Projected Hessian Updating Methods
{'Code': '05010200', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'CCF', 'LongName': 'Division of Computing and Communication Foundations'}}
{'SignBlockName': 'S. Kamal Abdali', 'PO_EMAI': None, 'PO_PHON': None}
This project will investigate numerical methods for solving nonlinear           programming problems.  The problems involve minimizing a nonlinear              function subject to a set of nonlinear equality and inequality                  constraints.  Sequential quadratic programming (SQP) methods are                particularly effective for solving problems of this nature.  It is              assumed that first derivatives of the objective and constraint                  functions are available, but that second derivatives may be too                 expensive to compute.  Instead the methods typically update a suitable          matrix which approximates second derivative information each iteration.         We are interested in developing SQP methods which maintain an                   approximation to second derivative information projected onto the               tangent space of the constraints.  The main motivation for the work is          that only the projected matrix enters into the optimality conditions            for the nonlinear problem.  We will investigate issues relating to both         the global and local convergence properties of algorithms.  These               include the choice of quasi-Newton update, active set strategy and              merit function.  The main goal is                                               two-fold: to produce efficient and reliable software and to provide a           theoretical framework for these methods.
{'FirstName': 'Chaya', 'LastName': 'Gurwitz', 'PI_MID_INIT': 'B', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Chaya B Gurwitz', 'EmailAddress': 'gurwitz@sci.brooklyn.cuny.edu', 'NSF_ID': '000184648', 'StartDate': '02/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'CUNY Brooklyn College', 'CityName': 'BROOKLYN', 'ZipCode': '112102850', 'PhoneNumber': '7189515622', 'StreetAddress': '2900 BEDFORD AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'New York', 'StateCode': 'NY', 'CONGRESSDISTRICT': '09', 'CONGRESS_DISTRICT_ORG': 'NY09', 'ORG_UEI_NUM': 'XNAKYW3FTSE1', 'ORG_LGL_BUS_NAME': 'RESEARCH FOUNDATION OF THE CITY UNIVERSITY OF NEW YORK', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '2865', 'Text': 'NUMERIC, SYMBOLIC & GEO COMPUT'}, {'Code': '9120', 'Text': 'CROSS-DIRECTORATE PROGRAMS'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8713893.xml'}