Last Modified: December 4, 2016

Performs the multiplication of two input matrices or an input matrix and an input vector.

Transforms the eigenvectors of a balanced matrix to those of the original matrix.

Performs Cholesky factorization on a symmetric or Hermitian positive definite matrix.

Performs Cholesky factorization on the rank-1 updated Cholesky matrix. The node performs Cholesky factorization directly on the known factored matrix instead of the updated matrix.

Generates a real matrix from a specified set of eigenvalues.

Generates a matrix of a specific type.

Computes the determinant of a matrix.

Computes the dot product of two vectors.

Finds the eigenvalues and right eigenvectors of a square matrix.

Computes the generalized right eigenvalues and eigenvectors of a pair of square matrices.

Computes the generalized singular value decomposition (GSVD) of a matrix pair.

Performs the Hessenberg decomposition of a matrix.

Finds the inverse of an input matrix, if the inverse exists.

Calculates the Kronecker product of two input matrices.

Performs the LU factorization of a matrix.

Solves the Lyapunov matrix equation.

Balances a general matrix to improve the accuracy of computed eigenvalues and eigenvectors.

Computes the characteristic polynomial of a matrix.

Computes the condition number of a matrix.

Computes the exponential of a square matrix by using the Pade Approximation method.

Computes the natural logarithm of a square matrix.

Computes the norm of a matrix.

Computes the *n*th power of a matrix.

Computes the rank of a matrix.

Computes the square root of a matrix.

Computes the outer product of two vectors.

Finds the pseudoinverse matrix of an input matrix by using singular value decomposition.

Performs the QR decomposition of a matrix with the option of column pivoting.

Performs the QZ decomposition of a pair of square matrices.

Performs the Schur decomposition of a square matrix.

Solves a linear system *A**x* = *y*.

Computes the angle between column spaces of two matrices.

Computes the singular value decomposition (SVD) of an *m* x *n* matrix.

Solves the Sylvester matrix equation.

Determines whether a matrix is of a specific type.

Finds the trace of a matrix.

Transposes a matrix.

Computes the norm of a vector.