Data structure

Algorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems.

This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java.

The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.

" .. .1 always worked with programming languages because it seemed to me that until you could understand those, you really couldn't understand computers. Understanding them doesn't really mean only being able to use them. A lot of people can use them without understanding them." Christopher Strachey The development of programming languages is one of the finest intellectual achievements of the new discipline called Computer Science. And yet, there is no other subject that I know of, that has such emotionalism and mystique associated with it. Thus, my attempt to write about this highly charged subject is taken with a good deal of in my role as professor I have felt the need for a caution. Nevertheless, modern treatment of this subject. Traditional books on programming languages are like abbreviated language manuals, but this book takes a fundamentally different point of view. I believe that the best possible way to study and understand today's programming languages is by focusing on a few essential concepts. These concepts form the outline for this book and include such topics as variables, expressions, statements, typing, scope, procedures, data types, exception handling and concurrency. By understanding what these concepts are and how they are realized in different programming languages, one arrives at a level of comprehension far greater than one gets by writing some programs in a xii Preface few languages. Moreover, knowledge of these concepts provides a framework for understanding future language designs.
This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently.

Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.

Topics and features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those coming from a different language background; reviews the use of hashing in sets and maps, along with an examination of binary search trees and tree traversals, and material on depth first search of graphs; discusses topics suitable for an advanced course, such as membership structures, heaps, balanced binary search trees, B-trees and heuristic search.

Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.

©2020 GoogleSite Terms of ServicePrivacyDevelopersArtistsAbout Google|Location: United StatesLanguage: English (United States)
By purchasing this item, you are transacting with Google Payments and agreeing to the Google Payments Terms of Service and Privacy Notice.