Numerical Linear Algebra

Catalog description

Direct and iterative solvers of dense and sparse linear systems of equations, numerical schemes for handling symmetric algebraic eigenvalue problems, and the singular-value decomposition and its applications in linear least squares problems. Typically offered Spring. Prerequisite(s): CS 31400 FOR LEVEL UG WITH MIN. GRADE OF C- OR MA 26500 FOR LEVEL UG WITH MIN. GRADE OF C- OR MA 35100 FOR LEVEL UG WITH MIN. GRADE OF C- AND MA 51100 FOR LEVEL GR WITH MIN. GRADE OF C- Course Learning Outcomes 1. Become knowledgeable about major kernels and algorithms underlying matrix computations for dense and sparse matrices including linear systems of equation, symmetric eigenvalue problems, and the singular-value decomposition. 2. Be able to implement basic versions of these kernels and algorithms. 3. Learn about the difference between direct and iterative methods for linear systems. 4. Gain expertise in the design of iterative methods and preconditioning techniques for large-scale sparse linear systems. 5. Be able to implement eigenvalue and singular-value problem solvers. View Class Schedule