3 credits
Fall 2026 Lecture Upper Division
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.
Learning Outcomes1Become 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.
2Be able to implement basic versions of these kernels and algorithms.
3Learn about the difference between direct and iterative methods for linear systems.
4Gain expertise in the design of iterative methods and preconditioning techniques for large-scale sparse linear systems.
5Be able to implement eigenvalue and singular-value problem solvers.
Prerequisites
Restrictions
made by Purdue students.