Techniques for Large-Scale Optimization
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Kristen M. Biggar, N-BioS', 'PO_EMAI': None, 'PO_PHON': None}
The objectives of this research are the theoretical development of              efficient algorithms for large-scale optimization and the experimental          examination of these algorithms.  The approach is to use decomposition          methods based on the dual decomposition technique. This technique takes         advantage of additive separability which is often found in large-scale          problems.  The new algorithms will be applicable to both convex as well         as non-convex optimization problems.  In addition, methods will be              developed for optimization of large-scale systems modeled by                    probabilistic discrete-event models.
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{'Name': 'Washington University', 'CityName': 'SAINT LOUIS', 'ZipCode': '63110', 'PhoneNumber': '3147474134', 'StreetAddress': 'ONE BROOKINGS DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Missouri', 'StateCode': 'MO', 'CONGRESSDISTRICT': '01', 'CONGRESS_DISTRICT_ORG': 'MO01', 'ORG_UEI_NUM': 'L6NFUM28LQM5', 'ORG_LGL_BUS_NAME': 'WASHINGTON UNIVERSITY, THE', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
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