Introduction to Eigen Vector

Eigen vectors are fundamental concepts in linear algebra that play a crucial role in various fields, from physics to computer science. They provide insights into the behavior of linear transformations encoded in matrices. This article delves into the definition, equations, methods, and examples of eigen vectors.

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    Definition of Eigen Vector of a matrix

    An eigen vector of a square matrix represents a non-zero vector that, when multiplied by the matrix, results in a scaled version of itself. In simpler terms, the direction of the vector remains unchanged, though its length may change.

    Eigen vector equation

    For a matrix A and an eigen vector v, the eigen vector equation is given by: A****v = λv

    Here, A is the matrix, v is the eigen vector, and λ (lambda) is the scalar value known as the eigenvalue corresponding to v.

    Eigen Vector method

    The eigen vector method involves solving the eigen vector equation to find both eigenvalues and corresponding eigen vectors. It is often accomplished using techniques like the characteristic polynomial, eigenvalue decomposition, or iterative algorithms.

    How to find eigenvector

    To find an eigen vector for a given eigenvalue λ, we substitute λ into the equation (A – λI)v = 0, where I is the identity matrix. The non-trivial solution of this equation gives the eigen vector.

    Eigen vectors can have both left and right forms. Left eigen vectors result from the equation u****A = λu, where u is the left eigen vector. Right eigen vectors result from the equation A****v = λv, where v is the right eigen vector. The left and right eigen vectors can be different for non-symmetric matrices.

    Applications of eigenvectors

    Eigen vectors of a matrix have diverse applications across various fields due to their ability to capture essential properties of linear transformations. These applications include:

    Quantum Mechanics: Eigen vectors represent stationary states in particle behavior.

    Data Analysis: Principal Component Analysis (PCA) reduces data dimensions while retaining patterns.

    Structural Engineering: Identifies modes of vibration and stability in structures.

    Image Compression: Efficiently compresses images by retaining crucial information.

    Mechanical Engineering: Analyzes vibrations and stresses in mechanical systems.

    Finance: Used in portfolio optimization and risk management.

    Computer Graphics: Shapes analysis, animation, and image manipulation.

    Chemistry: Deciphers molecular vibrational modes in spectroscopy.

    Social Networks: Identifies influential nodes and communities.

    Signal Processing: Aids in noise reduction, speech recognition, and image filtering.

    In various domains, eigen vectors enhance understanding and problem-solving capabilities.

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    Solved examples on Eigenvectors

    Find the eigen values and eigen vectors of matrix A = [2 2; 1 3]

    Solution: Eigen values = 4, 1; Eigen vectors = [1; 1], [-2; 1].

    For matrix B = [3 2; 1 4], compute the left and right eigen vectors.

    Solution: Left eigen vector = [2; -1], Right eigen vector = [1; 1].


    Eigen vectors offer profound insights into the behavior of linear transformations encapsulated in matrices. They find applications in various fields, including quantum mechanics, data analysis, and computer graphics, enhancing our understanding of complex systems.

    Frequently Asked Questions on Eigenvectors

    Can a matrix have no eigen vectors?

    Yes, some matrices do not have eigen vectors, which typically occurs in complex or irregular matrices.

    Do all eigen vectors have corresponding eigenvalues?

    Yes, each eigen vector corresponds to a specific eigenvalue.

    How are eigen vectors used in data analysis?

    Eigen vectors are used in techniques like Principal Component Analysis (PCA) to reduce data dimensions while preserving meaningful information.

    Are eigen vectors unique for a matrix?

    Eigen vectors are unique up to a scalar multiple. In other words, any scalar multiple of an eigen vector is also an eigen vector.

    Can a matrix have repeated eigenvalues?

    Yes, matrices can have repeated eigenvalues, and this often leads to more than one linearly independent eigen vector for the same eigenvalue.

    What is eigen value and eigen vector of a matrix?

    Eigenvalues and eigenvectors of a matrix are fundamental concepts in linear algebra. Eigenvalues are scalar values that scale eigenvectors, which are non-zero vectors representing directions unaffected by matrix transformations. They play a crucial role in various applications, from quantum mechanics to data analysis.

    Can a 2 by 2 matrix have eigen vectors?

    Yes, a 2 by 2 matrix can have eigen vectors. Eigen vectors and eigen values are concepts applicable to square matrices, including 2 by 2 matrices. For a 2 by 2 matrix, you can find its eigenvalues and corresponding eigenvectors using algebraic methods or computational techniques.

    What is the formula for eigen vectros?

    The formula to find eigen vectors for a square matrix A and its eigenvalue λ is: (A - λI*)v = 0 Here, I is the identity matrix, v is the eigen vector, and 0 is the zero vector. Solving this equation yields the eigen vector corresponding to the given eigenvalue.

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