Numerical optimization of generative network parameters

Robert A. Hummel, Joshua A. Taylor, Franz S. Hover

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

1 Scopus citations

Abstract

We address the early design of complex, large-scale systems by viewing them as random networks and optimizing structure over their generative parameters. In this approach, we do not seek specific topologies, but rather classes of near-optimal networks which correspond to desirable statistical behavior, while also allowing flexibility to accommodate unmodeled constraints. Functionally, we perform the optimization of generative parameters on small networks (e.g., one hundred nodes) and use the results to design large networks (e.g., one thousand or more nodes). This approach is a computationally feasible forward design path for large-scale systems. A numerical example is given in which a network's degree distribution is optimized for combined robustness and cost in a cascading failure scenario; the work has direct application to distributed communication systems.

Original languageEnglish (US)
Title of host publicationDesign and Manufacturing
PublisherAmerican Society of Mechanical Engineers (ASME)
Pages465-472
Number of pages8
EditionPARTS A AND B
ISBN (Print)9780791844274
DOIs
StatePublished - 2010
Externally publishedYes
EventASME 2010 International Mechanical Engineering Congress and Exposition, IMECE 2010 - Vancouver, BC, Canada
Duration: Nov 12 2010Nov 18 2010

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
NumberPARTS A AND B
Volume3

Conference

ConferenceASME 2010 International Mechanical Engineering Congress and Exposition, IMECE 2010
Country/TerritoryCanada
CityVancouver, BC
Period11/12/1011/18/10

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Fingerprint

Dive into the research topics of 'Numerical optimization of generative network parameters'. Together they form a unique fingerprint.

Cite this