Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

Computational geometry is concerned with the design and analysis of algorithms for geometrical problems. In addition, other more practically oriented, areas of computer science— such as computer graphics, computer-aided design, robotics, pattern recognition, and operations research—give rise to problems that inherently are geometrical. This is one reason computational geometry has attracted enormous research interest in the past decade and is a well-established area today. (For standard sources, we refer to the survey article by Lee and Preparata [19841 and to the textbooks by Preparata and Shames [1985] and Edelsbrunner [1987bl.) Readers familiar with the literature of computational geometry will have noticed, especially in the last few years, an increasing interest in a geometrical construct called the Voronoi diagram. This trend can also be observed in combinatorial geometry and in a considerable number of articles in natural science journals that address the Voronoi diagram under different names specific to the respective area. Given some number of points in the plane, their Voronoi diagram divides the plane according to the nearest-neighbor

Voronoi diagrams—a survey of a fundamental geometric data structure

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

This book offers a modern approach to computational geo- metry, an area thatstudies the computational complexity of geometric problems. Combinatorial investigations play an important role in this study.

Algorithms in Combinatorial Geometry

Urban Transportation Networks: Equilibrium Analysis With Mathematical Programming Methods

We propose an algorithm to compute the set of individual (nonnegligible) Poisson probabilities, rigorously bound truncation error, and guarantee no overflow or underflow. Work and space requirements are modest, both proportional to the square root of the Poisson parameter. Our algorithm appears numerically stable. We know no other algorithm with all these (good) features. Our algorithm speeds generation of truncated Poisson variates and the computation of expected terminal reward in continuous-time, uniformizable Markov chains. More generally, our algorithm can be used to evaluate formulas involving Poisson probabilities.

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Computing Poisson probabilities

Construction of Voronoi Polyhedra

We present a new family of shortest-route methods, which reduce an upper bound on running time, and make empirical comparisons for a certain class of networks. These methods also allow for exploitation of structure by pruning arcs and/or nodes.

Shortest-Route Methods: 1. Reaching, Pruning, and Buckets

Markov renewal programming is treated by linear fractional programming. Particular attention is given to the resolution of tied policies that minimize expected cost per unit time. The multichain case is handled by a decomposition approach.

Markov Renewal Programming by Linear Fractional Programming

We integrate tabu search, simulated annealing, genetic algorithms, and random restarting. In addition, while simulating the original Markov chain (defined on a state space tailored either to stand-alone simulated annealing or to the hybrid scheme) with the original cooling schedule implicitly, we speed up both stand-alone simulated annealing and the combination by a factor going to infinity as the number of transitions generated goes to infinity. Beyond this, speedup nearly linear in the number of independent parallel processors often can be expected.

Bennett L. Fox

Papers

Computing Poisson probabilities

Construction of Voronoi Polyhedra

Shortest-Route Methods: 1. Reaching, Pruning, and Buckets

Markov Renewal Programming by Linear Fractional Programming

Integrating and accelerating tabu search, simulated annealing, and genetic algorithms