In numerous computer applications there is a need of storing large sets of objects in such a way that some questions about those objects can be answered efficiently. Data structures that store such sets of objects can be either static (built for a fixed set of objects) or dynamic (insertions of new objects and deletions of existing objects can be performed). Especially for more complex searching problems as they arise in such fields as computational geometry, database design and computer graphics, only static data structures are available. This book aims at remedying this lack of flexibility by providing a number of general techniques for turning static data structures for searching problems into dynamic structures. Although the approach is basically theoretical, the techniques offered are often practically applicable. The book is written in such a way that it is readable for those who have some elementary knowledge of data structures and algorithms. Although this monograph was first published in 1983, it is still unique as a general treatment of methods for constructing dynamic data structures.

Describes several useful paradigms for the design and implementation of efficient external memory (EM) algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing.

One of the main problems in chip design is the enormous number of possible combinations of individual chip elements within a system, and the problem of their compatibility. The recent application of data structures, efficient algorithms, and ordered binary decision diagrams (OBDDs) has proven vital in designing the computer chips of tomorrow. This book provides an introduction to the foundations of this interdisciplinary research area, emphasizing its applications in computer aided circuit design.

A modern treatment of data structures using the C programming language. Emphasizes such programming practices as dynamic memory allocation, recursion, data abstraction, and "generic" data structures. Appropriate for sophomore level data structures courses that use C, taking advantage of the flexibility that C provides. (vs. VanWyck, Korsh/Garrett)

Get started with C++ programming by learning how to build applications using its data structures and algorithms Key FeaturesExplore data structures such as arrays, stacks, and graphs with real-world examplesStudy the trade-offs between algorithms and data structures and discover what works and what doesn'tDiscover how techniques such as bloom filters and multi-way heaps boost real-world applicationsBook Description C++ is a mature multi-paradigm programming language that enables you to write high-level code with a high degree of control over the hardware. Today, significant parts of software infrastructure, including databases, browsers, multimedia frameworks, and GUI toolkits, are written in C++. This book starts by introducing C++ data structures and how to store data using linked lists, arrays, stacks, and queues. In later chapters, the book explains the basic algorithm design paradigms, such as the greedy approach and the divide-and-conquer approach, which are used to solve a large variety of computational problems. Finally, you will learn the advanced technique of dynamic programming to develop optimized implementations of several algorithms discussed in the book. By the end of this book, you will have learned how to implement standard data structures and algorithms in efficient and scalable C++ 14 code. What you will learnBuild applications using hash tables, dictionaries, and setsExplore how modern hardware affects the actual run-time performance of programsApply common algorithms such as heapsort and merge sort for string data typesUse C++ template metaprogramming to write code librariesImplement a URL shortening service using a bloom filterUse appropriate modern C++ idioms such as std:: array instead of C-style arraysWho this book is for This book is for developers or students who want to revisit basic data structures and algorithm design techniques. Although no mathematical background is required, basic knowledge of complexity classes and Big O notation along with a qualification in an algorithms course will help you get the most out of this book. Familiarity with C++ 14 standard is assumed.

Algorithms and data structures are much more than abstract concepts. Mastering them enables you to write code that runs faster and more efficiently, which is particularly important for today’s web and mobile apps. Take a practical approach to data structures and algorithms, with techniques and real-world scenarios that you can use in your daily production code, with examples in JavaScript, Python, and Ruby. This new and revised second edition features new chapters on recursion, dynamic programming, and using Big O in your daily work. Use Big O notation to measure and articulate the efficiency of your code, and modify your algorithm to make it faster. Find out how your choice of arrays, linked lists, and hash tables can dramatically affect the code you write. Use recursion to solve tricky problems and create algorithms that run exponentially faster than the alternatives. Dig into advanced data structures such as binary trees and graphs to help scale specialized applications such as social networks and mapping software. You’ll even encounter a single keyword that can give your code a turbo boost. Practice your new skills with exercises in every chapter, along with detailed solutions. Use these techniques today to make your code faster and more scalable.

Koffman and Wolfgang introduce data structures in the context of C++ programming. They embed the design and implementation of data structures into the practice of sound software design principles that are introduced early and reinforced by 20 case studies. Data structures are introduced in the C++ STL format whenever possible. Each new data structure is introduced by describing its interface in the STL. Next, one or two simpler applications are discussed then the data structure is implemented following the interface previously introduced. Finally, additional advanced applications are covered in the case studies, and the cases use the STL. In the implementation of each data structure, the authors encourage students to perform a thorough analysis of the design approach and expected performance before actually undertaking detailed design and implementation. Students gain an understanding of why different data structures are needed, the applications they are suited for, and the advantages and disadvantages of their possible implementations. Case studies follow a five-step process (problem specification, analysis, design, implementation, and testing) that has been adapted to object-oriented programming. Students are encouraged to think critically about the five-step process and use it in their problem solutions. Several problems have extensive discussions of testing and include methods that automate the testing process. Some cases are revisited in later chapters and new solutions are provided that use different data structures. The text assumes a first course in programming and is designed for Data Structures or the second course in programming, especially those courses that include coverage of OO design and algorithms. A C++ primer is provided for students who have taken a course in another programming language or for those who need a review in C++. Finally, more advanced coverage of C++ is found in an appendix. Course Hierarchy: Course is the second course in the CS curriculum Required of CS majors Course names include Data Structures and Data Structures & Algorithms