approximating fault-tolerant domination in general graphs
DESCRIPTION
Approximating Fault-Tolerant Domination in General Graphs. Minimum Dominating Set. Can be approximated with ratio [Slavík, 1996] [ Chlebík and Chlebíková , 2008] NP-hard lower bound of [ Alon , Moshkovitz and Safra , 2006]. Minimum -tuple Dominating Set. - PowerPoint PPT PresentationTRANSCRIPT
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ETH Zurich – Distributed Computing – www.disco.ethz.ch
Klaus-Tycho Förster
Approximating Fault-Tolerant Domination in General Graphs
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Minimum Dominating Set
• Can be approximated with ratio– [Slavík, 1996]– [Chlebík and Chlebíková, 2008]
• NP-hard lower bound of – [Alon, Moshkovitz and Safra, 2006]
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Minimum -tuple Dominating Set
minimum 2-tuple dominating set
• -tuple dominating set: – Every node should have dominating nodes in its neighborhood
[Harary and Haynes, 2000 and Haynes, Hedetniemi and Slater, 1998]
• Can be approximated with ratio– [Klasing and Laforest, 2004]
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Minimum -Dominating Set
minimum 2-dominating set• -dominating set:
– Every node should be in the dominating set or have dominating nodes in its neighborhood[Fink and Jacobson, 1985]
• Best known approx.-ratio[Kuhn, Moscibroda and Wattenhofer, 2006]
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Overview of the remaining Talk
• -tuple domination vs -domination
• NP-hard lower bound for -domination
• Improved approximation ratio for -domination
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-tuple Domination versus -Domination
• -tuple dominating set only exists if min. degree
• Every -tuple dominating set is a -dominating set
• But how “bad” can a -tuple dom. set be in comparison?
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-tuple Domination versus -Domination: With = 2
• At least nodes for a -tuple dom. set• But nodes suffice for a -dom. set
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-tuple Domination versus -Domination
• : Off by a factor of nearly !
• For and Off by a factor (tight)
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NP−hard lower bound for 𝑘−domination• NP-hard lower bound for 1-domination
– [Alon, Moshkovitz and Safra, 2006]
• If we could approx. -dom. set with ratio of – Then build a -multiplication graph:
Example for
• NP-hard lower bound for -domination
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Improved approximation ratio for 𝑘−domination
• Utilizes a greedy-algorithm
• Use “degree” of per node– , if in the -dominating set– else #neighbors in the -dominating set, but at most
• Pick a node that improves total sum of degree the most
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• Let a fixed optimal solution have nodes
• Greedy does at least of remaining work per step
• If it does more, also good
• Total amount of work is
• This gives an approximation ratio of roughly
When does the Greedy Algorithm finish?
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• When is chopping off of the remaining work ineffective?
• When remaining work is less than r
• Then at most r more steps are needed
• Stop chopping after steps
• Gives an approx. ratio of
When to stop when chopping off…
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• does not look too nice…
• 1)
• 2)
• Yields: Approx. ratio of less than
•
Calculating the approximation ratio
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Extending the Domination Range
• Instead of dominating the 1-neighborhood…
• … dominate the -neighborhood
• Often called -step domination cf. [Hage and Harary, 1996]
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Extending the Domination Range
• The black nodes form a 2-step dominating set
• But not a 2-step 2-dominating set !
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Extending the Domination Range
• Instead of having dominating nodes in the -neighborhood …– (unless you are in the dominating set)
• … have node-disjoint paths of length at most
• Results in approximation ratio of:
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ETH Zurich – Distributed Computing – www.disco.ethz.ch
Klaus-Tycho Förster
Thank you