Large Sparse Stable Matrix Computation
{'Code': '05010200', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'John D. Gannon', 'PO_EMAI': None, 'PO_PHON': None}
The focus of this research is on the solution of sparse least squares           problems on sequential and parallel computers.                                                                                                                  An important problem of interest will be the solution of equality               constrained least squares problems, or as they are often posed, equality        constrained minimization problems.  Three different approaches will be          examined:  (1) Null Space Methods, (2) Iterative Solution of the Kuhn-          Tucker Equations, and (3) Weighted Least Squares Methods.  The methods          will be considered from the standpoints of error analysis, computational        complexity, and adaptability to high performance architectures.                                                                                                 Graph theoretical issues arise in the investigation of the null space           approach to equality constrained least squares, and solution of sparse          least squares problems.  In particular, the Dulmage-Mendelsohn                  decomposition of a rectangular matrix will play a role in the                   computational aspects of all the problems considered.
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{'Code': '2865', 'Text': 'NUMERIC, SYMBOLIC & GEO COMPUT'}
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