Jacobi Eigenvalue Algorithm

In numerical linear algebra, the Jacobi eigenvalue algorithm is an iterative method for calculating the eigenvalues and eigenvectors of a real symmetric matrix, a process known as diagonalization. It is named after Carl Gustav Jacob Jacobi, who first proposed the method in 1846, though it only became widely used in the 1950s with the advent of computers. The algorithm is inherently a dense matrix method, drawing little or no advantage from being applied to a sparse matrix and destroying sparseness by creating fill-in, and it does not preserve structures such as a banded matrix on which it operates.