Dynamic algorithms in D.E. Knuth's model: a probabilistic analysis

AbstractBy dynamic algorithms we mean algorithms that operate on dynamically varying data structures (dictionaries, priority queues, linear lists) subject to insertions I, deletions D, positive (negative) queries Q+ (Q−). Let us remember that dictionaries are implementable by unsorted or sorted lists, binary search trees, priority queues by sorted lists, binary search trees, binary tournaments, pagodas, binomial queues and linear lists by sorted or unsorted lists, etc. At this point the following question is very natural in computer science: for a given data structure, which representation is the most efficient? In comparing the space or time costs of two data organizations A and B for the same operations, we cannot merely compare the costs of individual operations for data of given sizes: A may be better than B on some data, and vice versa on others. A reasonable way to measure the efficiency of a data organization is to consider sequences of operations on the structure. Françon (1978, 1979) Knuth (1977) discovered that the number of possibilities for the ith insertion or negative query is equal to i, but that for deletions and positive queries this number depends on the size of the data structure. Answering the questions raised by Françon and Knuth is the main object of this paper more precisely, we show •how to obtain limiting processes;•how to compute explicitly the average costs;•how to obtain variance estimates;•that the costs coverage as n → ∞ to random variables, either Gaussian or depending on Brownian excursion functionals (the limiting distributions are, therefore, completely described).To our knowledge such a complete analysis has never been done before dynamic algorithms in Knuth's model

Large Deviations Analysis for Distributed Algorithms in an Ergodic Markovian Environment

We provide a large deviations analysis of deadlock phenomena occurring in distributed systems sharing common resources. In our model transition probabilities of resource allocation and deallocation are time and space dependent. The process is driven by an ergodic Markov chain and is reflected on the boundary of the d-dimensional cube. In the large resource limit, we prove Freidlin-Wentzell estimates, we study the asymptotic of the deadlock time and we show that the quasi-potential is a viscosity solution of a Hamilton-Jacobi equation with a Neumann boundary condition. We give a complete analysis of the colliding 2-stacks problem and show an example where the system has a stable attractor which is a limit cycle

Exact Maximal Height Distribution of Fluctuating Interfaces

We present an exact solution for the distribution P(h_m,L) of the maximal height h_m (measured with respect to the average spatial height) in the steady state of a fluctuating Edwards-Wilkinson interface in a one dimensional system of size L with both periodic and free boundary conditions. For the periodic case, we show that P(h_m,L)=L^{-1/2}f(h_m L^{-1/2}) for all L where the function f(x) is the Airy distribution function that describes the probability density of the area under a Brownian excursion over a unit interval. For the free boundary case, the same scaling holds but the scaling function is different from that of the periodic case. Numerical simulations are in excellent agreement with our analytical results. Our results provide an exactly solvable case for the distribution of extremum of a set of strongly correlated random variables.Comment: 4 pages revtex (two-column), 1 .eps figure include

Area distribution of the planar random loop boundary

We numerically investigate the area statistics of the outer boundary of planar random loops, on the square and triangular lattices. Our Monte Carlo simulations suggest that the underlying limit distribution is the Airy distribution, which was recently found to appear also as area distribution in the model of self-avoiding loops.Comment: 10 pages, 2 figures. v2: minor changes, version as publishe

Airy Distribution Function: From the Area Under a Brownian Excursion to the Maximal Height of Fluctuating Interfaces

The Airy distribution function describes the probability distribution of the area under a Brownian excursion over a unit interval. Surprisingly, this function has appeared in a number of seemingly unrelated problems, mostly in computer science and graph theory. In this paper, we show that this distribution also appears in a rather well studied physical system, namely the fluctuating interfaces. We present an exact solution for the distribution P(h_m,L) of the maximal height h_m (measured with respect to the average spatial height) in the steady state of a fluctuating interface in a one dimensional system of size L with both periodic and free boundary conditions. For the periodic case, we show that P(h_m,L)=L^{-1/2}f(h_m L^{-1/2}) for all L where the function f(x) is the Airy distribution function. This result is valid for both the Edwards-Wilkinson and the Kardar-Parisi-Zhang interfaces. For the free boundary case, the same scaling holds P(h_m,L)=L^{-1/2}F(h_m L^{-1/2}), but the scaling function F(x) is different from that of the periodic case. We compute this scaling function explicitly for the Edwards-Wilkinson interface and call it the F-Airy distribution function. Numerical simulations are in excellent agreement with our analytical results. Our results provide a rather rare exactly solvable case for the distribution of extremum of a set of strongly correlated random variables. Some of these results were announced in a recent Letter [ S.N. Majumdar and A. Comtet, Phys. Rev. Lett., 92, 225501 (2004)].Comment: 27 pages, 10 .eps figures included. Two figures improved, new discussion and references adde

Varieties of increasing trees

Résumé disponible dans les fichiers attaché