Skew-Hermitian matrix

where ${\displaystyle A^{\textsf {H}}}$ denotes the conjugate transpose of the matrix ${\displaystyle A}$. In component form, this means that

for all indices ${\displaystyle i}$ and ${\displaystyle j}$, where ${\displaystyle a_{ij}}$ is the element in the ${\displaystyle i}$-th row and ${\displaystyle j}$-th column of ${\displaystyle A}$, and the overline denotes complex conjugation.

Skew-Hermitian matrices can be understood as the complex versions of real skew-symmetric matrices, or as the matrix analogue of the purely imaginary numbers.^[2] The set of all skew-Hermitian ${\displaystyle n\times n}$ matrices forms the ${\displaystyle u($n)} Lie algebra, which corresponds to the Lie group U(n). The concept can be generalized to include linear transformations of any complex vector space with a sesquilinear norm.

Note that the adjoint of an operator depends on the scalar product considered on the ${\displaystyle n}$ dimensional complex or real space ${\displaystyle K^{n}}$. If ${\displaystyle (\cdot \mid \cdot )}$ denotes the scalar product on ${\displaystyle K^{n}}$, then saying ${\displaystyle A}$ is skew-adjoint means that for all ${\displaystyle \mathbf {u} ,\mathbf {v} \in K^{n}}$ one has ${\displaystyle (A\mathbf {u} \mid \mathbf {v} )=-(\mathbf {u} \mid A\mathbf {v} )}$.

Imaginary numbers can be thought of as skew-adjoint (since they are like ${\displaystyle 1\times 1}$ matrices), whereas real numbers correspond to self-adjoint operators.

Example[edit]

For example, the following matrix is skew-Hermitian

Properties[edit]

• The eigenvalues of a skew-Hermitian matrix are all purely imaginary (and possibly zero). Furthermore, skew-Hermitian matrices are normal. Hence they are diagonalizable and their eigenvectors for distinct eigenvalues must be orthogonal.^[3]
• All entries on the main diagonal of a skew-Hermitian matrix have to be pure imaginary; i.e., on the imaginary axis (the number zero is also considered purely imaginary).^[4]
• If${\displaystyle A}$ and ${\displaystyle B}$ are skew-Hermitian, then ${\displaystyle aA+bB}$ is skew-Hermitian for all real scalars ${\displaystyle a}$ and ${\displaystyle b}$.^[5]
• ${\displaystyle A}$ is skew-Hermitian if and only if ${\displaystyle iA}$ (or equivalently, ${\displaystyle -iA}$) is Hermitian.^[5]
• ${\displaystyle A}$ is skew-Hermitian if and only if the real part ${\displaystyle \Re {($A)}} isskew-symmetric and the imaginary part ${\displaystyle \Im {($A)}} issymmetric.
• If${\displaystyle A}$ is skew-Hermitian, then ${\displaystyle A^{k}}$ is Hermitian if ${\displaystyle k}$ is an even integer and skew-Hermitian if ${\displaystyle k}$ is an odd integer.
• ${\displaystyle A}$ is skew-Hermitian if and only if ${\displaystyle \mathbf {x} ^{\mathsf {H}}A\mathbf {y} =-{\overline {\mathbf {y} ^{\mathsf {H}}A\mathbf {x} }}}$ for all vectors ${\displaystyle \mathbf {x} ,\mathbf {y} }$.
• If${\displaystyle A}$ is skew-Hermitian, then the matrix exponential ${\displaystyle e^{A}}$isunitary.
• The space of skew-Hermitian matrices forms the Lie algebra ${\displaystyle u($n)} of the Lie group ${\displaystyle U($n)} .

Decomposition into Hermitian and skew-Hermitian[edit]

• The sum of a square matrix and its conjugate transpose ${\displaystyle \left(A+A^{\mathsf {H}}\right)}$ is Hermitian.
• The difference of a square matrix and its conjugate transpose ${\displaystyle \left(A-A^{\mathsf {H}}\right)}$ is skew-Hermitian. This implies that the commutator of two Hermitian matrices is skew-Hermitian.
• An arbitrary square matrix ${\displaystyle C}$ can be written as the sum of a Hermitian matrix ${\displaystyle A}$ and a skew-Hermitian matrix ${\displaystyle B}$:
  $${\displaystyle C=A+B\quad {\mbox{with}}\quad A={\frac {1}{2}}\left(C+C^{\mathsf {H}}\right)\quad {\mbox{and}}\quad B={\frac {1}{2}}\left(C-C^{\mathsf {H}}\right)}$$
  

See also[edit]

• Bivector (complex)
• Hermitian matrix
• Normal matrix
• Skew-symmetric matrix
• Unitary matrix

Notes[edit]

References[edit]

• Horn, Roger A.; Johnson, Charles R. (1985), Matrix Analysis, Cambridge University Press, ISBN 978-0-521-38632-6.
• Meyer, Carl D. (2000), Matrix Analysis and Applied Linear Algebra, SIAM, ISBN 978-0-89871-454-8.