About Matrix Factorization Functions
Matrix factorization is an essential tool in linear algebra applications. It is often a more expedient method to solve linear systems problems, since it is often not optimal to explicitly invert a matrix or calculate a determinant. In applied statistics, matrix factorization can also be used to present structure within data.
• Cholesky—
Cholesky square root of a matrix
• LU—
LU factorization into lower and upper triangular matrices
• QR—
QR factorization into an orthonormal and upper triangular matrix
• svd—Singular values decomposition
Additional Information
• The legacy, lower case, version of the lu, qr, and cholesky functions have been replaced with LU, QR, and Cholesky, respectively. The new upper case functions offer enhanced capability in terms of performance and stability, full pivoting control, complex support, and no limitations on input matrix dimensions.
• The Choelsky, LU, and QR functions use the optimized Intel MKL implementation of Lapack.