6 research outputs found
Understanding Search Trees via Statistical Physics
We study the random m-ary search tree model (where m stands for the number of
branches of a search tree), an important problem for data storage in computer
science, using a variety of statistical physics techniques that allow us to
obtain exact asymptotic results. In particular, we show that the probability
distributions of extreme observables associated with a random search tree such
as the height and the balanced height of a tree have a traveling front
structure. In addition, the variance of the number of nodes needed to store a
data string of a given size N is shown to undergo a striking phase transition
at a critical value of the branching ratio m_c=26. We identify the mechanism of
this phase transition, show that it is generic and occurs in various other
problems as well. New results are obtained when each element of the data string
is a D-dimensional vector. We show that this problem also has a phase
transition at a critical dimension, D_c= \pi/\sin^{-1}(1/\sqrt{8})=8.69363...Comment: 11 pages, 8 .eps figures included. Invited contribution to
STATPHYS-22 held at Bangalore (India) in July 2004. To appear in the
proceedings of STATPHYS-2
Continuum Cascade Model of Directed Random Graphs: Traveling Wave Analysis
We study a class of directed random graphs. In these graphs, the interval
[0,x] is the vertex set, and from each y\in [0,x], directed links are drawn to
points in the interval (y,x] which are chosen uniformly with density one. We
analyze the length of the longest directed path starting from the origin. In
the large x limit, we employ traveling wave techniques to extract the
asymptotic behavior of this quantity. We also study the size of a cascade tree
composed of vertices which can be reached via directed paths starting at the
origin.Comment: 12 pages, 2 figures; figure adde
Phase Transition in the Aldous-Shields Model of Growing Trees
We study analytically the late time statistics of the number of particles in
a growing tree model introduced by Aldous and Shields. In this model, a cluster
grows in continuous time on a binary Cayley tree, starting from the root, by
absorbing new particles at the empty perimeter sites at a rate proportional to
c^{-l} where c is a positive parameter and l is the distance of the perimeter
site from the root. For c=1, this model corresponds to random binary search
trees and for c=2 it corresponds to digital search trees in computer science.
By introducing a backward Fokker-Planck approach, we calculate the mean and the
variance of the number of particles at large times and show that the variance
undergoes a `phase transition' at a critical value c=sqrt{2}. While for
c>sqrt{2} the variance is proportional to the mean and the distribution is
normal, for c<sqrt{2} the variance is anomalously large and the distribution is
non-Gaussian due to the appearance of extreme fluctuations. The model is
generalized to one where growth occurs on a tree with branches and, in this
more general case, we show that the critical point occurs at c=sqrt{m}.Comment: Latex 17 pages, 6 figure
Simple solvable models with strong long-range correlations
We consider a set of independent and identically distributed (i.i.d)
random variables whose common distribution has a
parameter (or a set of parameters) which itself is random with its own
distribution. For a fixed value of this parameter , the variables are
independent and we call them conditionally independent and identically
distributed (c.i.i.d). However, once integrated over the distribution of the
parameter , the variables get strongly correlated, yet retaining a
solvable structure for various observables, such as for the sum and the
extremes of 's. This provides a simple recipe to generate a class of
solvable strongly correlated systems. We illustrate how this recipe works via
three physical examples where particles on a line perform independent (i)
Brownian motions, (ii) ballistic motions with random initial velocities, and
(iii) L\'evy flights, but they get strongly correlated via simultaneous
resetting to the origin. Our results are verified in numerical simulations.
This recipe can be used to generate an endless variety of solvable strongly
correlated systems.Comment: 24 pages, 9 figure