Eigenvalues are the special set of scalars associated with the system of linear equations. It is mostly used in matrix equations. ‘Eigen’ is a German word that means ‘proper’ or ‘characteristic’. Therefore, the term eigenvalue can be termed as characteristic value, characteristic root, proper values or latent roots as well. In simple words, the eigenvalue is a scalar that is used to transform the eigenvector. The basic equation is
Ax = λx
The number or scalar value “λ” is an eigenvalue of A.
In Mathematics, an eigenvector corresponds to the real non zero eigenvalues which point in the direction stretched by the transformation whereas eigenvalue is considered as a factor by which it is stretched. In case, if the eigenvalue is negative, the direction of the transformation is negative.
For every real matrix, there is an eigenvalue. Sometimes it might be complex. The existence of the eigenvalue for the complex matrices is equal to the fundamental theorem of algebra.
Eigenvectors are the vectors (nonzero) that do not change the direction when any linear transformation is applied. It changes by only a scalar factor. In a brief, we can say, if A is a linear transformation from a vector space V and x is a vector in V, which is not a zero vector, then v is an eigenvector of A if A(X) is a scalar multiple of x.
An Eigenspace of vector x consists of a set of all eigenvectors with the equivalent eigenvalue collectively with the zero vector. Though, the zero vector is not an eigenvector.
Let us say A is an “n × n” matrix and λ is an eigenvalue of matrix A, then x, a nonzero vector, is called as eigenvector if it satisfies the given below expression;
Ax = λx
x is an eigenvector of A corresponding to eigenvalue, λ.
Note:
There could be infinitely many Eigenvectors, corresponding to one eigenvalue.
For distinct eigenvalues, the eigenvectors are linearly dependent.
Suppose, A_{n×n} is a square matrix, then [A λI] is called an Eigen or characteristic matrix, which is an indefinite or undefined scalar. Where determinant of Eigen matrix can be written as, A λI and A λI = 0 is the Eigen equation or characteristics equation, where “I” is the identity matrix. The roots of an Eigen matrix are called Eigen roots.
Eigenvalues of a triangular matrix and diagonal matrix are equivalent to the elements on the principal diagonals. But eigenvalues of the scalar matrix are the scalar only.
In this shear mapping, the blue arrow changes direction, whereas the pink arrow does not. Here, the pink arrow is an eigenvector because it does not change direction. Also, the length of this arrow is not changed; its eigenvalue is 1.
Let us have a look at the example given below to learn how to find the eigenvalues of a 2 x 2 matrix.
Find the eigenvalues of the 2 x 2 matrix
Given,
Using the characteristic equation,
Let
be the 2 x 2 identity matrix.
A – λI = 0
λ(4 – λ) – (2)(0) = 0
4λ + λ2 = 0
λ(λ – 4) = 0
λ = 0 or λ – 4 = 0
Thus, λ = 0 or λ = 4
Hence, the two eigenvalues of the given matrix are λ = 0 and λ = 4.
Go through the following problem to find the Eigenvalue of 3 x 3 matrix.
We define the eigenspace of a matrix as the set of all the eigenvectors of the matrix. All the vectors in the eigenspace are linearly independent of each other.
To find the Eigenspace of the matrix we have to follow the following steps:
Step 1: Find all the eigenvalues of the given square matrix.
Step 2: For each eigenvalue find the corresponding eigenvector.
Step 3: Take the set of all the eigenvectors (say A). The resultant set so formed is called the Eigenspace of the following vector.
Eigenvalues and Eigenvectors are used to find diagonal matrices. A diagonal matrix is a matrix which can be written as A = XDX^{1 }where,
D is the matrix which is formed by replacing the 1’s in the identity matrix with eigenvalues.
X is the matrix formed by eigenvectors.
We can understand the concept of a diagonal matrix by taking the following example.
Example: Diagonalize the matrix A =
Solution: We have already solved for the eigenvalues and the eigenvectors of the A =
The eigenvalues of the A are λ = 0, λ = 0, and λ = 8
The eigenvectors of A are
Thus, D = , X =
We can easily find the inverse of X as, X^{1} =
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1. What are eigenvalues and eigenvectors? 
2. How do eigenvalues and eigenvectors relate to matrices? 
3. How can eigenvalues and eigenvectors be calculated? 
4. What are the applications of eigenvalues and eigenvectors? 
5. Can a matrix have multiple eigenvalues and eigenvectors? 

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