A maximum entropy based scalable algorithm for resource allocation problems

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a scalable algorithm for solving resource allocation problems on large datasets. This class of problems is posed as a multi-objective optimization problem in a Maximum Entropy Principle framework. This algorithm solves a multi-objective optimization problem that minimizes simultaneously the coverage cost and the computational cost by appropriate recursive prescription of smaller subsets required for a 'divide and conquer' strategy. It provides characterization of the inherent trade-off between reduction in computation time and the coverage cost. Simulations are presented that show significant improvements in the computational time required for solving the coverage problem while maintaining the coverage costs within prespecified tolerance limits.

Original languageEnglish (US)
Title of host publicationProceedings of the 2007 American Control Conference, ACC
Number of pages6
StatePublished - Dec 1 2007
Event2007 American Control Conference, ACC - New York, NY, United States
Duration: Jul 9 2007Jul 13 2007

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2007 American Control Conference, ACC
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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