Computational Linear Algebra for the Eigenvalue Problem

Catalog description

Provides a thorough background in the formulation and analysis of algorithms for the computation of eigenvalues and eigenvectors. Includes rank revealing factorizations, orthogonal transformations, perturbation theory for matrices, power iterations, and the QR algorithm for unsymmetric and symmetric matrices. Software will be used to implement and test algorithms. Knowledge of computer language is required. Prerequisite(s): for undergraduates and enrollment requirement(s) for graduate students: MATH 465 with a grade of C (2.0) or better.