Search arXiv⌕ Search

arXiv · 1401.6894

Accessibility percolation with backsteps

Abstract

Consider a graph in which each site is endowed with a value called \emph{fitness}. A path in the graph is said to be "open" or "accessible" if the fitness values along that path is strictly increasing. We say that there is accessibility percolation between two sites when such a path between them exists. Motivated by the so called House-of-Cards model from evolutionary biology, we consider this question on the $L$-hypercube $\{0,1\}^L$ where the fitness values are independent random variables. We show that, in the large $L$ limit, the probability that an accessible path exists from an arbitrary starting point to the (random) fittest site is no more than $x^*_{1/2}= 1-\frac12\sinh^{-1}(2) =0.27818\ldots$ and we conjecture that this probability does converge to $x^*_{1/2}$. More precisely, there is a phase transition on the value of the fitness $x$ of the starting site: assuming that the fitnesses are uniform in $[0,1]$, we show that, in the large $L$ limit, there is almost surely no path to the fittest site if $x>x^*_{1/2}$ and we conjecture that there are almost surely many paths if $x<x^*_{1/2}$. If one conditions on the fittest site to be on the opposite corner of the starting site rather than being randomly chosen, the picture remains the same but with the critical point being now $x^*_1= 1-\sinh^{-1}(1)= 0.11863\ldots$. Along the way, we obtain a large $L$ estimation for the number of self-avoiding paths joining two opposite corners of the $L$-hypercube.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Julien Berestycki, Éric Brunet, Zhan Shi. 2014-01-27. Accessibility percolation with backsteps. https://arxiv.org/abs/1401.6894

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Selection in the Zero-Noise Limit for High-Dimensional Diffusions with Measurable Drift

This paper investigates the zero-noise limit of high-dimensional small-noise diffusion processes governed by the stochastic differential equation (SDE) \begin{equation*} \mathrm{d}X_{t}^{\varepsilon }=b(X_{t}^{\varepsilon })\,\mathrm{d}t+\varepsilon \,\mathrm{d}W_{t},\quad X_{0}^{\varepsilon }=0,\quad \varepsilon >0, \end{equation*} where the drift coefficient $b$ is assumed to be measurable and bounded. Under this condition, the associated deterministic ordinary differential equation (ODE) $\dot{x}_{t}=b(x_{t})$ may admit multiple Filippov solutions, i.e., solutions in the sense of differential inclusions, due to the lack of Lipschitz continuity or uniqueness criteria. However, the introduction of non-degenerate additive noise restores well-posedness: the perturbed system admits a unique strong (pathwise) solution for each $\varepsilon >0$. Our analysis is based on the radial-spherical decomposition. We prove that, under certain assumptions, the radius dominates a one-dimensional comparison process and escapes to infinity at a linear rate, and the extrinsic angular martingale converges. Under an explicit separable gradient structure, expressed in terms of a radial potential $V(r,ω)=\langle b(rω),ω\rangle $, the angle converges, for every fixed noise, almost surely to the set of global angular maximizers of $V$, and its distance from that set tends to zero in probability as $\varepsilon\rightarrow0$. The limiting measure is compact, carried by immediate-departure Filippov paths, and, under a Lipschitz radial-graph condition, purely singular with Hausdorff dimension at most $d-1$. A continuous-mapping criterion for exit-time convergence is also established.

math.PR↗

Asymptotic optimality of dynamic first-fit packing on the half-axis

We revisit a classical problem in dynamic storage allocation. Items arrive in a linear storage medium, modeled as a half-axis, at a Poisson rate $r$ and depart after an independent exponentially distributed unit mean service time. The arriving item sizes (lengths) are assumed to be independent and identically distributed (i.i.d.) from a common distribution $H$. A widely employed algorithm for allocating the items is the "first-fit" discipline, namely, each arriving item is placed in the left-most vacant interval large enough to accommodate it. In a seminal 1985 paper, Coffman, Kadota, and Shepp ([6]) proved that in the special case of unit length items (i.e. degenerate $H$), as $r$ tends towards infinity, the first-fit algorithm is asymptotically optimal in the following sense: the steady-state ratio of expected "empty space" (gaps between items) to expected occupied space tends towards $0$. In a sequel to [6], Coffman, Kadota, and Shepp ([5]) conjectured that the first-fit discipline is also asymptotically optimal for non-degenerate $H$. In this paper we provide the first proof of first-fit asymptotic optimality for non-degenerate distributions $H$ of item sizes. Our main result is for the case when $H$ is concentrated on countably many positive real sizes forming an increasing sequence that is either finite or goes to infinity, with the average item size being finite. We prove that under the first-fit discipline, as $r$ tends towards infinity, the steady-state packing configuration (scaled down by $r$) converges in distribution to the limiting packing configuration with smaller items on the left, larger items on the right, and with no gaps between. In particular, this proves asymptotic optimality of first-fit in the following sense: if $P$ is the expected occupied space, then in steady-state the empty space (scaled down by $r$) in $[0,P]$ vanishes.

math.PR↗

Annealed almost periodic entropy

This work studies certain notions of entropy that can be associated to (i) a representation of a separable, unital C*-algebra $\mathfrak{A}$ and (ii) an auxiliary random sequence $(π_n)_{n\ge 1}$ of finite-dimensional representations of $\mathfrak{A}$. This continues a previous research program into the properties of these entropy notions when each $π_n$ is deterministic, which uncovered a range of analogies with entropy in ergodic theory and also with non-commutative generalizations of Szegő's limit theorems. We associate two new notions of entropy to data as in (i) and (ii) above: `annealed' AP entropy, which is roughly a kind of first-moment average of deterministic AP entropies; and `zeroth-order' AP entropy, which controls the large deviations probabilities that certain positive definite functions appear in the representations $π_n$ at all. After developing some of this general theory, we then focus on the special case in which $\mathfrak{A}$ is the group C*-algebra of a finitely-generated free group and each $π_n$ is generated by choosing a tuple of $n$-by-$n$ unitary matrices independently at random from Haar measure. In that case, explicit formulas can be derived for some of our notions of entropy, and new large deviations principles in random matrix theory are obtained as a consequence.

math.PR↗