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	Index
See also: linear equations, definition of eigenvalues and eigenvectors, The NIPALS Algorithm

Eigenvectors and Eigenvalues
Advanced Discussion

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

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,

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