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- Adjoint in weighted coordinates
- Adjoint operator
- Bilinear form
- Cayley transform of a Hermitian matrix
- Cholesky decomposition
- Cokernel compatibility equations
- Complex matrix
- Conjugate contragredient representation
- Exponential of a Hermitian matrix
- Finite-dimensional spectral theorem
- Hermitian conjugation
- Hermitian part of a matrix
- Hermitian parts of a unitary matrix
- Householder transformation
- Inverse-adjoint representation
- Local-unitary equivalence
- Low-rank approximation
- Matrix with orthonormal columns
- Moore-Penrose inverse
- Orthogonal projection matrix
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- Penrose equations
- Positive definiteness
- Positive definiteness of a Gram matrix
- Power iteration convergence for a diagonalizable matrix
- QR decomposition
- Rayleigh quotient
- Resolution of the identity in an orthonormal basis
- Sesquilinear form
- Singular value decomposition
- Sylvester's criterion
- Tensor-product coefficient matrix
- Transpose of a matrix product
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The conjugate transpose (also known as the Hermitian transpose) of a matrix is an operation that involves two steps: 1. **Transpose**: First, you transpose the matrix, which means you swap its rows and columns. For example, if you have a matrix \( A \) with elements \( a_{ij} \), its transpose \( A^T \) will have elements \( a_{ji} \).