Optimal experiment design in a filtering context with application to sampled network data

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking net...

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Bibliographic Details
Published inarXiv.org
Main Authors Singhal, Harsh, Michailidis, George
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.10.2010
Subjects
Design of experiments
Design optimization
Economic models
Filtration
Mathematical models
Optimization
Random walk
State space models
Statistics - Applications
Steady state
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Summary:We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, we relate our work to the general problem of steady state optimal design for state space models.
Bibliography:IMS-AOAS-AOAS283
ISSN:2331-8422
DOI:10.48550/arxiv.1010.1126