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AAAI
2007

Restart Schedules for Ensembles of Problem Instances

11 years 1 months ago
Restart Schedules for Ensembles of Problem Instances
The mean running time of a Las Vegas algorithm can often be dramatically reduced by periodically restarting it with a fresh random seed. The optimal restart schedule depends on the Las Vegas algorithm’s run length distribution, which in general is not known in advance and may differ across problem instances. We consider the problem of selecting a single restart schedule to use in solving each instance in a set of instances. We present offline algorithms for computing an (approximately) optimal restart schedule given knowledge of each instance’s run length distribution, generalization bounds for learning a restart schedule from training data, and online algorithms for selecting a restart schedule adaptively as new problem instances are encountered.
Matthew J. Streeter, Daniel Golovin, Stephen F. Sm
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where AAAI
Authors Matthew J. Streeter, Daniel Golovin, Stephen F. Smith
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