This is a guidebook for those who want to use computational experiments to support their work in algorithm design and analysis. Numerous case studies and examples show how to apply these concepts. All the necessary concepts in computer architecture and data analysis are covered so that the book can be used by anyone who has taken a course or two in data structures and algorithms.
The 11th International Conference on the Principles and Practice of Constraint Programming (CP 2005) was held in Sitges (Barcelona), Spain, October 1-5, 2005. Information about the conference can be found on the web at http://www.iiia.csic.es/cp2005/.Informationaboutpastconferencesinthe series can be found athttp://www.cs.ualberta.ca/~ai/cp/. The CP conference series is the premier international conference on c- straint programming and is held annually. The conference is concerned with all aspects of computing with constraints, including: algorithms, applications, environments, languages, models and systems. This year, we received 164 submissions. All of the submitted papers received atleastthreereviews, andthepapersandtheirreviewswerethenextensivelyd- cussed during an online Program Committee meeting. As a result, the Program Committee chose 48 (29.3%) papers to be published in full in the proceedings and a further 22 (13.4%)papers to be published as short papers.The full papers werepresentedattheconferencein twoparalleltracksandtheshortpaperswere presented as posters during a lively evening session. Two papers were selected by a subcommittee of the ProgramCommittee--consisting of Chris Beck, Gilles Pesant, and myself--to receive best paper awards. The conference program also includedexcellentinvitedtalksbyHþ ectorGe?ner, IanHorrocks, FrancescaRossi, and Peter J. Stuckey. As a permanent record, the proceedings contain four-page extended abstracts of the invited talks.
"My absolute favorite for this kind of interview preparation is Steven Skiena’s The Algorithm Design Manual. More than any other book it helped me understand just how astonishingly commonplace ... graph problems are -- they should be part of every working programmer’s toolkit. The book also covers basic data structures and sorting algorithms, which is a nice bonus. ... every 1 – pager has a simple picture, making it easy to remember. This is a great way to learn how to identify hundreds of problem types." (Steve Yegge, Get that Job at Google) "Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. ... Every programmer should read this book, and anyone working in the field should keep it close to hand. ... This is the best investment ... a programmer or aspiring programmer can make." (Harold Thimbleby, Times Higher Education) "It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!" (Cory Bart, University of Delaware) "The is the most approachable book on algorithms I have." (Megan Squire, Elon University) --- This newly expanded and updated third edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficiency. It serves as the primary textbook of choice for algorithm design courses and interview self-study, while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography. NEW to the third edition: -- New and expanded coverage of randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing -- Provides full online support for lecturers, including an improved website component with lecture slides and videos -- Full color illustrations and code instantly clarify difficult concepts -- Includes several new "war stories" relating experiences from real-world applications -- Over 100 new problems, including programming-challenge problems from LeetCode and Hackerrank. -- Provides up-to-date links leading to the best implementations available in C, C++, and Java Additional Learning Tools: -- Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them -- Exercises include "job interview problems" from major software companies -- Highlighted "take home lessons" emphasize essential concepts -- The "no theorem-proof" style provides a uniquely accessible and intuitive approach to a challenging subject -- Many algorithms are presented with actual code (written in C) -- Provides comprehensive references to both survey articles and the primary literature Written by a well-known algorithms researcher who received the IEEE Computer Science and Engineering Teaching Award, this substantially enhanced third edition of The Algorithm Design Manual is an essential learning tool for students and professionals needed a solid grounding in algorithms. Professor Skiena is also the author of the popular Springer texts, The Data Science Design Manual and Programming Challenges: The Programming Contest Training Manual.
Annotation The 31 papers discuss such topics as a model and software architecture for visualizing search results on the World Wide Web, a survey of longest common subsequence algorithms, an effective clustering algorithm to index high dimensional metric spaces, rotation-invariant histogram filters for similarity and distance measures between digital images, a fully compressed pattern matching algorithm for balanced straight-line programs, a word-stemming algorithm for the Spanish language, fast multi- pattern search algorithms for detecting intrusion, implementing document ranking within a logical framework, parallel searches using partitioned inverted files, and detecting prosodic stress and topics in spoken sentences. Subjects are not indexed. Annotation copyrighted by Book News, Inc., Portland, OR.
The Stanford GraphBase: A Platform for Combinatorial Computing represents the first efforts of Donald E. Knuth's preparation for Volume Four of The Art of Computer Programming. The book's first goal is to use examples to demonstrate the art of literate programming. Each example provides a programmatic essay that can be read and enjoyed as readily as it can be interpreted by machines. In these essays/programs, Knuth makes new contributions to several important algorithms and data structures, so the programs are of special interest for their content as well as for their style. The book's second goal is to provide a useful means for comparing combinatorial algorithms and for evaluating methods of combinatorial computing. To this end, Knuth's programs offer standard, freely available sets of data - the Stanford GraphBase - that may be used as benchmarks to test competing methods. The data sets are both interesting in themselves and applicable to a wide variety of problem domains. With objective tests, Knuth hopes to bridge the gap between theoretical computer scientists and programmers who have real problems to solve. As with all of Knuth's writings, this book is appreciated not only for the author's unmatched insight, but also for the fun and the challenge of his work. He illustrates many of the most significant and most beautiful combinatorial algorithms that are presently known and provides sample programs that can lead to hours of amusement. In showing how the Stanford GraphBase can generate an almost inexhaustible supply of challenging problems, some of which may lead to the discovery of new and improved algorithms, Knuth proposes friendly competitions. His own initial entries into such competitions are included in the book, and readers are challenged to do better. Features Includes new contributions to our understanding of important algorithms and data structures Provides a standard tool for evaluating combinatorial algorithms Demonstrates a more readable, more practical style of programming Challenges readers to surpass his own efficient algorithms 0201542757B04062001
MMIX is a RISC computer designed by Don Knuth to illustrate machine-level aspects of programming. In the author's book series "The Art of Computer Programming", MMIX replaces the 1960s-style machine MIX. A particular goal in the design of MMIX was to keep its machine language simple, elegant, and easy to learn. At the same time, all of the complexities needed to achieve high performance in practice are taken into account. This book constitutes a collection of programs written in CWEB that make MMIX a virtual reality. Among other utilities, an assembler converting MMIX symbolic files to MMIX objects and two simulators executing the programs in given object files are provided. The latest version of all programs can be downloaded from MMIX's home page. The book provides a complete documentation of the MMIX computer and its assembly language. It also presents mini-indexes, which make the programs much easier to understand. A corrected reprint of the book has been published in August 2014, replacing the version of 1999.
This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.
This book provides glimpses into contemporary research in information systems & technology, learning, artificial intelligence (AI), machine learning, and security and how it applies to the real world, but the ideas presented also span the domains of telehealth, computer vision, the role and use of mobile devices, brain–computer interfaces, virtual reality, language and image processing and big data analytics and applications. Great research arises from asking pertinent research questions. This book reveals some of the authors’ “beautiful questions” and how they develop the subsequent “what if” and “how” questions, offering readers food for thought and whetting their appetite for further research by the same authors.
