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Conjugate Transpose Matrix 📂Matrix Algebra

Conjugate Transpose Matrix

Definition

Let AA be a complex matrix of size m×nm \times n . Define A\overline{A} as follows, and call it the conjugate matrix of AA.

A:=[a11a12a1na21a22a2nam1am2amn]=[aij] \overline{A} :=\begin{bmatrix} \overline{a_{11}} & \overline{a_{12}} & \cdots & \overline{a_{1n}} \\ \overline{a_{21}} & \overline{a_{22}} & \cdots & \overline{a_{2n}} \\ \vdots & \vdots & \ddots & \vdots \\ \overline{a_{m1}} & \overline{a_{m2}} & \cdots & \overline{a_{mn}} \end{bmatrix} = \left[ \overline{a_{ij}} \right]

Here, a\overline{a} is the conjugate complex number of aa. In other words, a matrix whose elements are the conjugate complex numbers of the elements of another matrix is called a conjugate matrix. Let AA be a complex matrix of size m×nm\times n again. Define AA^{\ast} as follows, and call it the conjugate transpose of AA.

A:=AT=(A)T A^{\ast} := \overline{A^{T}} = \left( \overline{A} \right) ^{T}

Description

In addition to AA^{\ast}, other notations used are AA^{\dagger} and AHA^{H}. AA^{\dagger} is read as [Adagger], and HH of AHA^{H} comes from the Hermitian matrix. In physics, especially in quantum mechanics, AA^{\ast} is sometimes used only in the sense of a conjugate matrix. Thus, it is denoted as A=(A)TA^{\dagger}=(A^{\ast})^{T}. Meanwhile, in numerical linear algebra and elsewhere, AA^{\dagger} is used as the notation for a ‘pseudoinverse’, which acts like an inverse matrix, though it is not actually one. Given the wide use of linear algebra, resolving such notation issues requires one to stay focused and closely follow the subject being studied at the time.

Properties1

Let A,BA,B be any complex matrix, and let kCk\in \mathbb{C}.

(a) A=A\overline{\overline{A}}=A

(b) (AB)=A B\overline{(AB)} = \overline{A}\ \overline{B}

(c) (A)=A(A^{\ast})^{\ast}=A

(d) (A±B)=A±B\left( A \pm B\right)^{\ast} = A^{\ast} \pm B^{\ast}

(e) (kA)=kA(kA)^{\ast}=\overline{k}A^{\ast}

(f) (AB)=BA\left( AB \right)^{\ast} = B^{\ast} A^{\ast}

Proof

(a) (b)

Obvious from the properties of conjugate complex numbers and the definition of matrix multiplication.

(c) (d) (e)

Proven by (a), properties of the transpose matrix (AT)T=A \left( A^{T} \right) ^{T} = A , and the definition of matrix addition.

(f)

Proven by (b), properties of the transpose matrix (AB)T=BTAT\left( AB \right) ^{T} = B^{T} A^{T}.


  1. Howard Anton, Elementary Linear Algebra: Aplications Version (12th Edition, 2019), p437 ↩︎