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J. R. Norris

Publications and source records attributed to J. R. Norris.

7 recordsLinked to original sources
arXiv2013

Averaging over fast variables in the fluid limit for Markov chains: Application to the supermarket model with memory

We set out a general procedure which allows the approximation of certain Markov chains by the solutions of differential equations. The chains considered have some components which oscillate rapidly and randomly, while others are close to deterministic. The limiting dynamics are obtained by averaging the drift of the latter with respect to a local equilibrium distribution of the former. Some general estimates are proved under a uniform mixing condition on the fast variable which give explicit error probabilities for the fluid approximation. Mitzenmacher, Prabhakar and Shah [In Proc. 43rd Ann. Symp. Found. Comp. Sci. (2002) 799-808, IEEE] introduced a variant with memory of the "join the shortest queue" or "supermarket" model, and obtained a limit picture for the case of a stable system in which the number of queues and the total arrival rate are large. In this limit, the empirical distribution of queue sizes satisfies a differential equation, while the memory of the system oscillates rapidly and randomly. We illustrate our general fluid limit estimate by giving a proof of this limit picture.

math.PR↗
arXiv2010

Homogenization for advection-diffusion in a perforated domain

The volume of a Wiener sausage constructed from a diffusion process with periodic, mean-zero, divergence-free velocity field, in dimension 3 or more, is shown to have a non-random and positive asymptotic rate of growth. This is used to establish the existence of a homogenized limit for such a diffusion when subject to Dirichlet conditions on the boundaries of a sparse and independent array of obstacles. There is a constant effective long-time loss rate at the obstacles. The dependence of this rate on the form and intensity of the obstacles and on the velocity field is investigated. A Monte Carlo algorithm for the computation of the volume growth rate of the sausage is introduced and some numerical results are presented for the Taylor--Green velocity field.

math.PR↗
arXiv2008

Differential equation approximations for Markov chains

We formulate some simple conditions under which a Markov chain may be approximated by the solution to a differential equation, with quantifiable error probabilities. The role of a choice of coordinate functions for the Markov chain is emphasised. The general theory is illustrated in three examples: the classical stochastic epidemic, a population process model with fast and slow variables, and core-finding algorithms for large random hypergraphs.

math.PR↗
arXiv2005

Structure of large random hypergraphs

The theme of this paper is the derivation of analytic formulae for certain large combinatorial structures. The formulae are obtained via fluid limits of pure jump-type Markov processes, established under simple conditions on the Laplace transforms of their Levy kernels. Furthermore, a related Gaussian approximation allows us to describe the randomness which may persist in the limit when certain parameters take critical values. Our method is quite general, but is applied here to vertex identifiability in random hypergraphs. A vertex v is identifiable in n steps if there is a hyperedge containing v all of whose other vertices are identifiable in fewer steps. We say that a hyperedge is identifiable if every one of its vertices is identifiable. Our analytic formulae describe the asymptotics of the number of identifiable vertices and the number of identifiable hyperedges for a Poisson(β) random hypergraph Λon a set V of N vertices, in the limit as N\to \infty. Here βis a formal power series with nonnegative coefficients β_0,β_1,..., and (Λ(A))_{A\subseteq V} are independent Poisson random variables such that Λ(A), the number of hyperedges on A, has mean Nβ_j/\pmatrixN j whenever |A|=j.

math.PR↗
arXiv2004

Continuous and discontinuous phase transitions in hypergraph processes

Let V denote a set of N vertices. To construct a "hypergraph process", create a new hyperedge at each event time of a Poisson process; the cardinality K of this hyperedge is random, with arbitrary probability generating function r(x), except that we assume P(K=1) +P(K=2) > 0. Given K=k, the k vertices appearing in the new hyperedge are selected uniformly at random from V. Hyperedges of cardinality 1 are called patches, and serve as a way of selecting root vertices. Identifiable vertices are those which are reachable from these root vertices, in a strong sense which generalizes the notion of graph component. Hyperedges are also called identifiable if all of their vertices are identifiable. We use "fluid limit" scaling: hyperedges arrive at rate N, and we study structures of size O(1) and O(N). After division by N, numbers of identifiable vertices and reducible hyperedges exhibit phase transitions, which may be continuous or discontinuous depending on the shape of the structure function -log(1 - x)/r'(x), for x in (0,1). Both the case P(K=1) > 0 and the case P(K=1) = 0 < P(K=2) are considered; for the latter, a single extraneous patch is added to mark the root vertex.

math.PR↗
arXiv2004

Structure of large random hypergraphs

The theme of this paper is the derivation of analytic formulae for certain large combinatorial structures. The formulae are obtained via fluid limits of pure jump type Markov processes, established under simple conditions on the Laplace transforms of their Levy kernels. Furthermore, a related Gaussian approximation allows us to describe the randomness which may persist in the limit when certain parameters take critical values. Our method is quite general, but is applied here to vertex identifiability in random hypergraphs. A vertex v is identifiable in n steps if there is a hyperedge containing v all of whose other vertices are identifiable in fewer than n steps. We say that a hyperedge is identifiable if every one of its vertices is identifiable. Our analytic formulae describe the asymptotics of the number of identifiable vertices and the number of identifiable hyperedges for a Poisson random hypergraph on a set of N vertices, in the limit as N goes to infinity.

math.PR↗