Computation and Theory in Decentralized Optimization
{'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 is a proposal for research in developing better methods for                solving optimization problems that present special difficulties because         of their large size, because they contain nonsmooth data, or because            they contain stochastic elements.  It includes both direct development          and testing of algorithms, and improvement of the theory base to                support future algorithmic development.                                         Previous NSF support has made possible the development of a new, very           fast, decomposition method for structured convex optimization problems          (bundle-based decomposition).  The method has been theoretically                justified, and has been tested on large structured linear programming           problems with excellent results.                                                                                                                                The next step, proposed here, is to test the algorithm on decomposable          nonlinear (convex) programming problems to see if its computational             effectiveness in the linear case carries over to nonlinear problems.            It is planned to test specially structured problems, such as nonlinear          networks with side constraints, as well as block-separable problems             without any particular network structure.                                                                                                                       At the same time, it is planned to carry out research aimed at                  accelerating the convergence of the underlying (bundle) algorithm used          in the decomposition method.  Such acceleration would not only extend           the speed and capability of the method just described, but would also           make it possible for many other kinds of nonsmooth convex programming           problems to be solved more quickly and easily.  To devise such                  acceleration methods the PI will study the efficient approximation of           nonsmooth optimization problems, primarily those having some convex             structure, and the use of such approximations in algorithm development.
{'FirstName': 'Stephen', 'LastName': 'Robinson', 'PI_MID_INIT': 'M', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Stephen M Robinson', 'EmailAddress': 'smrobins@wisc.edu', 'NSF_ID': '000333492', 'StartDate': '06/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Wisconsin-Madison', 'CityName': 'MADISON', 'ZipCode': '537151218', 'PhoneNumber': '6082623822', 'StreetAddress': '21 North Park Street', 'StreetAddress2': 'Suite 6301', 'CountryName': 'United States', 'StateName': 'Wisconsin', 'StateCode': 'WI', 'CONGRESSDISTRICT': '02', 'CONGRESS_DISTRICT_ORG': 'WI02', 'ORG_DUNS_NUM': '161202122', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF WISCONSIN SYSTEM', 'ORG_PRNT_DUNS_NUM': '041188822'}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '2865', 'Text': 'NUMERIC, SYMBOLIC & GEO COMPUT'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8801489.xml'}