# Linear Algebra Nodes (G Dataflow)

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 nth power of a matrix.
Computes the rank of a matrix.
Computes the square root of a matrix.
Computes the outer product of two vectors.
Solves the polynomial eigenvalue problem.
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 Ax = 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.