Essential Algorithms

Author: Rod Stephens
Publisher: John Wiley & Sons
ISBN: 1118797299
Format: PDF, ePub, Mobi
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A friendly and accessible introduction to the most useful algorithms Computer algorithms are the basic recipes for programming. Professional programmers need to know how to use algorithms to solve difficult programming problems. Written in simple, intuitive English, this book describes how and when to use the most practical classic algorithms, and even how to create new algorithms to meet future needs. The book also includes a collection of questions that can help readers prepare for a programming job interview. Reveals methods for manipulating common data structures such as arrays, linked lists, trees, and networks Addresses advanced data structures such as heaps, 2-3 trees, B-trees Addresses general problem-solving techniques such as branch and bound, divide and conquer, recursion, backtracking, heuristics, and more Reviews sorting and searching, network algorithms, and numerical algorithms Includes general problem-solving techniques such as brute force and exhaustive search, divide and conquer, backtracking, recursion, branch and bound, and more In addition, Essential Algorithms features a companion website that includes full instructor materials to support training or higher ed adoptions.

Essential Algorithms

Author: Rod Stephens
Publisher: John Wiley & Sons
ISBN: 1118612108
Format: PDF, Mobi
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Exercises; Chapter 18: Distributed Algorithms; Types of Parallelism; Distributed Algorithms; Summary; Exercises; Chapter 19: Interview Puzzles; Asking Interview Puzzle Questions; Answering Interview Puzzle Questions; Summary; Exercises; Appendix A: Summary of Algorithmic Concepts; Chapter 1: Algorithm Basics; Chapter 2: Numeric Algorithms; Chapter 3: Linked Lists; Chapter 4: Arrays; Chapter 5: Stacks and Queues; Chapter 6: Sorting; Chapter 7: Searching; Chapter 8: Hash Tables; Chapter 9: Recursion; Chapter 10: Trees; Chapter 11: Balanced Trees; Chapter 12: Decision Trees.

The Algorithm Design Manual

Author: Steven S Skiena
Publisher: Springer Science & Business Media
ISBN: 1848000707
Format: PDF, Mobi
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This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses 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, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • 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 • Includes several NEW "war stories" relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Practical Analysis of Algorithms

Author: Dana Vrajitoru
Publisher: Springer
ISBN: 3319098888
Format: PDF, Mobi
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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.

Differential Evolution

Author: Kenneth Price
Publisher: Springer Science & Business Media
ISBN: 3540313060
Format: PDF, ePub
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Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.

Algorithms

Author: Fethi Rabhi
Publisher: Addison Wesley
ISBN: 9780201596045
Format: PDF, ePub
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A student introduction to the design of algorithms for problem solving. Written from a functional programming perspective, the text should appeal to anyone studying algorithms. Included are end-of-chapter exercises and bibliographic references.

Compact Data Structures

Author: Gonzalo Navarro
Publisher: Cambridge University Press
ISBN: 1316791009
Format: PDF, ePub
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Compact data structures help represent data in reduced space while allowing it to be queried, navigated, and operated in compressed form. They are essential tools for efficiently handling massive amounts of data by exploiting the memory hierarchy. They also reduce the resources needed in distributed deployments and make better use of the limited memory in low-end devices. The field has developed rapidly, reaching a level of maturity that allows practitioners and researchers in application areas to benefit from the use of compact data structures. This first comprehensive book on the topic focuses on the structures that are most relevant for practical use. Readers will learn how the structures work, how to choose the right ones for their application scenario, and how to implement them. Researchers and students in the area will find in the book a definitive guide to the state of the art in compact data structures.

Data Structures and Algorithms Using C

Author: Michael McMillan
Publisher: Cambridge University Press
ISBN: 0521670152
Format: PDF, Mobi
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Michael McMillan discusses the implementation of data structures and algorithms from the .NET framework. The comprehensive text includes basic data structures and algorithms plus advanced algorithms such as probabilistic algorithms and dynamics programming.

Introduction to Algorithms

Author: Thomas H. Cormen
Publisher: MIT Press
ISBN: 0262533057
Format: PDF, ePub, Docs
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A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

Adaptive Filtering

Author: Paulo S. R. Diniz
Publisher: Springer Science & Business Media
ISBN: 1461441064
Format: PDF, ePub, Docs
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In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.