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Data structures and network algorithms, Tarjan, Robert Endre


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Автор: Tarjan, Robert Endre
Название:  Data structures and network algorithms
Перевод названия: Структуры данных и сетевые алгоритмы
ISBN: 9780898711875
Издательство: Mare Nostrum (Eurospan)
Классификация:





ISBN-10: 0898711878
Обложка/Формат: Paperback
Страницы: 140
Вес: 0.24 кг.
Дата издания: 1984
Серия: Cbms-nsf regional conference series
Иллюстрации: Ill.
Размер: 228 x 152 x 8
Читательская аудитория: Tertiary education (us: college)
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms.

Distributed Algorithms,

Автор: Nancy A. Lynch
Название: Distributed Algorithms,
ISBN: 1558603484 ISBN-13(EAN): 9781558603486
Издательство: Elsevier Science
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Цена: 136990.00 T
Наличие на складе: Поставка под заказ.
Описание: A guide to designing, implementing and analyzing distributed algorithms. It covers problems including resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election and global snapshots.

Combinatorial Optimization (algorithms and Combinatorics 21) 5th ed.

Автор: Korte, Bernhard, Vygen, Jens
Название: Combinatorial Optimization (algorithms and Combinatorics 21) 5th ed.
ISBN: 3642244874 ISBN-13(EAN): 9783642244872
Издательство: Springer
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Цена: 60510.00 T
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Описание: This comprehensive textbook on combinatorial optimization places special emphasis on theoretical results and algorithms with provably good performance, in contrast to heuristics. It is based on numerous courses on combinatorial optimization and specialized topics, mostly at graduate level. This book reviews the fundamentals, covers the classical topics (paths, flows, matching, matroids, NP-completeness, approximation algorithms) in detail, and proceeds to advanced and recent topics, some of which have not appeared in a textbook before. Throughout, it contains complete but concise proofs, and also provides numerous exercises and references. This fifth edition has again been updated, revised, and significantly extended, with more than 60 new exercises and new material on various topics, including Cayley's formula, blocking flows, faster b-matching separation, multidimensional knapsack, multicommodity max-flow min-cut ratio, and sparsest cut. Thus, this book represents the state of the art of combinatorial optimization.

Approximation Algorithms

Автор: Vazirani Vijay V.
Название: Approximation Algorithms
ISBN: 3540653678 ISBN-13(EAN): 9783540653677
Издательство: Springer
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Цена: 65210.00 T
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Описание: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field.

Synthetic aperture radar signal processing with matlab algorithms

Автор: Soumekh, Mehrdad
Название: Synthetic aperture radar signal processing with matlab algorithms
ISBN: 0471297062 ISBN-13(EAN): 9780471297062
Издательство: Wiley
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Цена: 188970.00 T
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Описание: An up-to-date analysis of the SAR wavefront reconstruction signal theory and its digital implementation. With the advent of fast computing and digital information processing techniques, synthetic aperture radar (SAR) technology has become both more powerful and more accurate.

Algorithms for Data Science

Автор: Steele
Название: Algorithms for Data Science
ISBN: 3319457950 ISBN-13(EAN): 9783319457956
Издательство: Springer
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Цена: 83850.00 T
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Описание:

This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.
This book has three parts:
(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.
(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.
(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.


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