Research Initiation:  Dual Methods for Large-Scale NonlinearNetwork Optimization
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti', 'PO_EMAI': None, 'PO_PHON': None}
This proposal concerns the theoretical and computational development            of Lagrangian dual methods especially designed to solve large-scale             convex network problems.  These methods combine conjugate gradient              directions with efficient line search procedures.  They possess the             two basic properties that are necessary to be practical for large-              scale nonlinear optimization:  they use the structure and sparsity of           the problem to save memory requirements and to simplify matrix-vector           products; and they achieve rapid convergence because Newton directions          are employed.                                                                                                                                                   The first objective of the proposed research is to continue the                 preliminary research work by the principal investigator on separable            quadratic networks.  Theoretical issues such as the finite convergence          property and the use of preconditioning matrices to accelerate the              conjugate gradient methods will be investigated.  The second objective          includes the development of an efficient line search technique                  necessary for the extension of the Lagrangian dual approach to convex           separable networks.  Another objective concerns the design of an                algorithm based on this dual approach for implementation on parallel            computers.  Finally, an extensive computational study with large real-          world network problems will be performed to test this important new             methodology for larger-scale convex network optimization.
{'FirstName': 'Jose', 'LastName': 'Ventura', 'PI_MID_INIT': 'A', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Jose A Ventura', 'EmailAddress': 'jav1@psu.edu', 'NSF_ID': '000484445', 'StartDate': '09/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Missouri-Columbia', 'CityName': 'COLUMBIA', 'ZipCode': '652113020', 'PhoneNumber': '5738827560', 'StreetAddress': '121 UNIVERSITY HALL', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Missouri', 'StateCode': 'MO', 'CONGRESSDISTRICT': '03', 'CONGRESS_DISTRICT_ORG': 'MO03', 'ORG_UEI_NUM': 'SZPJL5ZRCLF4', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF MISSOURI SYSTEM', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '145400', 'Text': 'SYSTEMS THEORY'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8809501.xml'}