Eigen Values

It is possible to have multiple names for the set of scalars associated with a linear system of equations (in this case, a matrix equation), such as characteristic roots, characteristic values (Hoffman and Kunze 1971), proper values, or latent roots (Marcus and Minc 1988, p. 144) or latent roots (Hoffman and Kunze 1971). 

This is a very essential step in systems analysis and engineering, where it is comparable to matrix diagonalization and occurs in applications as diverse as stability analysis, the physics of spinning bodies, and the tiny oscillations of vibrating systems, to mention a few. 

There is no similar difference between left and right in the case of eigenvalues; however, there is a distinction between right and left in the case of eigenvectors. 

It is known as eigen decomposition in this work, and it is also known as the eigen decomposition theorem since it states that the breakdown of a square matrix into its eigenvalues and eigenvectors is always feasible as long as the matrix consisting of the eigenvectors of is square. eigenvalues examples

Some of the most essential characteristics of eigenvalues 

• It is true that the eigenvalues of real symmetric and hermitian matrices are true. 
• Real skew symmetric and skew hermitian matrices have eigenvalues that are either pure imaginary or zero, depending on the case. 
• The eigenvalues of unitary and orthogonal matrices have unit modulus | λ | = 1 and are symmetric. 
• If λ1, λ2…….λn are the eigenvalues of A, then kλ1, kλ2…….kλn are eigenvalues of kA
• Assuming that A has eigenvalues λ1, λ2, λ3, and so on, then the eigenvalues of kA have values kλ1, kλ2, kλ3, and so on. 
• If A has eigenvalues λ1, λ2, λ3, λ4, λ5, then A-1 has eigenvalues 1/λ1, 1/λ2,….respectively. 
• Assuming that A has eigenvalues of λ1, λ2, λ3, and so on, then Ak has eigenvalues of λ1, λ2, λ3, and so on, till λnk. 
• Attribute values of A = Attribute Values of AT (Transpose) 
• The sum of Eigenvalues equals the trace of A. (Sum of diagonal elements of A) 
• The product of Eigenvalues equals |A|. 
• In A, the maximum number of different eigenvalues is equal to the size of A. 
• If A and B are two matrices of the same order, then the Eigenvalues of AB are equal to the Eigenvalues of BA. 

Eigenspaces, geometric multiplicity, and the eigen basis for matrices

Assuming that an eigenvalue A is given, define the set E as consisting of all vectors v given that value A. 

Due to the fact that the eigenspace E is a linear subspace, it is closed when added together. In other words, if two vectors u and v are members of the set E, denoted by the symbols u, v ∈ E, then (u + v) ∈ E, or equivalently A(u + v) = λ (u + v). The distributive property of matrix multiplication may be used to determine whether or not this is true. Furthermore, since E is a linear subspace, it is closed when scalar multiplication is performed on it. As a result, for every complex number v greater than E, (v) greater than E, or equivalently a(v) = λ(av). As an example, the fact that multiplication of complex matrices by complex numbers is commutative may be used to verify this claim. They are also linked as long as u + v and av do not equal zero and are not eigenvectors of A. 

Method to find eigenvectors and eigenvalues of any square matrix A
We know that,

AX = λX

=> AX – λX = 0

             => (A – λI) X = 0

Above condition will be true only if (A – λI) is singular. That means,

|A – λI| = 0 

The eigenvalues of the matrix A are represented by the roots of the characteristic equation. 

Now, in order to identify the eigenvectors, we simply enter each eigen value into (1) and solve the problem using Gaussian elimination. , that is, convert the augmented matrix (A – λI) = 0 to Create a linear system of equations in row echelon form and solve the linear system of equations that results. 

Eigenvalues have certain characteristics. 

• Eigenvectors with Distinct eigenvalues are Linearly Independent
• Singular Matrices have Zero eigenvalues
• If A is a square matrix, then λ = 0 is not an eigenvalue of A
• For a scalar multiple of a matrix: Assume A is a square matrix, and e is one of the eigenvalues of Aλ. Then an is an eigen value of the function aλ.

Conclusion

This is a very essential step in systems analysis and engineering, where it is comparable to matrix diagonalization and occurs in applications as diverse as stability analysis, the physics of spinning bodies, and the tiny oscillations of vibrating systems, to mention a few. There is no similar difference between left and right in the case of eigenvalues; however, there is a distinction between right and left in the case of eigenvectors. Nondegenerate systems are those in which all eigenvalues are different, and putting them back in results in separate equations for the components of each corresponding eigenvector, and the system is known as non degenerate when this occurs. When the eigenvalues are fold degenerate, the system is said to be degenerate, and the eigenvectors are not linearly independent, the system is said to be degenerate.