Sparsity and sparse matrices. Data structures for sparse matrices. Direct methods for sparse linear systems. Reordering techniques to reduce fill-in such as minimal degree ordering and nested dissection ordering. Iterative methods. Preconditioning algorithms. Algorithms forsparse eigenvalue problems and sparse least-squares.prereq: 5304 or numerical linear algebra course or instr consent