In the area of statistical physics, Monte Carlo algorithms based on Markov chain simulation have been in use for many years. The validity of these algorithms depends crucially on the rate of convergence to equilibrium of the Markov chain being simulated. Unfortunately, the classical theory of stochastic processes hardly touches on the sort of non-asymptotic analysis required in this application. As a consequence, it had previously not been possible to make useful, mathematically rigorous statements about the quality of the estimates obtained. Within the last ten years, analytical tools have been devised with the aim of correcting this deficiency. As well as permitting the analysis of Monte Carlo algorithms for classical problems in statistical physics, the introduction of these tools has spurred the development of new approximation algorithms for a wider class of problems in combinatorial enumeration and optimization. The “Markov chain Monte Carlo ” method has been applied to a variety of such problems, and often provides the only known efficient (i.e., polynomial time) solution technique.

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We present algorithms for solving symmetric, diagonally-dominant linear systems to accuracy ɛ in time linear in their number of non-zeros and log(κf (A)/ɛ), where κf (A) isthe condition number of the matrix defining the linear system. Our algorithm applies the preconditioned Chebyshev iteration with preconditioners designed using nearly-linear time algorithms for graph sparsification and graph partitioning.

In the last decade important relations between Laplace eigenvalues and eigenvectors of graphs and several other graph parameters were discovered. In these notes we present some of these results and discuss their consequences. Attention is given to the partition and the isoperimetric properties of graphs, the max-cut problem and its relation to semidefinite programming, rapid mixing of Markov chains, and to extensions of the results to infinite graphs.

.<F3.827e+05> We consider a finite random walk on a weighted graph<F3.539e+05><F3.827e+05> G; we show that the fraction of time spent in a set of vertices<F3.539e+05> A<F3.827e+05> converges to the stationary probability<F3.539e+05><F3.827e+05><F3.539e+05><F3.827e+05> #(A) with error probability exponentially small in the length of the random walk and the square of the size of the deviation from<F3.539e+05><F3.827e+05><F3.539e+05><F3.827e+05> #(A). The exponential bound is in terms of the expansion of<F3.539e+05> G<F3.827e+05> and improves previous results of [D. Aldous,<F3.405e+05> Probab. Engrg. Inform.<F3.827e+05> Sci., 1 (1987), pp. 33--46], [L. Lovasz and M. Simonovits,<F3.405e+05> Random Structures<F3.827e+05> Algorithms, 4 (1993), pp. 359--412], [M. Ajtai, J. Komlos, and E. Szemeredi,<F3.405e+05> Deterministic simulation of<F3.827e+05> logspace, in Proc. 19th ACM Symp. on Theory of Computing, 1987]. We show that taking the sample average from one trajectory gives a more e#cien...

We study the smallest number /(K) such that a given convex body K in IR n can be cut into two parts K 1 and K 2 by a surface with an (n \Gamma 1)-dimensional measure /(K)vol(K 1 ) \Delta vol(K 2 )=vol(K). Let M 1 (K) be the average distance of a point of K from its center of gravity. We prove for the "isoperimetric coefficient" that /(K) ln 2 M 1 (K) ; and give other upper and lower bounds. We conjecture that our upper bound is best possible up to a constant. Our main tool is a general "Localization Lemma" that reduces integral inequalities over the n-dimensional space to integral inequalities in a single variable. This lemma was first proved by two of the authors in an earlier paper, but here we give various extensions and variants that make its application smoother. We illustrate the usefulness of the lemma by showing how a number of well-known results can be proved using it.

Abstract. In 1978, Osserman [124] wrote an extensive survey on the isoperimetric inequality. The Brunn-Minkowski inequality can be proved in a page, yet quickly yields the classical isoperimetric inequality for important classes of subsets of R n, and deserves to be better known. This guide explains the relationship between the Brunn-Minkowski inequality and other inequalities in geometry and analysis, and some applications. 1.

Given a high dimensional convex body K ` IR n by a separation oracle, we can approximate its volume with relative error ", using O (n 5 ) oracle calls. Our algorithm also brings the body into isotropic position. As all previous randomized volume algorithms, we use "rounding" followed by a multiphase Monte-Carlo (product estimator) technique. Both parts rely on sampling (generating random points in K), which is done by random walk. Our algorithm introduces three new ideas: ffl the use of the isotropic position (or at least an approximation of it) for rounding, ffl the separation of global obstructions (diameter) and local obstructions (boundary problems) for fast mixing, and ffl a stepwise interlacing of rounding and sampling. 1 . Introduction For a variety of geometric objects, classical results characterize various geometric parameters. Many of these results are useful even in practical situations: they can easily be transformed into efficient algorithms. Some other theorem...

The notion of conductance introduced by Jerrum and Sinclair [JS] has been widely used to prove rapid mixing of Markov chains. Here we introduce a variant of this - instead of measuring the conductance of the worst subset of states, we show that it is enough to bound a certain weighted average conductance (where the average is taken over subsets of states with different sizes.) In the case of convex bodies, we show that this average conductance is better than the known bounds for the worst case; this helps us save a factor of O(n) which is incurred in all proofs as a "penalty" for a "bad start" (i.e., because the starting distribution may be arbitrary).

This paper is concerned with the efficient sampling of random points from n, where the underlying density F is log-concave (i.e., log F is concave). This is a natural restriction which is satisfied by many common distributions, for example, the multivariate normal. The algorithm we use generates a sample path from a Markov chain whose stationary distribution is (close to) F. The algorithm falls into the class of Metropolis algorithms. It has applications to the problem of computing the volume of convex bodies and in statistics. Recent statistics literature has focused on the many applications of Markov chain Monte Carlo algorithms (see [11]). However, theoretical bounds on the convergence rate of such algorithms have been limited. Using recent developments in the theory of rapidly mixing Markov chains, in particular the notion of conductance [6, 10], Applegate and Kannan [2] proved a bound on the convergence rate of the chain considered in this paper. These theoretical bounds are too large to be useful in practice. In this paper, we prove tighter bounds using an approach related to the classical Poincar inequalities instead of conductance. These bounds are small enough to be useful in practice and have already found application in the generation of random contingency tables [4]. Instead of sampling from the continuum of points in n, we discretize the problem by assuming that n is divided into a set of hypercubes n of side ( is a given small positive real number) and the problem is to choose one of these cubes each with probability proportional to the integral of F over the cube. (If necessary, a sample from the continuum can then be picked by standard rejection sampling techniques from the cube chosen; we omit details of this.) Second, we assume that we have a compact ...