Alistair Sinclair

Approximating the permanent

A randomised approximation scheme for the permanent of a 0–1s presented. The task of estimating a permanent is reduced to that of almost uniformly generating perfect matchings in a graph; the latter is accomplished by simulating a Markov chain whose states are the matchings in the graph. For a wide class of 0–1 matrices the approximation scheme is fully-polynomial, i.e., runs in time polynomial in the size of the matrix and a parameter that controls the accuracy of the output. This class includes all dense matrices (those that contain sufficiently many 1’s) and almost all sparse matrices in some reasonable probabilistic model for 0–1 matrices of given density.For the approach sketched above to be computationally efficient, the Markov chain must be rapidly mixing: informally, it must converge in a short time to its stationary distribution. A major portion of the paper is devoted to demonstrating that the matchings chain is rapidly mixing, apparently the first such result for a Markov chain with genuinely c...

Approximate counting, uniform generation and rapidly mixing Markov chains

The paper studies effective approximate solutions to combinatorial counting and uniform generation problems. Using a technique based on the simulation of ergodic Markov chains, it is shown that, for self-reducible structures, almost uniform generation is possible in polynomial time provided only that randomised approximate counting to within some arbitrary polynomial factor is possible in polynomial time. It follows that, for self-reducible structures, polynomial time randomised algorithms for counting to within factors of the form (1 +n-@) are available either for all fl E R or for no fi E R. A substantial part of the paper is devoted to investigating the rate of convergence of finite ergodic Markov chains, and a simple but powerful characterisation of rapid convergence for a broad class of chains based on a structural property of the underlying graph is established. Finally, the general techniques of the paper are used to derive an almost uniform generation procedure for labelled graphs with a given degree sequence which is valid over a much wider range of degrees than previous methods: this in turn leads to randomised approximate counting algorithms for these graphs with very good

Improved Bounds for Mixing Rates of Marcov Chains and Multicommodity Flow.

The paper is concerned with tools for the quantitative analysis of finite Markov chains whose states are combinatorial structures. Chains of this kind have algorithmic applications in many areas, including random sampling, approximate counting, statistical physics and combinatorial optimisation. The efficiency of the resulting algorithms depends crucially on the mixing rate of the chain, i.e. , the time taken for it to reach its stationary or equilibrium distribution. The paper presents a new upper bound on the mixing rate, based on the solution to a multicommodity flow problem in the Markov chain viewed as a graph. The bound gives sharper estimates for the mixing rate of several important complex Markov chains. As a result, improved bounds are obtained for the runtimes of randomised approximation algorithms for various problems, including computing the permanent of a 0–1 matrix, counting matchings in graphs, and computing the partition function of a ferromagnetic Ising system. Moreover, solutions to the multicommodity flow problem are shown to capture the mixing rate quite closely: thus, under fairly general conditions, a Markov chain is rapidly mixing if and only if it supports a flow of low cost.

Algorithms for random generation and counting: a Markov chain approach

Synopsis.- 1 Preliminaries.- 1.1 Some basic definitions.- 1.2 Notions of tractability.- 1.3 An extended model.- 1.4 Counting, generation and self-reducibility.- 1.5 An interesting class of relations.- 2 Markov chains and rapid mixing.- 2.1 The Markov chain approach to generation problems.- 2.2 Conductance and the rate of convergence.- 2.3 A characterisation of rapid mixing.- 3 Direct Applications.- 3.1 Some simple examples.- 3.2 Approximating the permanent.- 3.3 Monomer-dimer systems.- 3.4 Concluding remarks.- 4 Indirect Applications.- 4.1 A robust notion of approximate counting.- 4.2 Self-embeddable relations.- 4.3 Graphs with specified degrees.- Appendix: Recent developments.

Optimal speedup of Las Vegas algorithms

Abstract Let A be a Las Vegas algorithm, i.e., A is a randomized algorithm that always produces the correct answer when it stops but whose running time is a random variable. We consider the problem of minimizing the expected time required to obtain an answer from A using strategies which simulate A as follows: run A for a fixed amount of time t 1 , then run A independently for a fixed amount of time t 2 , etc. The simulation stops if A completes its execution during any of the runs. Let scL = ( t 1 , t 2 ,…) be a strategy, and let l A = inf scL T ( A , scL ), where T ( A , scL ) i s the expected value of the running time of the simulation of A under strategy scL . We describe a simple universal strategy scL univ , with the property that, for any algorithm A , T ( A , scL univ ) = O( lin A log( linA )). Furthermore, we show that this is the best performance that can be achieved, up to a constant factor, by any universal strategy.

Local divergence of Markov chains and the analysis of iterative load-balancing schemes

We develop a general technique for the quantitative analysis of iterative distributed load balancing schemes. We illustrate the technique by studying two simple, intuitively appealing models that are prevalent in the literature: the diffusive paradigm, and periodic balancing circuits (or the dimension exchange paradigm). It is well known that such load balancing schemes can be roughly modeled by Markov chains, but also that this approximation can be quite inaccurate. Our main contribution is an effective way of characterizing the deviation between the actual loads and the distribution generated by a related Markov chain, in terms of a natural quantity which we call the local divergence. We apply this technique to obtain bounds on the number of rounds required to achieve coarse balancing in general networks, cycles and meshes in these models. For balancing circuits, we also present bounds for the stronger requirement of perfect balancing, or counting.

Markov Chain Algorithms for Planar Lattice Structures

Consider the following Markov chain, whose states are all domino tilings of a 2n× 2n chessboard: starting from some arbitrary tiling, pick a 2×2 window uniformly at random. If the four squares appearing in this window are covered by two parallel dominoes, rotate the dominoes

A polynomial-time approximation algorithm for the permanent of a matrix with non-negative entries

90^{\rm o}

Fast uniform generation of regular graphs

in place. Repeat many times. This process is used in practice to generate a random tiling and is a widely used tool in the study of the combinatorics of tilings and the behavior of dimer systems in statistical physics. Analogous Markov chains are used to randomly generate other structures on various two-dimensional lattices. This paper presents techniques which prove for the first time that, in many interesting cases, a small number of random moves suffice to obtain a uniform distribution.

Improved Bounds for Mixing Rates of Marked Chains and Multicommodity Flow

We present a fully-polynomial randomized approximation scheme for computing the permanent of an arbitrary matrix with non-negative entries.