## A Guide to Algorithm Design

Author: Anne Benoit
Publisher: CRC Press
ISBN: 1439898138
Format: PDF, ePub, Mobi

Presenting a complementary perspective to standard books on algorithms, A Guide to Algorithm Design: Paradigms, Methods, and Complexity Analysis provides a roadmap for readers to determine the difficulty of an algorithmic problem by finding an optimal solution or proving complexity results. It gives a practical treatment of algorithmic complexity and guides readers in solving algorithmic problems. Divided into three parts, the book offers a comprehensive set of problems with solutions as well as in-depth case studies that demonstrate how to assess the complexity of a new problem. Part I helps readers understand the main design principles and design efficient algorithms. Part II covers polynomial reductions from NP-complete problems and approaches that go beyond NP-completeness. Part III supplies readers with tools and techniques to evaluate problem complexity, including how to determine which instances are polynomial and which are NP-hard. Drawing on the authors’ classroom-tested material, this text takes readers step by step through the concepts and methods for analyzing algorithmic complexity. Through many problems and detailed examples, readers can investigate polynomial-time algorithms and NP-completeness and beyond.

## Randomized Algorithms

Author: Rajeev Motwani
Publisher: Cambridge University Press
ISBN: 1139643134
Format: PDF, Mobi

For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.

## The R Book

Author: Michael J. Crawley
Publisher: John Wiley & Sons
ISBN: 1118448960
Format: PDF, Docs

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

## A Programmer s Companion to Algorithm Analysis

Author: Ernst L. Leiss
Publisher: CRC Press
ISBN: 1420011707
Format: PDF, Kindle

Until now, no other book examined the gap between the theory of algorithms and the production of software programs. Focusing on practical issues, A Programmer's Companion to Algorithm Analysis carefully details the transition from the design and analysis of an algorithm to the resulting software program. Consisting of two main complementary parts, the book emphasizes the concrete aspects of translating an algorithm into software that should perform based on what the algorithm analysis indicated. In the first part, the author describes the idealized universe that algorithm designers inhabit while the second part outlines how this ideal can be adapted to the real world of programming. The book explores analysis techniques, including crossover points, the influence of the memory hierarchy, implications of programming language aspects, such as recursion, and problems arising from excessively high computational complexities of solution methods. It concludes with four appendices that discuss basic algorithms; memory hierarchy, virtual memory management, optimizing compilers, and garbage collection; NP-completeness and higher complexity classes; and undecidability in practical terms. Applying the theory of algorithms to the production of software, A Programmer's Companion to Algorithm Analysis fulfills the needs of software programmers and developers as well as students by showing that with the correct algorithm, you can achieve a functional software program.

## Computer Science Handbook Second Edition

Author: Allen B. Tucker
Publisher: Chapman and Hall/CRC
ISBN: 9781584883609
Format: PDF, ePub

When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chapters either new or significantly revised, the Computer Science Handbook, Second Edition is exactly the kind of reference you need. This rich collection of theory and practice fully characterizes the current state of the field and conveys the modern spirit, accomplishments, and direction of computer science. Highlights of the Second Edition: Coverage that reaches across all 11 subject areas of the discipline as defined in Computing Curricula 2001, now the standard taxonomy More than 70 chapters revised or replaced Emphasis on a more practical/applied approach to IT topics such as information management, net-centric computing, and human computer interaction More than 150 contributing authors--all recognized experts in their respective specialties New chapters on: cryptography computational chemistry computational astrophysics human-centered software development cognitive modeling transaction processing data compression scripting languages event-driven programming software architecture

## Petascale Computing

Publisher: CRC Press
ISBN: 9781584889106
Format: PDF, ePub, Docs

Although the highly anticipated petascale computers of the near future will perform at an order of magnitude faster than today’s quickest supercomputer, the scaling up of algorithms and applications for this class of computers remains a tough challenge. From scalable algorithm design for massive concurrency toperformance analyses and scientific visualization, Petascale Computing: Algorithms and Applications captures the state of the art in high-performance computing algorithms and applications. Featuring contributions from the world’s leading experts in computational science, this edited collection explores the use of petascale computers for solving the most difficult scientific and engineering problems of the current century. Covering a wide range of important topics, the book illustrates how petascale computing can be applied to space and Earth science missions, biological systems, weather prediction, climate science, disasters, black holes, and gamma ray bursts. It details the simulation of multiphysics, cosmological evolution, molecular dynamics, and biomolecules. The book also discusses computational aspects that include the Uintah framework, Enzo code, multithreaded algorithms, petaflops, performance analysis tools, multilevel finite element solvers, finite element code development, Charm++, and the Cactus framework. Supplying petascale tools, programming methodologies, and an eight-page color insert, this volume addresses the challenging problems of developing application codes that can take advantage of the architectural features of the new petascale systems in advance of their first deployment.

## The Garbage Collection Handbook

Author: Richard Jones
Publisher: CRC Press
ISBN: 1315388014
Format: PDF, Kindle

## Algorithms and Theory of Computation Handbook Second Edition

Author: Mikhail J. Atallah
Publisher: Chapman and Hall/CRC
ISBN: 9781584888222
Format: PDF, Docs

Algorithms and Theory of Computation Handbook, Second Edition: General Concepts and Techniques provides an up-to-date compendium of fundamental computer science topics and techniques. It also illustrates how the topics and techniques come together to deliver efficient solutions to important practical problems. Along with updating and revising many of the existing chapters, this second edition contains four new chapters that cover external memory and parameterized algorithms as well as computational number theory and algorithmic coding theory. This best-selling handbook continues to help computer professionals and engineers find significant information on various algorithmic topics. The expert contributors clearly define the terminology, present basic results and techniques, and offer a number of current references to the in-depth literature. They also provide a glimpse of the major research issues concerning the relevant topics.

## Handbook of Scheduling

Author: Joseph Y-T. Leung
Publisher: CRC Press
ISBN: 9780203489802
Format: PDF

Researchers in management, industrial engineering, operations, and computer science have intensely studied scheduling for more than 50 years, resulting in an astounding body of knowledge in this field. Handbook of Scheduling: Algorithms, Models, and Performance Analysis, the first handbook on scheduling, provides full coverage of the most recent and advanced topics on the subject. It assembles researchers from all relevant disciplines in order to facilitate cross-fertilization and create new scheduling insights. The book comprises six major parts, each of which breaks down into smaller chapters: · Part I introduces materials and notation, with tutorials on complexity theory and algorithms for the minimization of makespan, total completion time, dual objectives, maximum lateness, the number of late jobs, and total tardiness. · Part II is devoted to classical scheduling problems. · Part III explores scheduling models that originate in computer science, operations research, and management science. · Part IV examines scheduling problems that arise in real-time systems, focusing on meeting hard deadline constraints while maximizing machine utilization. · Part V discusses stochastic scheduling and queueing networks, highlighting jobs that are not deterministic. · Part VI covers applications, discussing scheduling problems in airline, process, and transportation industries, as well as in hospitals and educational institutions.

## Ensemble Methods

Author: Zhi-Hua Zhou
Publisher: CRC Press
ISBN: 1439830037
Format: PDF, ePub

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.