Eigenvalues and eigenvectors are fundamental concepts in linear algebra that provide important information about the structure and behavior of linear transformations and square matrices. They play a central role in mathematics, physics, engineering, computer science, statistics, and data science. An eigenvector of a matrix is a nonzero vector whose direction remains unchanged when the matrix acts on it, while the corresponding eigenvalue represents the factor by which the vector is scaled. This research article presents a systematic study of the theory of eigenvalues and eigenvectors, including their definitions, characteristic equations, algebraic and geometric multiplicities, eigenspaces, diagonalization, and fundamental properties. The article also examines important applications in systems of linear differential equations, stability analysis, vibration analysis, principal component analysis, Markov chains, quantum mechanics, and graph theory. Particular attention is given to the role of diagonalization and symmetric matrices in simplifying complex mathematical problems. The study demonstrates that eigenvalue-based methods provide a powerful framework for understanding both theoretical and applied problems involving linear transformations.
Eigenvalues, Eigenvectors, Characteristic Equation, Eigenspace, Diagonalization, Algebraic Multiplicity, Geometric Multiplicity, Linear Algebra, Applications
Manisha Sahu. Eigenvalues And Eigenvectors: Theory and Applications. Indian Journal of Modern Research and Reviews. 2026; 4(8):190-195
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