Distributed Sensor Selection using a Truncated Newton Method
release_37qpvzc3wvfz7jv66n3ulbxk7a
by
Danny Bickson, Danny Dolev
2009
Abstract
We propose a new distributed algorithm for computing a truncated Newton
method, where the main diagonal of the Hessian is computed using belief
propagation. As a case study for this approach, we examine the sensor selection
problem, a Boolean convex optimization problem. We form two distributed
algorithms. The first algorithm is a distributed version of the interior point
method by Joshi and Boyd, and the second algorithm is an order of magnitude
faster approximation. As an example application we discuss distributed anomaly
detection in networks. We demonstrate the applicability of our solution using
both synthetic data and real traffic logs collected from the Abilene Internet
backbone.
In text/plain
format
Archived Files and Locations
application/pdf 164.9 kB
file_7omkpudqzfcnjixzxl4spctkze
|
arxiv.org (repository) web.archive.org (webarchive) |
0907.0931v1
access all versions, variants, and formats of this works (eg, pre-prints)