Algorithmic Issues on Locally Dense Networks.

Abstract

The PI was awarded the United States Office of Naval Research grant N00014-95-1-0779 titled 'Algorithmic Issues on Locally Dense Networks' scheduled to extend from May 1, 1995 to August 31, 1996. The total amount of the award was $43,582. Although the research supported by this grant encompasses a wide variety of issues and approaches, the unifying theme of our effort was the design of efficient algorithms for locally dense graphs - a class that extends and generalizes a number of classical classes of graphs with many practical applications. The grant was specifically designed to investigate various classes of networks that feature locally dense behaviour. Examples include LANs as well as some classes of Wide Area Networks. Our goal was to provide a unifying look at these types of networks. Results were sought both at the structural and algorithmic level. At the structural level we were interested in identifying the 'agent' responsible for the linear structure apparent in these networks. It is this very linear structure that is exploited, in one way or another, by all known algorithm on the underlying graph classes. Our goal was to identify a common algorithmic base that will result in simpler and more efficient algorithms for a large spectrum of computational problems.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1996
Accession Number
ADA328895

Entities

People

  • Stephan Olariu

Organizations

  • Old Dominion University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Computations
  • Computer Networks
  • Computer Science
  • Distributed Computing
  • Fault Tolerance
  • Information Processing
  • Machine Perception
  • Mathematics
  • Military Research
  • Networks
  • New York
  • Theoretical Computer Science
  • United States
  • Wide Area Networks

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Operations Research
  • Research Science/Academic Research