You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.

Eigenvectors and Eigenvalues
Advanced Discussion

The following section gives some hints on how eigenvectors can be calculated. In order to solve the fundamental equation

Ae =le

for its eigenvectors e and eigenvalues l, we have to rearrange this equation (I is the identity matrix):

Ae =le

Ae -le = o

(A -lI )e = o

Note that from the last equation we cannot conclude that any of the product terms are zero. However, if we look at the determinants of this equation,

 |A -lI||e| = |o|,

we see that a non-trivial solution is that  |A - lI|  and/or  |e|  have to be zero. So our initial condition, Ae =le, is met when the equations above are fulfilled. The case that  |e| = 0 is the less interesting one, since this is only true if the vector e equals the zero vector o. So, for further considerations one has to look at  |A -lI| = 0. In fact, this equation is so important that it has been given a special name:
Characteristic Determinant
Characteristic Function
For a given matrix A,  |A -lI| denotes its characteristic determinant in the unknown l. The polynomial function c(t) := |A - lI| is called the characteristic function of A. This implies that the determinant is expanded.


Example: Characteristic Determinant


Finally, eigenvectors and eigenvalues are defined as a solution of the characteristic function:

Eigenvalue, Eigenvector For a given matrix A and its characteristic function c(t) = |A -lI|, the roots of the characteristic equation c(t) = 0 are called eigenvalues (or characteristical roots) l1, l2, ..., lk. They meet the criterion Ae = ljej for all j in [1, k] for certain vectors ej. Those vectors ej, each of them corresponding with an eigenvalue lj, are called eigenvectors (or characteristic vectors).


Last Update: 2004-Jul-03