Eigen vector of a matrix A is a vector represented by a matrix X such that when X is multiplied with matrix A, then the direction of the resultant matrix remains same as vector X.
Mathematically, above statement can be represented as:
AX = λX
where A is any arbitrary matrix, λ are eigen values and X is an eigen vector corresponding to each eigen value.
Here, we can see that AX is parallel to X. So, X is an eigen vector.
Method to find eigen vectors and eigen values of any square matrix A
We know that,
AX = λX
=> AX – λX = 0
=> (A – λI) X = 0 …..(1)
Above condition will be true only if (A – λI) is singular. That means,
|A – λI| = 0 …..(2)
(2) is known as characteristic equation of the matrix.
The roots of the characteristic equation are the eigen values of the matrix A.
Now, to find the eigen vectors, we simply put each eigen value into (1) and solve it by Gaussian elimination, that is, convert the augmented matrix (A – λI) = 0 to row echelon form and solve the linear system of equations thus obtained.
Some important properties of eigen values
Eigen values of real symmetric and hermitian matrices are real
Eigen values of real skew symmetric and skew hermitian matrices are either pure imaginary or zero
Eigen values of unitary and orthogonal matrices are of unit modulus |λ| = 1
If λ1, λ2…….λn are the eigen values of A, then kλ1, kλ2…….kλn are eigen values of kA
If λ1, λ2…….λn are the eigen values of A, then 1/λ1, 1/λ2…….1/λn are eigen values of A-1
If λ1, λ2…….λn are the eigen values of A, then λ1k, λ2k…….λnk are eigen values of Ak
Eigen values of A = Eigen Values of AT (Transpose)
Sum of Eigen Values = Trace of A (Sum of diagonal elements of A)
Product of Eigen Values = |A|
Maximum number of distinct eigen values of A = Size of A
If A and B are two matrices of same order then, Eigen values of AB = Eigen values of BA
This article has been contributed by Saurabh Sharma.
If you would like to contribute, please email us your interest at email@example.com
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above
Attention reader! Don’t stop learning now. Get hold of all the important DSA concepts with the DSA Self Paced Course at a student-friendly price and become industry ready.
- Basis Vectors in Linear Algebra - ML
- Orthogonal and Orthonormal Vectors in Linear Algebra
- Mathematics | Probability
- Mathematics | Generalized PnC Set 2
- Mathematics | Generalized PnC Set 1
- Mathematics | Power Set and its Properties
- Mathematics | Generating Functions - Set 2
- Mathematics | Indefinite Integrals
- Mathematics | Introduction to Proofs
- Definite Integral | Mathematics
- Mathematics | Law of total probability
- Mathematics | Predicates and Quantifiers | Set 2
- Mathematics | Propositional Equivalences
- Mathematics | Predicates and Quantifiers | Set 1
- Mathematics | Conditional Probability
- Mathematics | Matrix Introduction
- Mathematics | The Pigeonhole Principle
- Mathematics | Rules of Inference
- Mathematics | Rolle's Mean Value Theorem
- Mathematics | Set Operations (Set theory)