Probabilistic Analysis of Algorithms: On Computing Methodologies for Computer Algorithms Performance Evaluation

ISBN-13: 9781461291602 / Angielski / Miękka / 2011 / 240 str.

Micha Hofri

Opis

Probabilistic Analysis of Algorithms begins with a presentation of the "tools of the trade" currently used in probabilistic analyses, and continues with an applications section in which these tools are used in the analysis ofr selected algorithms. The tools section of the book provides the reader with an arsenal of analytic and numeric computing methods which are then applied to several groups of algorithms to analyze their running time or storage requirements characteristics. Topics covered in the applications section include sorting, communications network protocols and bin packing. While the discussion of the various algorithms is sufficient to motivate their structure, the emphasis throughout is on the probabilistic estimation of their operation under distributional assumptions on their input. Probabilistic Analysis of Algorithms assumes a working knowledge of engineering mathematics, drawing on real and complex analysis, combinatorics and probability theory. While the book is intended primarily as a text for the upper undergraduate and graduate student levels, it contains a wealth of material and should also prove an important reference for researchers. As such it is addressed to computer scientists, mathematicians, operations researchers, and electrical and industrial engineers who are interested in evaluating the probable operation of algorithms, rather than their worst-case behavior.

Spis treści

1 Introduction.- 1.1 Criteria for the Performance and Quality of Algorithms.- 1.2 The Analysis of a Very Simple Algorithm.- 1.3 Comments on Sources and Resources.- Exercises and Complements.- 2 Tools of the Trade.- 2.1 Introduction to Asymptotics.- 2.1.1 Asymptotic Notation.- 2.1.2 Summation Asymptotics.- 2.1.3 Euler’s Summation Formula.- Exercises and Complements.- 2.2 Generating Functions.- 2.2.1 Elementary Properties and Applications.- 2.2.2 Probability gf’s (pgf), Moment gf’s.- 2.2.3 Lagrange Expansion and Applications.- 2.2.4 The Poisson Transform.- Exercises and Complements.- 2.3 Integral Transforms (it’s).- 2.3.1 Laplace Transform.- 2.3.2 Mellin Transform.- 2.3.3 Mellin Summation Formula.- Exercises and Complements.- 2.4 Combinatorial Calculus (The Symbolic Operator Method).- 2.4.1 Elementary Examples.- 2.4.2 Admissible Combinatorial Constructions.- 2.4.3 Operator Methods.- Exercises and Complements.- 2.5 Asymptotics from Generating Functions.- 2.5.1 Complex Functions — Definitions and Theorems.- 2.5.2 Expansions at Singularities.- 2.5.3 Entire Functions.- Exercises and Complements.- 2.6 Selected Results from Probability Theory.- 2.6.1 The Representation of an Algorithm by a Markov Chain.- 2.6.2 Inequalities for Sums of Bounded Random Variables.- 2.6.3 Wald’s Identity.- Exercises and Complements.- 3 Algorithms over Permutations.- 3.1 MAX — Locating the Largest Term in a Permutation.- Exercises and Complements.- 3.2 Representations of Permutations.- 3.2.1 Cycles in a Permutation.- 3.2.2 Inversions.- Exercises and Complements.- 3.3 Analysis of Sorting Algorithms.- 3.3.1 Insertion Sort.- 3.3.2 Shell Sort.- 3.3.3 Linear Probing Sort.- Exercises and Complements.- 4 Algorithms for Communications Networks.- 4.1 The Efficiency of Multiple Connections.- 4.1.1 Disjointly Shared Channels — Capacity Considerations.- 4.1.2 Counting Realizable Configurations.- 4.1.3 Asymptotic Capacity Estimates.- Exercises and Complements.- 4.2 Collision Resolution Stack Algorithms.- 4.2.1 Channel Capacity Analysis.- 4.2.2 Top of Stack Probabilities and Message Delay.- 4.2.3 Message Delay via Renewal Considerations.- 4.2.4 Note on Computations.- Exercises and Complements.- 5 Bin Packing Heuristics.- 5.1 The Next-Fit Bin Packing Algorithm.- 5.1.1 Regularity and Convergence Properties.- 5.1.2 Next-Fit with X ~ U (0,1) — Expected Values.- 5.1.3 Next-Fit with X~ U(0,a) — Expected Values.- 5.1.4 The Distribution of An(NF), when X~ U(0,1).- Exercises and Complements.- 5.2 The Next-Fit-Decreasing Bin Packing Algorithm.- 5.2.1 Direct Evaluation of Bin Requirements.- 5.2.2 Asymptotic Bounds on Moments.- Exercises and Complements.- Appendix A: Binomial Coefficients.- Appendix B: Stirling Numbers.- Appendix C: Inequalities.- References.- Notation Index.