Data Structures and Algorithms

Annotated Reference with Examples


Data Structures and Algorithms
Data Structures and Algorithms
Open Access

Book Details

Authors Granville Barnett, Luca Del Tongo
Published 2008
Edition 1
Paperback 111 pages
Language English
License Open Access

Book Description

This book provides implementations of common and uncommon algorithms in pseudocode which is language independent and provides for easy porting to most imperative programming languages. It is not a definitive book on the theory of data structures and algorithms.

For the most part this book presents implementations devised by the authors themselves based on the concepts by which the respective algorithms are based upon so it is more than possible that our implementations differ from those considered the norm.

You should use this book alongside another on the same subject, but one that contains formal proofs of the algorithms in question. In this book we use the abstract big Oh notation to depict the run time complexity of algorithms so that the book appeals to a larger audience.

Data Structures and Algorithms (DSA) features implementations of data structures and algorithms that are not implemented in any version of .NET. This book is the result of a series of emails sent back and forth between the two authors during the development of a library for the .NET framework of the same name. A key factor of this book and its associated implementations is that all algorithms were designed by us, using the theory of the algorithm in question as a guideline. The book use's pseudo code to describe the solutions that we have created so that it can be easily ported to many imperative OO languages like C#, C++, and Java (amongst others).


This book is published as open-access, which means it is freely available to read, download, and share without restrictions.

If you enjoyed the book and would like to support the author, you can purchase a printed copy (hardcover or paperback) from official retailers.

Download and Read Links

PDF

Share this Book

[localhost]# find . -name "*Similar_Books*"


Open Data Structures

Algorithms

Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Mor

Problem Solving with Algorithms and Data Structures, 3rd Edition

Python Algorithms

The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice

Think Data Structures

Java

If you're a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering - data structures and algorithms - in a way that's clearer, more concise, and more engaging than other materials. By emphasizing practic

Fundamentals of Computer Programming with C#

C#

This open book aims to provide novice programmers solid foundation of basic knowledge regardless of the programming language. This book covers the fundamentals of programming that have not changed significantly over the last 10 years. Educational content was developed by an authoritative author team led by Svetlin Nakov from the Software University

Pro TBB

C / C++

This book is a modern guide for all C++ programmers to learn Threading Building Blocks (TBB). Written by TBB and parallel programming experts, this book reflects their collective decades of experience in developing and teaching parallel programming with TBB, offering their insights in an approachable manner. Throughout the book the authors present

Introduction to Data Science

R

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data vi