1Become 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.