A Textbook Of Data Structures And Algorithms Volume 3

Data structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.

Data structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.

The design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.

Data Structures and Algorithms Using C++ helps students to master data structures, their algorithms and the analysis of complexities of these algorithms. Each chapter includes an Abstract Data Type (ADT) and applications along with a detailed explanation of the topics. This book meets the requirements of the course curricula of all Indian universities.

Introduction -- Array-based lists -- Linked lists -- Skiplists -- Hash tables -- Binary trees -- Random binary search trees -- Scapegoat trees -- Red-black trees -- Heaps -- Sorting algorithms -- Graphs -- Data structures for integers -- External memory searching.

Data structures and algorithms is a fundamental course in Computer Science, which enables learners across any discipline to develop the much-needed foundation of efficient programming, leading to better problem solving in their respective disciplines. A Textbook of Data Structures and Algorithms is a textbook that can be used as course material in classrooms, or as self-learning material. The book targets novice learners aspiring to acquire advanced knowledge of the topic. Therefore, the content of the book has been pragmatically structured across three volumes and kept comprehensive enough to help them in their progression from novice to expert. With this in mind, the book details concepts, techniques and applications pertaining to data structures and algorithms, independent of any programming language. It includes 181 illustrative problems and 276 review questions to reinforce a theoretical understanding and presents a suggestive list of 108 programming assignments to aid in the implementation of the methods covered.

Data structures and algorithms are presented at the college level in a highly accessible format that presents material with one-page displays in a way that will appeal to both teachers and students. The thirteen chapters cover: Models of Computation, Lists, Induction and Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Graphs, Strings, Discrete Fourier Transform, Parallel Computation. Key features: Complicated concepts are expressed clearly in a single page with minimal notation and without the "clutter" of the syntax of a particular programming language; algorithms are presented with self-explanatory "pseudo-code." * Chapters 1-4 focus on elementary concepts, the exposition unfolding at a slower pace. Sample exercises with solutions are provided. Sections that may be skipped for an introductory course are starred. Requires only some basic mathematics background and some computer programming experience. * Chapters 5-13 progress at a faster pace. The material is suitable for undergraduates or first-year graduates who need only review Chapters 1 -4. * This book may be used for a one-semester introductory course (based on Chapters 1-4 and portions of the chapters on algorithm design, hashing, and graph algorithms) and for a one-semester advanced course that starts at Chapter 5. A year-long course may be based on the entire book. * Sorting, often perceived as rather technical, is not treated as a separate chapter, but is used in many examples (including bubble sort, merge sort, tree sort, heap sort, quick sort, and several parallel algorithms). Also, lower bounds on sorting by comparisons are included with the presentation of heaps in the context of lower bounds for comparison-based structures. * Chapter 13 on parallel models of computation is something of a mini-book itself, and a good way to end a course. Although it is not clear what parallel

Learn Data Structures and Algorithms! This book is a collection of lectures notes on Data Structures and Algorithms. The content found in this book supplements the free video lecture series, of the same name, "Advanced Data Structures", by the author, Dr. Daniel Page. This video lecture series is available at http://www.pagewizardgames.com/datastructures. This book: -Contains Computer Science topics and materials comparable to those found among university courses at a similar level (second-year) at top Canadian universities. -Provides an accessible written companion and supplemental notes for those that wish to learn the subject of Data Structures and Algorithms from the video lecture series, but have difficulties taking notes, or would prefer having a written alternative to follow along. This book is ideal for those with already an introductory programming background, know a little bit about computing, and wish to learn more about Data Structures and Algorithms and begin a more formal study of Computer Science. The materials here are a great place to start for supplemental/additional learning materials on the subject for self-study, university students, or those that want to learn more about Computer Science. Dr. Daniel Page places great emphasis on the introductory mathematical aspects of Computer Science, a natural transition from a basic programming background to thinking a bit more like a computer scientist about Computer Science. This book is not a textbook. The author assumes the reader is familiar with algebra, functions, common finite and infinite series such as arithmetic series and geometric series, and basic control structures in programming or logic. All the algorithms in this book are described in English, or using Java-like pseudocode. Chapters -Chapter 1 - Introduction: Data Structures, Problems, Input Size, Algorithms, The Search Problem. -Chapter 2 - Intro to Analysis of Algorithms I: Complexity Analysis, Comparing Algorithms, Growth Rate of Functions (Asymptotics), Showing f is O(g), Showing f is not O(g). -Chapter 3 - Intro to Analysis of Algorithms II: Some Properties of O, An Iterative Example, Back to our "Easy" Search Problem. -Chapter 4 - Dictionaries: The Dictionary Problem, Simple Implementations of a Dictionary. -Chapter 5 - Hashing: Hash Function, Hash Code, Separate Chaining, Open Addressing, Revisiting the Load Factor. -Chapter 6 - Trees: Tree ADT, Linked Tree Representation, Tree Property, Computing Height of a Tree, Tree Traversals -Chapter 7 - Priority Queues & Heaps: Priority Queues, Heaps, Array-Based Implementation, Building a Heap, Application: Sorting, Introduction to Amortized Analysis -Chapter 8 - Binary Search Trees: Ordered Dictionary ADT, BST Implementations, Inorder Traversal, Smallest, Get, Put, Remove, Successor. -Chapter 9 - AVL Trees: Height, AVL Trees, Re-Balancing AVL Trees, putAVL, removeAVL, AVL Tree Performance. -Chapter 10 - Graphs: Degrees and the Handshaking Lemma, Complete Graphs, Paths and Cycles, Trees, Forests, Subgraphs, and Connectivity, Graph Representations. -Chapter 11 - Graph Traversals: Depth-First Search (DFS), Path-Finding, Cycle Detection, Counting Vertices, DFS Tree, Breadth-First Search (BFS), Summary. -Chapter 12 - Minimum Spanning Trees: Weighted Graphs, Minimum Spanning Trees & Algorithms, Prim's Algorithm, Heap-Based Implementation of Prim's Algorithm and More! -Chapter 13 - Shortest Paths: Single-Source Shortest Path Problem, Dijkstra's Algorithm. -Chapter 14 - Multiway Search Trees: Beyond Binary Search Trees, Get, Put, Successor and Remove, (2,4)-Trees, B-Trees.