An Improved Upper Bound for Bootstrap Percolation in All Dimensions

Mathematics – Combinatorics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

19 pages, 2 figures

Scientific paper

In $r$-neighbor bootstrap percolation on the vertex set of a graph $G$, a set $A$ of initially infected vertices spreads by infecting, at each time step, all uninfected vertices with at least $r$ previously infected neighbors. When the elements of $A$ are chosen independently with some probability $p$, it is interesting to determine the critical probability $p_c(G,r)$ at which it becomes likely that all of $V(G)$ will eventually become infected. Improving a result of Balogh, Bollob\'as, and Morris, we give a second term in the expansion of the critical probability when $G = [n]^d$ and $d \geq r \geq 2$. We show that there exists a constant $c > 0$ such that for all $d \geq r \geq 2$, \[p_c([n]^d, r) \leq (\dfrac{\lambda(d,r)}{\log_{(r-1)}(n)} - \dfrac{c}{(\log_{(r-1)}(n))^{2 - 1/(2d - 2)}})^{d-r+1}\] as $n \to \infty$, where $\lambda(d,r)$ is an exact constant and $\log_{(k)}(n)$ denotes the $k$-times iterated logarithm of $n$.

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

An Improved Upper Bound for Bootstrap Percolation in All Dimensions 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 An Improved Upper Bound for Bootstrap Percolation in All Dimensions, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An Improved Upper Bound for Bootstrap Percolation in All Dimensions will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-33579

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