Eigenvalues

What are eigenvalues?

Eigenvalues, also known as characteristic roots, are a type of scalar associated with a linear system of equations. Determining a system’s eigenvalues and eigenvectors is crucial in physics and engineering. It is comparable to matrix diagonalization and occurs in applications as diverse as stability analysis, rotating body physics, and tiny oscillations of vibrating systems, to mention a few. Each eigenvalue has an eigenvector that corresponds to it. 

Where are Eigenvectors and Eigenvalues Used?

In linear differential equations, where you want to find a rate of change or maintain relationships between two variables, eigenvalues and eigenvectors are essential. Let’s try to understand with examples.

1. Consider eigenvalues and eigenvectors as summaries of big matrices.
2. A matrix allows us to represent a significant amount of data. It takes a long time to compute a huge matrix. One of the most important methods for increasing efficiency in computationally expensive jobs is to lower the dimensions after ensuring that the majority of the critical information is preserved. As a result, only one eigenvalue and eigenvector are used to capture crucial details held in a big matrix. Data churning components can also benefit from this strategy.
3. Component analysis is one of the most critical ways for reducing dimension space without sacrificing important information. The concept of eigenvalues and eigenvectors lies at the heart of component analysis (PCA). The idea is to calculate the eigenvectors and eigenvalues of the features’ covariance matrix.
4. Eigenvectors and eigenvalues are also employed in facial recognition algorithms like EigenFaces.
5. They’re employed to save space in terms of dimension. The data is compressed using the Eigenvectors and Eigenvalues approach. Many methods, such as PCA, use eigenvalues and eigenvectors to minimise the number of dimensions.
6. Eigenvalues can prevent overfitting and are also employed in regularisation. Simply refer to eigenvalues notes for better understanding.

Important Properties of Eigenvalues

• Real symmetric and hermitian matrices have real eigenvalues.

• The eigenvalues of skew hermitian and real skew-symmetric matrices are either pure imaginary or zero.

• Unitary and orthogonal matrices have unit modulus eigenvalues || = 1.

• If λ1, λ2…….λn are the eigenvalues of A, then kλ1, kλ2…….kλn are eigenvalues of kA

• If λ1, λ2…….λn are the eigenvalues of A, then 1/λ1, 1/λ2…….1/λn are eigenvalues of A-1

• If λ1, λ2…….λn are the eigenvalues of A, then λ1k, λ2k…….λnk are eigenvalues of Ak

• Eigenvalues of A = EigenValues of AT (Transpose)

• Sum of EigenValues = Trace of A (Sum of diagonal elements of A)

• Product of EigenValues = |A|

• Maximum number of distinct eigenvalues of A = Size of A

• If A and B are two matrices of the same order then, Eigenvalues of AB = Eigenvalues of BA

How to Calculate Eigenvector & Eigenvalue?

The steps for calculating the eigenvalue and eigenvector of any matrix A are as follows. To know more about it, simply refer to the eigenvalues notes.

Calculate one or more eigenvalues depending upon the number of dimensions of a square matrix. Determine the corresponding eigenvectors. For calculating the eigenvalues, one needs to solve the following equation:

(A-λI)X=0

A is the parent matrix 

I is the identity matrix

X is any matrix

The eigenvalues of a non-zero eigenvector can be obtained by solving the following equation:

|A-λI|= 0

In the above equation, I is the identity matrix, and values of lambda is the eigenvalue. After the evaluation of eigenvalues, eigenvectors can be solved by the equation (A – λ I)x = 0

When dealing with a complicated system with a large number of dimensions and a significant amount of data, the notions of eigenvectors and eigenvalues aid in the transformation of the data into a collection of the most critical dimensions (principal components). As a result, the data is processed more quickly.

Conclusion

Eigenvalues are key concepts used in feature extraction techniques such as Principal Component Analysis, an algorithm used to reduce dimensionality while training a machine learning model. It is used in several fields, including machine learning, quantum computing, communication system design, construction designs, electrical and mechanical engineering, etc. You can go through eigenvalues important questions for a better understanding.