Eigenvectors are a special set of vectors associated with a linear system of equations (i.e., a matrix equation) that are sometimes also known as characteristic vectors, proper vectors, or latent vectors (Marcus and Minc 1988, p. 144).
The determination of the eigenvectors and eigenvalues of a system is extremely important in physics and engineering, where it is equivalent to matrix diagonalization and arises in such common applications as stability analysis, the physics of rotating bodies, and small oscillations of vibrating systems, to name only a few. Each eigenvector is paired with a corresponding socalled eigenvalue. Mathematically, two different kinds of eigenvectors need to be distinguished: left eigenvectors and right eigenvectors. However, for many problems in physics and engineering, it is sufficient to consider only right eigenvectors. The term "eigenvector" used without qualification in such applications can therefore be understood to refer to a right eigenvector.
The decomposition of a square matrix into eigenvalues and eigenvectors is known in this work as eigen decomposition, and the fact that this decomposition is always possible as long as the matrix consisting of the eigenvectors of is square is known as the eigen decomposition theorem.
Define a right eigenvector as a column vector satisfying
(1)

where is a matrix, so
(2)

which means the right eigenvalues must have zero determinant, i.e.,
(3)

Similarly, define a left eigenvector as a row vector satisfying
(4)

Taking the transpose of each side gives
(5)

which can be rewritten as
(6)

Rearrange again to obtain
(7)

which means
(8)

Rewriting gives
(9)
 
(10)
 
(11)

where the last step follows from the identity
(12)

Equating equations (◇) and (11), which are both equal to 0 for arbitrary and , therefore requires that , i.e., left and right eigenvalues are equivalent, a statement that is not true for eigenvectors.
Let be a matrix formed by the columns of the right eigenvectors and be a matrix formed by the rows of the left eigenvectors. Let
(13)

Then
(14)
 
(15)

and
(16)
 
(17)

so
(18)

But this equation is of the form
(19)

where is a diagonal matrix, so it must be true that is also diagonal. In particular, if is a symmetric matrix, then the left and right eigenvectors are simply each other's transpose, and if is a selfadjoint matrix (i.e., it is Hermitian), then the left and right eigenvectors are adjoint matrices.
Eigenvectors may not be equal to the zero vector. A nonzero scalar multiple of an eigenvector is equivalent to the original eigenvector. Hence, without loss of generality, eigenvectors are often normalized to unit length.
While an matrix always has eigenvalues, some or all of which may be degenerate, such a matrix may have between 0 and linearly independent eigenvectors. For example, the matrix has only the single eigenvector .
Eigenvectors may be computed in the Wolfram Language using Eigenvectors[matrix]. This command always returns a list of length , so any eigenvectors that are not linearly independent are returned as zero vectors. Eigenvectors and eigenvalues can be returned together using the command Eigensystem[matrix].
Given a matrix with eigenvectors , , and and corresponding eigenvalues , , and , then an arbitrary vector can be written
(20)

Applying the matrix ,
(21)
 
(22)

so
(23)

If , and , it therefore follows that
(24)

so repeated application of the matrix to an arbitrary vector amazingly results in a vector proportional to the eigenvector with largest eigenvalue.