Thus is an eigenvalue of with generalized eigenvector . The vector converges to an eigenvector of the largest eigenvalue. The multiplicity gives the number of linearly independent eigenvectors corresponding to each eigenvalue. Since formula 20 is the eigenvector of highest eigenvalue, formula 26. The linear combinations of the solutions are the eigenvectors associated with the eigenvalue . Doing so produces a simple vector, which is the required eigenvector for this particular eigenvalue. This will quickly converge to the eigenvector of the closest eigenvalue to . Viewed in another way, is an eigenvector of corresponding to the eigenvalue . Then formula 70 is an eigenfunction of formula 71 with eigenvalue 0. The last factor in the polynomial is due to the zero eigenvalue formula 13. In this case the eigenfunction is itself a function of its associated eigenvalue. Multiply each exponentiated eigenvalue by the corresponding undetermined coefficient matrix . Intuitively, we glob together the Jordan block invariant subspaces corresponding to the same eigenvalue. The resulting equation is known as eigenvalue equation. There will be linearly independent solutions to each eigenvalue equation.