logo

Positive Semidefinite Matrices and Their Real Powers 📂Matrix Algebra

Positive Semidefinite Matrices and Their Real Powers

Definition

For a positive-definite matrix A0A \ge 0 and a real number tRt \in \mathbb{R}, the tt-power of AA is defined as follows. At:=exp(tlogA) A^{t} := \exp \left( t \log A \right) Here, exp\exp and log\log are respectively the matrix exponential and matrix logarithm.

Explanation

Generally, the power of a matrix AtA^{t} is defined as taking the product of the matrix tt times for a natural number tNt \in \mathbb{N}, and if AA is an invertible matrix, there exists an inverse matrix A1A^{-1} allowing it to be generalized for an integer tZt \in \mathbb{Z}.

Going a step further, if AA is a positive-definite matrix, its matrix logarithm logA\log A exists and since all eigenvalues are positive, AtA^{t} can be naturally generalized for a real number tt. The relationship between all eigenvalues being positive and the existence of AtA^{t} can be readily accepted during the proof of the following theorem.

Theorem

For a positive-definite matrix AA and a real number tt, AtA^{t} exists uniquely.

Proof

Let’s denote the space of Hermitian matrices and the convex set of positive-definite matrices as Hn\mathbb{H}_{n} and Pn\mathbb{P}_{n}, respectively. Note that we must be cautious not to arbitrarily use operations like etE=eteEe^{t E} = e^{t} e^{E} during the proof since tt is assumed to be real.

Lemma 1: Unitary Diagonalization of Matrix Exponential

Spectral Theory: It is equivalent for AA to be a Hermitian matrix and to be unitarily diagonalizable: A=A    A=QΛQ A = A^{\ast} \iff A = Q \Lambda Q^{\ast}

A Hermitian matrix XHnX \in \mathbb{H}_{n} can be diagonalized as X=QDQX = Q D Q^{\ast} according to Spectral Theory. Here, QQ is a unitary matrix, and DD is a diagonal matrix consisting of the eigenvalues of AA. Thus, the following holds: expX=eX=k=01k!Xk=k=01k!QDkQ=Q[k=01k!Dk]Q=QeDQ \begin{align*} \exp X =& e^{X} \\ =& \sum_{k=0}^{\infty} \frac{1}{k!} X^{k} \\ =& \sum_{k=0}^{\infty} \frac{1}{k!} Q D^{k} Q^{\ast} \\ =& Q \left[ \sum_{k=0}^{\infty} \frac{1}{k!} D^{k} \right] Q^{\ast} \\ =& Q e^{D} Q^{\ast} \end{align*}

Lemma 2: Eigenvalues of matrix and Matrix Exponential

If we denote the eigenvalues of X=QDQX = Q D Q^{\ast} as d1,,dnd_{1} , \cdots , d_{n}, that is, if we set expX=QeDQ\exp X = Q e^{D} Q^{\ast} to be D=diag(d1,,dn)D = \diag \left( d_{1} , \cdots , d_{n} \right), the following holds: eX=Qk=01k![d1000000dn]kQ=Q[kd1k/k!000000kdnk/k!]Q=Q[ed1000000edn]Q \begin{align*} e^{X} =& Q \sum_{k=0}^{\infty} {\frac{ 1 }{ k! }} \begin{bmatrix} d_{1} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & d_{n} \end{bmatrix}^{k} Q^{\ast} \\ =& Q \begin{bmatrix} \sum_{k} d_{1}^{k} / k! & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & \sum_{k} d_{n}^{k} / k! \end{bmatrix} Q^{\ast} \\ =& Q \begin{bmatrix} e^{d_{1}} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & e^{d_{n}} \end{bmatrix} Q^{\ast} \end{align*} The following necessary and sufficient condition can be derived in the same manner: X=Q[d1000000dn]Q    eX=Q[ed1000000edn]Q X = Q \begin{bmatrix} d_{1} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & d_{n} \end{bmatrix} Q^{\ast} \iff e^{X} = Q \begin{bmatrix} e^{d_{1}} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & e^{d_{n}} \end{bmatrix} Q^{\ast}

Concrete Form

The matrix logarithm log:PnHn\log : \mathbb{P}_{n} \to \mathbb{H}_{n} of the positive-definite matrix AA, which is logA\log A, is a Hermitian matrix and thus can be unitarily diagonalized in the same way: tlogA=tUEU=U[tE]U t \cdot \log A = t \cdot U E U^{\ast} = U \left[ t E \right] U^{\ast} If we consider λ1,,λn\lambda_{1} , \cdots , \lambda_{n} to be the eigenvalues of AA, where EE is diag(logλ1,,logλn)\diag \left( \log \lambda_{1}, \cdots, \log \lambda_{n} \right), which consists of the logarithms of each eigenvalue arranged in a diagonal matrix, we can observe the concrete form of AtA^{t}. At=exp(tlogA)=UetEULemma 1=U[etlogλ1000000etlogλn]ULemma 2=U[λ1t000000λnt]U \begin{align*} A^{t} =& \exp \left( t \log A \right) \\ =& U e^{t E} U^{\ast} & \because \text{Lemma 1} \\ =& U \begin{bmatrix} e^{t \log \lambda_{1}} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & e^{t \log \lambda_{n}} \end{bmatrix} U^{\ast} & \because \text{Lemma 2} \\ =& U \begin{bmatrix} \lambda_{1}^{t} & 0 & 0 \\ 0 & \ddots & 0 \\ 0 & 0 & \lambda_{n}^{t} \end{bmatrix} U^{\ast} \end{align*}


In the proof, the existence of AtA^{t} is confirmed through Spectral Theory and the fact that the matrix logarithm is bijective, and it’s shown that no other AtA^{t} can exist since the diagonal matrix neatly presents as a power of positive numbers. For example, the square root matrix A\sqrt{A} of a positive-definite matrix AA would uniquely exist.