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In this chapter, we consider the processes in a homogeneous space of a Lie group G induced by Lévy processes in G. In Section 2.1, these processes are introduced as one-point motions of Lévy processes in Lie groups. We derive the stochastic integral equations satisfied by these processes and discuss their Markov property. In Section 2.2, we consider the Markov processes in a homogeneous space of G that are invariant under the action of G. We study the relations among various invariance properties, we present Hunt's result on the generators of G-invariant processes, and we show that these processes are one-point motions of left Lévy processes in G that are also invariant under the right action of the isotropy subgroup. The last section of this chapter contains a discussion of Riemannian Brownian motions in Lie groups and homogeneous spaces.
One-Point Motions
Let G be a Lie group that acts on a manifold M on the left and let gt be a process in G. For any x ∈ M, the process xt = gtx will be called the one-point motion of gt in M starting from x. In general, the one-point motion of a Markov process in G is not a Markov process in M.
This chapter contains an introduction to Lévy processes in a general Lie group. The left and right Lévy processes in a topological group G are defined in Section 1.1. They can be constructed from a convolution semigroup of probability measures on G and are Markov processes with left or right invariant Feller transition semigroups. In the next two sections, we introduce Hunt's theorem for the generator of a Lévy process in a Lie group G and prove some related results for the Lévy measure determined by the jumps of the process. In Section 1.4, the Lévy process is characterized as a solution of a stochastic integral equation driven by a Brownian motion and an independent Poisson random measure whose characteristic measure is the Lévy measure. Some variations and extensions of this stochastic integral equation are discussed. The proofs of the stochastic integral equation characterization, due to Applebaum and Kunita, and of Hunt's theorem, will be given in Chapter 3. For Lévy processes in matrix groups, a more explicit stochastic integral equation, written in matrix form, is obtained in Section 1.5.
Lévy Processes
The reader is referred to Appendices A and B for the basic definitions and facts on Lie groups, stochastic processes, and stochastic analysis.
The present volume provides an introduction to Lévy processes in general Lie groups, and hopefully an accessible account on the limiting and dynamical properties of such processes in semi-simple Lie groups of non-compact type. Lévy processes in Euclidean spaces, including the famous Brownian motion, have always played a central role in probability theory. In recent times, there has been intense research activity in exploring the probabilistic connections of various algebraic and geometric structures, therefore, the study of stochastic processes in Lie groups has become increasingly important. This book is aimed at serving two purposes. First, it may provide a foundation to the theory of Lévy processes in Lie groups, as this is perhaps the first book written on the subject, and second it will present some important results in this area, revealing an interesting connection between probability and Lie groups.
Please note: when referring to a result in a referenced text, the chapter and enunciation numbering system in that text has been followed.
David Applebaum, Olav Kallenberg and Wang Longmin read portions of the manuscript and provided useful comments. Part of the book was written during the author's visit to Nankai University, Tianjin, China in the fall of 2002. I wish to take this opportunity to thank my hosts, Wu Rong and Zhou Xingwei, for their hospitality. It would be hard to imagine this work ever being completed without my wife's support and understanding.
In this chapter, we apply Fourier analysis to study the distributions of Lévy processes gt in compact Lie groups. After a brief review of the Fourier analysis on compact Lie groups based on the Peter—Weyl theorem, we discuss in Section 4.2 the Fourier expansion of the distribution density pt of a Lévy process gt in terms of matrix elements of irreducible unitary representations of G. It is shown that if gt has an L2 density pt, then the Fourier series converges absolutely and uniformly on G, and the coefficients tend to 0 exponentially as time t → ∞. In Section 4.3, for Lévy processes invariant under the inverse map, the L2 distribution density is shown to exist, and the exponential bounds for the density as well as the exponential convergence of the distribution to the normalized Haar measure are obtained. The same results are proved in Section 4.4 for conjugate invariant Lévy processes. In this case, the Fourier expansion is given in terms of irreducible characters, a more manageable form of Fourier series. An example on the special unitary group SU(2) is computed explicitly in the last section. The results of this chapter are taken from Liao [43].
Fourier Analysis on Compact Lie Groups
This section is devoted to a brief discussion of Fourier series of L2 functions on a compact Lie group G based on the Peter—Weyl theorem.
Let graph $G=(V,E)$ and integer $b\geq 1$ be given. A set $S\subseteq V$ is said to be $b$-independent if $u,v\in S$ implies $d_G(u,v)>b$, where $d_G(u,v)$ is the shortest distance between $u$ and $v$ in $G$. The $b$-independence number $\a_b(G)$ is the size of the largest $b$-independent subset of $G$. When $b=1$ this reduces to the standard definition of independence number.
We study this parameter in relation to the random graph $G_{n,p},\,p=d/n$, in particular, when $d$ is a large constant. We show that w.h.p. if $d\geq d_{\epsilon, b}$, $$ \left| \alpha_b(G_{n,p}) - \frac{2bn}{d^b} \biggl(\log{d} - \frac{\log{\log{d}}}{b} - \frac{\log{2b}}{b} + \frac{1}{b}\biggr)\right| \leq \frac{\epsilon n}{d^b}.$$
Improving an old result of Clarkson, Edelsbrunner, Guibas, Sharir and Welzl, we show that the number of distinct distances determined by a set $P$ of $n$ points in three-dimensional space is $\Omega(n^{77/141-\varepsilon})=\Omega(n^{0.546})$, for any $\varepsilon>0$. Moreover, there always exists a point $p\in P$ from which there are at least so many distinct distances to the remaining elements of $P$. The same result holds for points on the three-dimensional sphere. As a consequence, we obtain analogous results in higher dimensions.
We show that for every $\varepsilon\,{>}\,0$ there exists an $r_0\,{=}\,r_0(\varepsilon)$ such that, for all integers $r\,{\ge}\, r_0$, every graph of average degree at least $r+\varepsilon$ and girth at least 1000 contains a subdivision of $K_{r+2}$. Combined with a result of Mader this implies that, for every $\varepsilon\,{>}\,0$, there exists an $f(\varepsilon)$ such that, for all $r\,{\ge}\, 2$, every graph of average degree at least $r+\varepsilon$ and girth at least $f(\varepsilon)$ contains a subdivision of $K_{r+2}$. We also prove a more general result concerning subdivisions of arbitrary graphs.
Using representation theory, we obtain a necessary and sufficient condition for a discrete-time Markov chain on a finite state space $E$ to be representable as $$\Psi_n \Psi_{n-1} \cdots \Psi_1 z,\quad n \geq 0,$$ for any $z \in E$, where the $\Psi_i$ are independent, identically distributed random permutations taking values in some given transitive group of permutations on $E$. The condition is particularly simple when the group is 2-transitive on $E$. We also work out the explicit form of our condition for the dihedral group of symmetries of a regular polygon.
What simple condition on a graph $G$ will ensure that $G\,{\succ}\,K_t$? As usual, $G\,{\succ}\,K_t$ means that $K_t$ is a minor of the graph $G$ (in other words, $G$ has vertex disjoint connected subgraphs $W_1,\ldots,W_t$ and at least one edge between $W_i$ and $W_j$, $1\,{\le}\, i<j\,{\le}\, t$).
We compute the fat-shattering function and the level fat-shattering function for important classes of affine functions. We observe that the level fat-shattering function and the fat-shattering function are identical for these classes. In addition we observe that the notion that adding the constant term to linear functions increases the dimension by at most 1 is incorrect for fat-shattering and level fat-shattering.
In this paper we prove several point selection theorems concerning objects ‘spanned’ by a finite set of points. For example, we show that for any set $P$ of $n$ points in $\R^2$ and any set $C$ of $m \,{\geq}\, 4n$ distinct pseudo-circles, each passing through a distinct pair of points of $P$, there is a point in $P$ that is covered by (i.e., lies in the interior of) $\Omega(m^2/n^2)$ pseudo-circles of $C$. Similar problems involving point sets in higher dimensions are also studied.
Most of our bounds are asymptotically tight, and they improve and generalize results of Chazelle, Edelsbrunner, Guibas, Hershberger, Seidel and Sharir [8], where weaker bounds for some of these cases were obtained.
We give results on the strong connectivity for spaces of sparse random digraphs specified by degree sequence. A full characterization is provided, in probability, of the fan-in and fan-out of all vertices including the number of vertices with small ($o(n)$) and large ($cn$) fan-in or fan-out. We also give the size of the giant strongly connected component, if any, and the structure of the bow-tie digraph induced by the vertices with large fan-in or fan-out. Our results follow a direct analogy of the extinction probabilities of classical branching processes.
We look at a model of random graphs suggested by Gilbert: given an integer $n$ and $\delta > 0$, scatter $n$ vertices independently and uniformly on a metric space, and then add edges connecting pairs of vertices of distance less than $\delta$ apart.
We consider the asymptotics when the metric space is the interval [0, 1], and $\delta = \delta(n)$ is a function of $n$, for $n \to \infty$. We prove that every upwards closed property of (ordered) graphs has at least a weak threshold in this model on this metric space. (But we do find a metric space on which some upwards closed properties do not even have weak thresholds in this model.) We also prove that every upwards closed property with a threshold much above connectivity's threshold has a strong threshold. (But we also find a sequence of upwards closed properties with lower thresholds that are strictly weak.)