Matrix diagonalization is one of the central techniques in linear algebra for simplifying the study of square matrices and linear transformations. The method replaces a matrix by a similar diagonal matrix whenever a sufficient number of linearly independent eigenvectors exists. Because multiplication, powers, and many functions of a diagonal matrix are considerably easier to compute, diagonalization provides both theoretical insight and practical computational advantages. This article presents the basic theory of eigenvalues, eigenvectors, characteristic polynomials, and diagonalization; explains the conditions under which a matrix is diagonalizable; and discusses important applications in systems of linear differential equations, discrete dynamical systems, Markov chains, quadratic forms, oscillations, and matrix powers. A small illustrative example is included to show the procedure. The discussion also considers symmetric matrices and the limitations of diagonalization, including the role of Jordan canonical form when an eigenbasis does not exist.
Matrix diagonalization; eigenvalues; eigenvectors; similarity transformation; linear algebra; differential equations; Markov chains; quadratic forms.
Manisha Sahu. Matrix Diagonalization and Its Applications in Linear Algebra. Indian Journal of Modern Research and Reviews. 2026; 4(8):185-189
Download PDF
