk=det(A_k)/det(A_k−1) for k = 2, 3, . . . , n.^[3]

Combining Theorem II with Theorem III yields:

Statement I: If the symmetric matrix A can be factored as A=R^TR 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 $A=B^TB$ , then the QR decomposition (closely related to Gram-Schmidt process) of B (B=QR) yields: $A=B^TB=R^TQ^TQR=R^TR$ , 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=B^TB, where B is nonsingular (Theorem I), the expression A=B^TB implies that A possess factorization of the form A=R^TR 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 holds because they were proven for each of the above theorems.

Notes

^ 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

References

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 .