The design, analysis, and implementation of algorithms for the solution of sparse matrix problems on distributed memory multiprocessors will be investigated. The development of these parallel sparse matrix algorithms should have an impact of challenging large-scale computational problems in several scientific, econometric, and engineering disciplines. The following are among the problems that will be investigated: o A spectral nested dissection algorithm for computing good orderings for factoring sparse matrices efficiently in parallel, o Algorithms for mapping computational subtasks and the given matrix to the processors in a distributed memory multiprocessor, o The role played by the clique tree, a data structure for the compact representation of filled matrices, in several sparse matrix algorithms, and o The design of sparse orthogonal factorization algorithms for the solution of least-squares problems on distributed memory multiprocessors.
