# ECSE 506: Stochastic Control and Decision Theory

Positive definite matrices

# 1 Definite and basic properties

Definition

A $$n \times n$$ symmetric matrix $$M$$ is called

• positive definite (written as $$M > 0$$) if for all $$x \in \reals^n$$, $$x \neq 0$$, we have $x^\TRANS M x > 0.$

• positive semi definite (written as $$M \ge 0$$) if for all $$x \in \reals^n$$, $$x \neq 0$$, we have $x^\TRANS M x \ge 0.$

## 1.1 Examples

• $$\MATRIX{ x_1 & x_2 } \MATRIX{ 3 & 0 \\ 0 & 2 } \MATRIX{ x_1 \\ x_2 } = 3 x_1^2 + 2 x_2^2$$.

Thus, $$\MATRIX{ 3 & 0 \\ 0 & 2 } > 0$$.

• $$\MATRIX{ x_1 & x_2 } \MATRIX{ 0 & 0 \\ 0 & 2 } \MATRIX{ x_1 \\ x_2 } = 2 x_2^2$$.

Thus, $$\MATRIX{ 0 & 0 \\ 0 & 2 } \ge 0$$.

## 1.2 Remarks on positive definite matrices

1. By making particular choices of $$x$$ in the definition of positive definite matrix, we have that for a positive definite matrix $$M$$,

• $$M_{ii} > 0$$ for all $$i$$
• $$M_{ij} < \sqrt{M_{ii} M_{jj}}$$ for all $$i \neq j$$.

However, satisfying these inequalities is not sufficient for positive definiteness.

2. A symmetric matrix is positive definite (respt. postive semi-definite) if and only if all of its eigenvalues are positive (respt. non-negative).

3. Therefore, a sufficient condition for a symmetric matrix to be positive definite is that all diagonal elements are positive and the matrix is diagonally dominant, i.e., $$M_{ii} > \sum_{j \neq i} | M_{ij}|$$ for all $$i$$.

4. If $$M$$ is symmetric positive definite, then so is $$M^{-1}$$.

5. If $$M$$ is symmetric positive definite, then $$M$$ has a unique symmetric positive definite square root $$R$$ (i.e., $$RR = M$$).

6. If $$M$$ is symmetric positive definite, then $$M$$ has a unique Cholesky factorization $$M = T^\TRANS T$$, where $$T$$ is upper triangular with positive diagonal elements.

7. The set of positive semi-definite matrices forms a convex cone.

8. Positive definiteness introduces a partial order on the convex cone of positive semi-definite matrices. In particular, we say that for two positive semi-definite matrices $$M$$ and $$N$$ of the same dimension, $$M \succeq N$$ if $$M - N$$ is positive semi-definite. For this reason, often $$M \succ 0$$ and $$M \succeq 0$$ is used a short-hand to denote that $$M$$ is positive definite and positive semi-definite.

9. Let $$M$$ is a symmetric square matrix. Let $λ_1(M) \ge λ_2(M) \ge \dots \ge λ_n(M)$ denote the ordered (real) eigenvalues of $$M$$. Then $λ_1(M)I \succeq M \succeq λ_n(M)I.$

10. If $$M \succeq N$$, then $λ_k(M) \ge λ_k(N), \quad k \in \{1, \dots, n\}.$

11. If $$M \succeq N \succ 0$$, then $N^{-1} \succeq M^{-1} \succ 0.$

12. If $$M \succeq N$$ are $$n × n$$ matrices and $$T$$ is a $$m × n$$ matrix, then $T^\TRANS M T \succeq T^\TRANS N T.$

13. If $$M, N$$ are $$n×$$ positive semi-definite matrices, then $\sum_{i=1}^k λ_i(M) λ_{n-i+1}(N) \le \sum_{i=1}^k λ_i(MN) \le \sum_{i=1}^k λ_i(M)λ_i(N), \quad k \in \{1, \dots, n\}.$ Note that this property does not require $$M - N$$ to be positive or negative semi-definite.

14. If $$M \succ 0$$ and $$T$$ are square matrices of the same size, then $TMT + M^{-1} \succeq 2T.$

## 1.3 A useful relationships.

Symmetric block matrices of the form

$C = \MATRIX{ A & X \\ X^\TRANS B }$

often appear in applications. If $$A$$ is non-singular, we can write

$\MATRIX{A & X \\ X^\TRANS B } = \MATRIX{I & 0 \\ X^\TRANS A^{-1} & I} \MATRIX{A & 0 \\ 0 & B - X^\TRANS A^{-1} X } \MATRIX{I & A^{-1} X \\ 0 & I }$ which shows that $$C$$ is congruent to a block diagonal matrix, which is positive definite when its diagonal blocks are postive definite. Therefore, $$C$$ is positive definite if and only if both $$A$$ and $$B - X^\TRANS A^{-1} X$$ are positive definite. The matrix $$B = X^\TRANS A^{-1} X$$ is called the Shur complement of $$A$$ in $$C$$.

## 1.4 Determinant bounds

Fischer’s inequality. Suppose $$A$$ and $$C$$ are positive semidefinite matrix and $M = \MATRIX{A & B \\ B^\TRANS & C}.$ Then $\det(M) \le \det(A) \det(C).$

Recursive application of Fischer’s inequality gives the Hadamard’s inequality for a symmetric positive definite matrix: $\det(A) \le A_{11} A_{22} \cdots A_{nn},$ with equality if and only if $$A$$ is diagonal.

Prop. 1

If $$M \succ N \succ 0$$ are $$n × n$$ matrices and $$T$$ is a $$m × n$$ matrix, then $\sup_{ T \neq 0} \frac{ \| T^\TRANS M T \| }{ \| T^\TRANS N T \|} \le \frac{ \det(M) }{ \det(N) },$ where for any matrix $$M$$, $\| M \| = \sup_{x \neq 0} \frac{ \| M x \|_2 }{ \|x\|_2 }$ is the $$2$$-norm of the matrix.

Prop. 1 is taken from .

The properties of positive definite matrices are stated in any book on the theory of matrices. See for example Marshall et al. (2011).

Historically, a matrix used as a test matrix for testing positive definiteness was the Wilson matrix $W = \MATRIX{5 & 7 & 6 & 5 \\ 7 & 10 & 8 & 7 \\ 6 & 8 & 10 & 9 \\ 5 & 7 & 9 & 10}.$ For a nice overview of Wilson matrix, see this blog post.

Marshall, A.W., Olkin, I., and Arnold, B.C. 2011. Inequalities: Theory of majorization and its applications. Springer New York. DOI: 10.1007/978-0-387-68276-1.

This entry was last updated on 14 Aug 2020