MATH 5165 - Numerical Linear Algebra

Matrix norms and condition numbers. Direct methods for linear systems and their accuracy and stability. Iterative methods for large sparse linear systems and their convergence. Least squares methods for non-square linear systems. Matrix Decompositions (LU and Cholesky Decompositions, Rank Decomposition, QR decomposition; Eigendecomposition and Diagonalization, Singular Value Decomposition). Efficient matrix multiplication methods. Matrix approximations. Matrix phylogeny. Examples of numerical linear algebra in machine learning.

Credit Hours: (3)
Prerequisite(s): ITSC 1212 and MATH 2164, or permission of department.
Cross-listed Course(s): MATH 4165

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