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Discrete probability theory and the theory of algorithms have become close partners over the last ten years, though the roots of this partnership go back much longer. The papers in this volume address the latest developments in this active field. They are from the IMA Workshops "Probability and Algorithms" and "The Finite Markov Chain Renaissance." They represent the current thinking of many of the world's leading experts in the field.
Researchers and graduate students in probability, computer science, combinatorics, and optimization theory will all be interested in this collection of articles. The techniques developed and surveyed in this volume are still undergoing rapid development, and many of the articles of the collection offer an expositionally pleasant entree into a research area of growing importance.
List of contents
On simulating a Markov chain stationary distribution when transition probabilities are unknown.- A note on network reliability.- Rectangular arrays with fixed margins.- Three examples of Monte-Carlo Markov chains: at the interface between statistical computing, computer science, and statistical mechanics.- The move-to-front rule for self-organizing lists with Markov dependent requests.- The asymptotic lower bound on the diagonal Ramsey numbers: A closer look.- Random walks and undirected graph connectivity: A survey.- Sidon sets with small gaps.- Variations on the monotone subsequence theme of Erd?s and Szekeres.- Randomised approximation schemes for Tutte-Gröthendieck invariants.- Quasi-additive Euclidean functionals.
About the author
JOEL H. SPENCER, PhD, is Professor of Mathematics and Computer Science at the Courant Institute of New York University. He is the cofounder and coeditor of the journal Random Structures and Algorithms and is also a Sloane Foundation Fellow.
