Gets the type that can be used with Blas routines that all types can be implicitly converted to.
Computs Cholesky decomposition of symmetric positive definite matrix 'A'. The factorization has the form: \A = U**T * U, if UPLO = 'U', or \A = L * L**T, if UPLO = 'L' Where U is an upper triangular matrix and L is lower triangular.
Solves a system of linear equations A * X = B with a symmetric matrix A using the Cholesky factorization: \A = U**T * U or \A = L * L**T computed by choleskyDecomp.
Covariance matrix.
Matrix determinant.
Matrix determinant.
Eigenvalues and eigenvectors of symmetric matrix.
Calculates the inverse of a matrix.
Computes the factorization of a real symmetric matrix A using the Bunch-Kaufman diagonal pivoting method. The for of the factorization is: \A = L*D*L**T Where L is product if permutation and unit lower triangular matrices, and D is symmetric and block diagonal with '1 x 1' and '2 x 2' diagonal blocks.
Solves a system of linear equations \A * X = B with symmetric matrix 'A' using the factorization \A = U * D * U**T, or \A = L * D * L**T computed by ldlDecomp.
Computes LU factorization of a general 'M x N' matrix 'A' using partial pivoting with row interchanges. The factorization has the form: \A = P * L * U Where P is a permutation matrix, L is lower triangular with unit diagonal elements (lower trapezoidal if m > n), and U is upper triangular (upper trapezoidal if m < n).
Solves a system of linear equations \A * X = B, or \A**T * X = B with a general 'N x N' matrix 'A' using the LU factorization computed by luDecomp.
Solve systems of linear equations AX = B for X. Computes minimum-norm solution to a linear least squares problem if A is not a square matrix.
General matrix-matrix multiplication. Allocates result to an uninitialized slice using GC.
General matrix-matrix multiplication. Allocates result to an uninitialized slice using GC.
General matrix-matrix multiplication.
Vector-vector multiplication (dot product).
Principal component analysis of raw data.
Computes Moore-Penrose pseudoinverse of matrix.
Computes a QR factorization of matrix 'a'.
Solve the least squares problem: \min ||A * X - B|| Using the QR factorization: \A = Q * R computed by qrDecomp.
Computes the singular value decomposition.
Eigenvalues and eigenvectors POD.
Consist LDL factorization;
LUResult consist lu factorization.
Principal component analises result.
Lubeck - Linear Algebra