A New Approach to Ralaxation in Integer Programming
{'Code': '07010000', 'Directorate': {'LongName': 'Directorate For Engineering'}, 'Division': {'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Kristen M. Biggar, N-BioS'}
The focus of this project is on the analysis of a new relaxation                approximation for integer programming problems and on the design of             algorithms exploiting this technique.  This new relaxation, called              composite decomposition, can be viewed as a generalization of both              surrogate and Lagrangean relaxations.  The principal investigator has           recently proved that it dominates Lagrangean, surrogate and LP                  relaxations, in the sense that the bounds on the optimum are at least           as good and probably better.  The aim of this proposal is to study dual         ascent heuristic methods which appear best suited to the composite              decomposition relaxation.  Emphasis will be placed on classes of                problems which lend themselves naturally to this kind of decomposition,         such as network location problems, generalized assignment/allocation            problems, and multiple choice problems, as well as specially structured         problems with a few general side constraints.
{'FirstName': 'Monique', 'LastName': 'Guignard-Spielberg', 'EmailAddress': 'guignard@wharton.upenn.edu', 'StartDate': '09/15/1985', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Pennsylvania', 'CityName': 'Philadelphia', 'ZipCode': '191046205', 'PhoneNumber': '2158987293', 'StreetAddress': 'Research Services', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'StateCode': 'PA'}
{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1985&All=true', 'xml': '8508142.xml'}