Matrix transformations
Matrix decomposition can be used in the following cases:
- as an intermediate step when solving systems of linear equations
- for matrix inversion
- when calculating determinants
- when finding eigenvalues and eigenvectors of a matrix
- when computing analytic functions of matrices
- when using the least squares method
- in the numerical solution of differential equations
Different matrix decomposition types are used depending on the problem.
Function |
Action |
|---|---|
Computes the Cholesky decomposition |
|
Computes the eigenvalues and right eigenvectors of a square matrix |
|
Computes the eigenvalues of a general matrix |
|
LU factorization of a matrix as the product of a lower triangular matrix and an upper triangular matrix |
|
LUP factorization with partial pivoting, which refers to LU decomposition with row permutations only: PA=LU |
|
Compute the qr factorization of a matrix |
|
Singular Value Decomposition |