DocumentCode :
3171150
Title :
A Maximum Entropy Based Scalable Algorithm for Resource Allocation Problems
Author :
Sharma, Puneet ; Salapaka, Srinivasa ; Beck, Carolyn
Author_Institution :
Univ. of Illinois at Urbana Champaign, Urbana
fYear :
2007
fDate :
9-13 July 2007
Firstpage :
516
Lastpage :
521
Abstract :
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 pre- specified tolerance limits.
Keywords :
divide and conquer methods; maximum entropy methods; operations research; optimisation; computational cost; coverage cost; divide and conquer strategy; maximum entropy based scalable algorithm; multi-objective optimization; resource allocation; Cities and towns; Computational efficiency; Computational modeling; Cost function; Drugs; Entropy; Libraries; Motion control; Partitioning algorithms; Resource management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
ISSN :
0743-1619
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2007.4282846
Filename :
4282846
Link To Document :
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