Past

Levy trees are random continuous trees that are obtained as

scaling limits of Galton-Watson trees. Continuous tree means here real tree, that is a certain class of path-connected metric spaces without cycles. This class of random trees contains in particular the continuum random tree of Aldous that is the limit of the uniform random tree with N vertices and egde length one over the square root of N when N goes to infinity. In this talk I give a precise definition of the Levy trees and I explain some interesting fractal properties of these trees. This talk is based on joint works with J-F Le Gall and M. Winkel available on arxiv : math.PR/0501079 (published in

PTRF) math.PR/0509518 (preprint)

math.PR/0509690 (preprint).

I will present two-sided estimates for the heat kernel of the elliptic

Schr

Taking a view common in the finite element analysis, we interpret

the first N terms of the usual Fourier series solution as the exact

solution of an approximating problem in a subspace spanned by the

eigenfunctions of the underlying Sturm Liouville problem. This view

leads to a consistent solution technique for the heat, wave and

Poisson's equation, and allows an analysis of the error caused by

truncating the Fourier series. Applications to a variety of problems

will be discussed to demonstrate that the analytic approach remains a

valuable complement to purely numerical methods.

The talk is intended for students with an interest in actually

solving partial differential equations. It assumes a standard

background in undergraduate mathematics but not necessarily prior

exposure to the subject. The goal is to show that there is more to

separation of variables than is apparent from standard texts on

engineering mathematics.

It is by now well known that one cannot HEAR the shape of a

drum: There are many known examples of isospectral yet not isometric "drums". Recently we discovered that the sequences of integers formed by counting the nodal domains of successive eigenfunctions encode geometrical information, which can also be used to resolve spectral ambiguities. I shall discuss these sequences and indicate how the information stored in the nodal sequences can be deciphered.

We discuss a method for solving very large structured symmetric indefinite equation systems arising in optimization with interior point methods.

Many real-life economic models involve system dynamics, spatial distribution or uncertainty and lead to large-scale optimization problems. Such problems usually have a hidden structure: they are constructed by replication of some small generic block. The linear algebra subproblems which arise in optimization algorithms for such problems involve matrices which are not only sparse, but they additionally display a block-structure with many smaller blocks sparsely distributed in the large matrix.

We have developed a structure-exploiting parallel interior point solver for optimization problems. Its design uses object-orientated programming techniques. The progress OOPS (Object-Orientated Parallel Solver: http://www.maths.ed.ac.uk/~gondzio/parallel/solver.html) on a number of different computing platforms and achieves scalability on a number of different computing platforms. We illustrate its performance on a collection of problems with sizes reaching 10⁹ variables arising from asset liability management and portfolio optimization.

This is a joint work with Andreas Grothey.

Given a family of independent events in a probability space, the probability

that none of the events occurs is of course the product of the probabilities

that the individual events do not occur. If there is some dependence between the

events, however, then bounding the probability that none occurs is a much less

trivial matter. The Lov

/notices/events/abstracts/stochastic-analysis/mt05/m

In Clarendon Lab

A self-interacting random walk is a random process evolving in an environment depending on its past behaviour.

The notion of Edge-Reinforced Random Walk (ERRW) was introduced in 1986 by Coppersmith and Diaconis [2] on a discrete graph, with the probability of a move along an edge being proportional to the number of visits to this edge. In the same spirit, Pemantle introduced in 1988 [5] the Vertex-Reinforced Random Walk (VRRW), the probability of move to an adjacent vertex being then proportional to the number of visits to this vertex (and not to the edge leading to the vertex). The Self-Interacting Diffusion (SID) is a continuous counterpart to these notions.

Although introduced by similar definitions, these processes show some significantly different behaviours, leading in their understanding to various methods. While the study of ERRW essentially requires some probabilistic tools, corresponding to some local properties, the comprehension of VRRW and SID needs a joint understanding of on one hand a dynamical system governing the general evolution, and on the other hand some probabilistic phenomena, acting as perturbations, and sometimes changing the nature of this dynamical system.

The purpose of our talk is to present our recent results on the subject [1,3,4,6].

Bibliography

[1] M. Bena

Numerous physical systems are justifiably modelled as Markov processes. However,

in practical applications the (usually implicit) assumptions concerning accurate

measurement of the system are often a fair departure from what is possible in

reality. In general, this lack of exact information is liable to render the

In Dept of Statistics

Recently random planar structures, such as planar graphs and outerplanar graphs, have received much attention. Typical questions one would ask about them are the following: how many of them are there, can we sample a random instance uniformly at random, and what properties does a random planar structure have ? To answer these questions we decompose the planar structures along their connectivity. For the asymptotic enumeration we interpret the decomposition in terms of generating funtions and derive the asymptotic number, using singularity analysis. For the exact enumeration and the uniform generation we use the so-called recursive method: We derive recursive counting formulas along the decomposition, which yields a deterministic polynomial time algorithm to sample a planar structure that is uniformly distributed. In this talk we show how to apply these methods to several labeled planar structures, e.g., planar graphs, cubic planar graphs, and outerplanar graphs.

In Dept of Statistics

/notices/events/abstracts/applied-analysis/tt05/Heywood.pdf

A firm issues a convertible bond. At each subsequent time, the bondholder

must decide whether to continue to hold the bond, thereby collecting coupons, or

to convert it to stock. The bondholder wishes to choose a conversion strategy to

maximize the bond value. Subject to some restrictions, the bond can be called by

the issuing firm, which presumably acts to maximize the equity value of the firm

by minimizing the bond value. This creates a two-person game. We show that if

the coupon rate is below the interest rate times the call price, then conversion

should precede call. On the other hand, if the dividend rate times the call

price is below the coupon rate, call should precede conversion. In either case,

the game reduces to a problem of optimal stopping. This is joint work with Mihai

Sirbu.