In linear algebra, a square matrix (or more generally, a linear transformation from a complex vector space with a sesquilinear norm to itself) A is said to be skew-Hermitian or antihermitian if its conjugate transpose A^* is also its negative. That is, if it satisfies the relation:

A^* = −A

or in component form, if A = (a_i,j):

for all i and j.

Examples

For example, the following matrix is skew-Hermitian:

Properties

• All entries on the main diagonal of a skew-Hermitian matrix have to be pure imaginary, ie. on the imaginary axis. Hence the same is true for the eigenvalues of a skew-Hermitian matrix.
• If A is skew-Hermitian, then iA is Hermitian
• If A, B are skew-Hermitian, then aA + bB is skew-Hermitian for all real scalars a, b.
• All skew-Hermitian matrices are normal.
• If A is skew-Hermitian, then A² is Hermitian.
• If A is skew-Hermitian, then A raised to an odd power is skew-Hermitian.

See also

• skew-symmetric matrix
• Hermitian matrix
• normal matrix
• unitary matrix.