The eigenvalue problem is a problem of considerable theoretical interest and wide-ranging application. For example, this problem is crucial in solving systems of differential equations, analyzing population growth models, and calculating powers of matrices (in order to define the exponential matrix). Other areas such as physics, sociology, biology, economics and statistics have focused considerable attention on "eigenvalues" and "eigenvectors"-their applications and their computations. Before we give the formal definition, let us introduce these concepts on an example.

**Example.**Consider the matrix

Consider the three column matrices

We have

In other words, we have

Next consider the matrix

*P*for which the columns are

*C*

_{1},

*C*

_{2}, and

*C*

_{3}, i.e.,

We have

*det*(

*P*) = 84. So this matrix is invertible. Easy calculations give

Next we evaluate the matrix

*P*

^{-1}

*AP*. We leave the details to the reader to check that we have

In other words, we have

Using the matrix multiplication, we obtain

which implies that

*A*is similar to a diagonal matrix. In particular, we have

for . Note that it is almost impossible to find

*A*

^{75}directly from the original form of

*A*.

This example is so rich of conclusions that many questions impose themselves in a natural way. For example, given a square matrix

*A*, how do we find column matrices which have similar behaviors as the above ones? In other words, how do we find these column matrices which will help find the invertible matrix

*P*such that

*P*

^{-1}

*AP*is a diagonal matrix?

From now on, we will call column matrices

**vectors**. So the above column matrices

*C*

_{1},

*C*

_{2}, and

*C*

_{3}are now vectors. We have the following definition.

**Definition.**Let

*A*be a square matrix. A non-zero vector

*C*is called an

**eigenvector**of

*A*if and only if there exists a number (real or complex) such that

If such a number exists, it is called an

**eigenvalue**of

*A*. The vector

*C*is called eigenvector associated to the eigenvalue .

**Remark.**The eigenvector

*C*must be non-zero since we have

for any number .

**Example.**Consider the matrix

We have seen that

where

So

*C*

_{1}is an eigenvector of

*A*associated to the eigenvalue 0.

*C*

_{2}is an eigenvector of

*A*associated to the eigenvalue -4 while

*C*

_{3}is an eigenvector of

*A*associated to the eigenvalue 3.

It may be interesting to know whether we found all the eigenvalues of

*A*in the above example. In the next page, we will discuss this question as well as how to find the eigenvalues of a square matrix.