ABSTRACT
Linear transformations can be represented by matrices which act on vectors. Eigenvalues, eigenvectors and eigenspaces are properties of a matrix1. They capture all the essential properties of the matrix or the corresponding transformation. Historically, the importance of eigenvalues and the corresponding eigenvectors arose from studies in physics and in the study of quadratic forms and differential equations. The concepts of eigenvalues and eigenvectors extend to linear transformations in more general spaces. These have applications in many different areas of science; in particular, in economics, engineering, finance, quantum mechanics, mathematics and statistics.
