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 for
sparse eigenvalue problems and sparse least-squares.
prereq: 5304 or numerical linear algebra course or instr consent
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