Additive approximation for edge-deletion problems

Mathematics – Combinatorics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

A graph property is monotone if it is closed under removal of vertices and edges. In this paper we consider the following edge-deletion problem; given a monotone property P and a graph G, compute the smallest number of edge deletions that are needed in order to turn G into a graph satisfying P. We denote this quantity by E_P(G). Our first result states that for any monotone graph property P, any \epsilon >0 and n-vertex input graph G one can approximate E_P(G) up to an additive error of \epsilon n^2 Our second main result shows that such approximation is essentially best possible and for most properties, it is NP-hard to approximate E_P(G) up to an additive error of n^{2-\delta}, for any fixed positive \delta. The proof requires several new combinatorial ideas and involves tools from Extremal Graph Theory together with spectral techniques. Interestingly, prior to this work it was not even known that computing E_P(G) precisely for dense monotone properties is NP-hard. We thus answer (in a strong form) a question of Yannakakis raised in 1981.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Additive approximation for edge-deletion problems does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Additive approximation for edge-deletion problems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Additive approximation for edge-deletion problems will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-692273

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.