Matrix decomposition refers to the process of factorizing a matrix into a product of smaller matrices. By decomposing a large matrix, one can efficiently perform many matrix algorithms.
Common examples of matrix decompositions, each with its advantages and applications, include:
- Spectral decomposition
- LU decomposition
- QR factorization
- Schur decomposition