Preliminaries

Def: A matrix $A \in M_n$ is normal if $AA^∗ = A^∗A$, that is, if $A$ commutes with its conjugate transpose.

Def: A complex matrix $A$ is unitary if $AA^∗ = I$ or $A^∗A = I$, and a real matrix $B$ is orthogonal if $BB^T = I$ or $B^TB = I$.

Image adapted from Meyer’s book

There is no so-called “orthonormal” matrix. There is just an orthogonal matrix whose rows or columns are orthonormal vectors.

Notice that $$ \begin{gathered} U^*U = I \Longleftrightarrow U^{\star}UU^{\star} = IU^{\star} = U^{\star} \ \Longleftrightarrow UU^{\star} = (U^{\star})^{-1}U^{\star} = I \end{gathered} $$ the columns of $U$ are orthonormal if and only if the rows are orthonormal.

So the definition can be summarized as below:

  • Hermitian: $A=A^{\star}$
  • Unitary: $A^{\star}A=AA^{\star}=I$
  • Symmetric: $A = A^{T}$
  • Orthogonal: $A^TA=AA^T=I$

Eigenvalue Decomposition

Singular Value Decomposition

$LU$ Factorization

$QR$ Factorization