Beyond the Worst-Case Analysis of Algorithms

Beyond the Worst-Case Analysis of Algorithms
Author: Tim Roughgarden
Publisher: Cambridge University Press
Total Pages: 705
Release: 2021-01-14
Genre: Computers
ISBN: 1108494315

Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Beyond the Worst-Case Analysis of Algorithms

Beyond the Worst-Case Analysis of Algorithms
Author: Tim Roughgarden
Publisher: Cambridge University Press
Total Pages: 705
Release: 2021-01-14
Genre: Computers
ISBN: 1108786170

There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.

Average Case Analysis of Algorithms on Sequences

Average Case Analysis of Algorithms on Sequences
Author: Wojciech Szpankowski
Publisher: John Wiley & Sons
Total Pages: 580
Release: 2011-10-14
Genre: Mathematics
ISBN: 1118031024

A timely book on a topic that has witnessed a surge of interest over the last decade, owing in part to several novel applications, most notably in data compression and computational molecular biology. It describes methods employed in average case analysis of algorithms, combining both analytical and probabilistic tools in a single volume. * Tools are illustrated through problems on words with applications to molecular biology, data compression, security, and pattern matching. * Includes chapters on algorithms and data structures on words, probabilistic and analytical models, inclusion-exclusion principles, first and second moment methods, subadditive ergodic theorem and large deviations, elements of information theory, generating functions, complex asymptotic methods, Mellin transform and its applications, and analytic poissonization and depoissonization. * Written by an established researcher with a strong international reputation in the field.

Analysis of Algorithms

Analysis of Algorithms
Author: Jeffrey J. McConnell
Publisher: Jones & Bartlett Learning
Total Pages: 471
Release: 2008
Genre: Computers
ISBN: 0763707821

Data Structures & Theory of Computation

Practical Analysis of Algorithms

Practical Analysis of Algorithms
Author: Dana Vrajitoru
Publisher: Springer
Total Pages: 475
Release: 2014-09-03
Genre: Computers
ISBN: 3319098888

This book introduces the essential concepts of algorithm analysis required by core undergraduate and graduate computer science courses, in addition to providing a review of the fundamental mathematical notions necessary to understand these concepts. Features: includes numerous fully-worked examples and step-by-step proofs, assuming no strong mathematical background; describes the foundation of the analysis of algorithms theory in terms of the big-Oh, Omega, and Theta notations; examines recurrence relations; discusses the concepts of basic operation, traditional loop counting, and best case and worst case complexities; reviews various algorithms of a probabilistic nature, and uses elements of probability theory to compute the average complexity of algorithms such as Quicksort; introduces a variety of classical finite graph algorithms, together with an analysis of their complexity; provides an appendix on probability theory, reviewing the major definitions and theorems used in the book.

Twenty Lectures on Algorithmic Game Theory

Twenty Lectures on Algorithmic Game Theory
Author: Tim Roughgarden
Publisher: Cambridge University Press
Total Pages: 356
Release: 2016-08-30
Genre: Computers
ISBN: 1316781178

Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.

Algorithms and Autonomy

Algorithms and Autonomy
Author: Alan Rubel
Publisher: Cambridge University Press
Total Pages: 217
Release: 2021-05-20
Genre: Computers
ISBN: 1108841813

This book examines how algorithms in criminal justice, education, housing, elections and beyond affect autonomy, freedom, and democracy. This title is also available as Open Access on Cambridge Core.

An Introduction to the Analysis of Algorithms

An Introduction to the Analysis of Algorithms
Author: Robert Sedgewick
Publisher: Addison-Wesley
Total Pages: 735
Release: 2013-01-18
Genre: Computers
ISBN: 0133373487

Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure. Improvements and additions in this new edition include Upgraded figures and code An all-new chapter introducing analytic combinatorics Simplified derivations via analytic combinatorics throughout The book’s thorough, self-contained coverage will help readers appreciate the field’s challenges, prepare them for advanced results—covered in their monograph Analytic Combinatorics and in Donald Knuth’s The Art of Computer Programming books—and provide the background they need to keep abreast of new research. "[Sedgewick and Flajolet] are not only worldwide leaders of the field, they also are masters of exposition. I am sure that every serious computer scientist will find this book rewarding in many ways." —From the Foreword by Donald E. Knuth

The Design and Analysis of Algorithms

The Design and Analysis of Algorithms
Author: Dexter C. Kozen
Publisher: Springer Science & Business Media
Total Pages: 327
Release: 2012-12-06
Genre: Computers
ISBN: 1461244005

These are my lecture notes from CS681: Design and Analysis of Algo rithms, a one-semester graduate course I taught at Cornell for three consec utive fall semesters from '88 to '90. The course serves a dual purpose: to cover core material in algorithms for graduate students in computer science preparing for their PhD qualifying exams, and to introduce theory students to some advanced topics in the design and analysis of algorithms. The material is thus a mixture of core and advanced topics. At first I meant these notes to supplement and not supplant a textbook, but over the three years they gradually took on a life of their own. In addition to the notes, I depended heavily on the texts • A. V. Aho, J. E. Hopcroft, and J. D. Ullman, The Design and Analysis of Computer Algorithms. Addison-Wesley, 1975. • M. R. Garey and D. S. Johnson, Computers and Intractibility: A Guide to the Theory of NP-Completeness. w. H. Freeman, 1979. • R. E. Tarjan, Data Structures and Network Algorithms. SIAM Regional Conference Series in Applied Mathematics 44, 1983. and still recommend them as excellent references.