Probabilistic Analysis of Algorithms: On Computing Methodologies for Computer Algorithms Performance Evaluation (Monographs in Computer Science)

Micha Hofri

ISBN 10: 1461291607 / ISBN 13: 9781461291602

Published by Springer New York 1987-01-01, 1987

About this Item

Seller Inventory # 6666-IUK-9781461291602

Bibliographic Details

• Title: Probabilistic Analysis of Algorithms: On Computing Methodologies for Computer Algorithms Performance Evaluation (Monographs in Computer Science)
• Publisher: Springer New York 1987-01-01
• Publication Date: 1987
• Language: English
• ISBN 10: 1461291607
• ISBN 13: 9781461291602
• Binding: Paperback
• Condition: New

About this title

Synopsis

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.