Some Computational Developments for Applications of         Piecewise Quadratic Programs
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{'SignBlockName': 'Kevin I. Sewell', 'PO_EMAI': None, 'PO_PHON': None}
Piecewise quadratic programming is an important model in optimization.          This model has many potential applications in theory and practice               including dual quadratic programming, stochastic quadratic programming          with simple resource, and parametric quadratic programming.  Recently           the author has started a systematic study on this topic and has                 implemented a direct algorithm for linearly constrained convex                  separable piecewise quadratic programming (PQP).  This research will            continue the investigation on PQP in the following areas: direct and            indirect algorithms, both exact and approximate; network related PQP            problems; PQP as an approximation to general nonlinear separable                programming with linear constraints; and applications of PQP in                 parallel computation.                                                                                                                                           The PI has developed expertise through his doctoral research that               qualifies him well for the project.  Northwestern provides an                   excellent environment for this type of research.  An award is highly            recommended.
{'FirstName': 'Jie', 'LastName': 'Sun', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jie Sun', 'EmailAddress': None, 'NSF_ID': '000432076', 'StartDate': '04/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
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[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '5514', 'Text': 'OPERATIONS RESEARCH'}]
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