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Author: Pat Morin
Publisher: Athabasca University Press
Published: 2013
Total Pages: 336
ISBN-13: 1927356385
DOWNLOAD EBOOK →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.
Author: Allen Downey
Publisher: "O'Reilly Media, Inc."
Published: 2017-07-07
Total Pages: 157
ISBN-13: 1491972343
DOWNLOAD EBOOK →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 practical knowledge and skills over theory, author Allen Downey shows you how to use data structures to implement efficient algorithms, and then analyze and measure their performance. You’ll explore the important classes in the Java collections framework (JCF), how they’re implemented, and how they’re expected to perform. Each chapter presents hands-on exercises supported by test code online. Use data structures such as lists and maps, and understand how they work Build an application that reads Wikipedia pages, parses the contents, and navigates the resulting data tree Analyze code to predict how fast it will run and how much memory it will require Write classes that implement the Map interface, using a hash table and binary search tree Build a simple web search engine with a crawler, an indexer that stores web page contents, and a retriever that returns user query results Other books by Allen Downey include Think Java, Think Python, Think Stats, and Think Bayes.
Author: Langsam
Publisher: Pearson Education India
Published: 2003-09
Total Pages: 692
ISBN-13: 9788131711484
DOWNLOAD EBOOK →Author: Nell Dale
Publisher: Jones & Bartlett Publishers
Published: 2012
Total Pages: 823
ISBN-13: 1449613543
DOWNLOAD EBOOK →Continuing the success of the popular second edition, the updated and revised Object-Oriented Data Structures Using Java, Third Edition is sure to be an essential resource for students learning data structures using the Java programming language. It presents traditional data structures and object-oriented topics with an emphasis on problem-solving, theory, and software engineering principles. Beginning early and continuing throughout the text, the authors introduce and expand upon the use of many Java features including packages, interfaces, abstract classes, inheritance, and exceptions. Numerous case studies provide readers with real-world examples and demonstrate possible solutions to interesting problems. The authors' lucid writing style guides readers through the rigor of standard data structures and presents essential concepts from logical, applications, and implementation levels. Key concepts throughout the Third Edition have been clarified to increase student comprehension and retention, and end-of-chapter exercises have been updated and modified. New and Key Features to the Third Edition: -Includes the use of generics throughout the text, providing the dual benefits of allowing for a type safe use of data structures plus exposing students to modern approaches. -This text is among the first data structures textbooks to address the topic of concurrency and synchonization, which are growing in the importance as computer systems move to using more cores and threads to obtain additional performance with each new generation. Concurrency and synchonization are introduced in the new Section 5.7, where it begins with the basics of Java threads. -Provides numerous case studies and examples of the problem solving process. Each case study includes problem description, an analysis of the problem input and required output, and a discussion of the appropriate data structures to use. -Expanded chapter exercises allow you as the instructor to reinforce topics for your students using both theoretical and practical questions. -Chapters conclude with a chapter summary that highlights the most important topics of the chapter and ties together related topics.
Author: Daniel R. Page
Publisher: PageWizard Games, Learning & Entertainment
Published: 2020-11-08
Total Pages: 161
ISBN-13: 1777407516
DOWNLOAD EBOOK →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.
Author: Peter Brass
Publisher: Cambridge University Press
Published: 2019-05-16
Total Pages: 0
ISBN-13: 9781108735513
DOWNLOAD EBOOK →Advanced Data Structures presents a comprehensive look at the ideas, analysis, and implementation details of data structures as a specialized topic in applied algorithms. Data structures are how data is stored within a computer, and how one can go about searching for data within. This text examines efficient ways to search and update sets of numbers, intervals, or strings by various data structures, such as search trees, structures for sets of intervals or piece-wise constant functions, orthogonal range search structures, heaps, union-find structures, dynamization and persistence of structures, structures for strings, and hash tables. This is the first volume to show data structures as a crucial algorithmic topic, rather than relegating them as trivial material used to illustrate object-oriented programming methodology, filling a void in the ever-increasing computer science market. Numerous code examples in C and more than 500 references make Advanced Data Structures an indispensable text. topic. Numerous code examples in C and more than 500 references make Advanced Data Structures an indispensable text.
Author: J.A. Storer
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 609
ISBN-13: 146120075X
DOWNLOAD EBOOK →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
Author: Marcello La Rocca
Publisher: Simon and Schuster
Published: 2021-08-10
Total Pages: 768
ISBN-13: 1638350221
DOWNLOAD EBOOK →Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside Build on basic data structures you already know Profile your algorithms to speed up application Store and query strings efficiently Distribute clustering algorithms with MapReduce Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization
Author: Dzejla Medjedovic
Publisher: Simon and Schuster
Published: 2022-08-16
Total Pages: 302
ISBN-13: 1638356564
DOWNLOAD EBOOK →Massive modern datasets make traditional data structures and algorithms grind to a halt. This fun and practical guide introduces cutting-edge techniques that can reliably handle even the largest distributed datasets. In Algorithms and Data Structures for Massive Datasets you will learn: Probabilistic sketching data structures for practical problems Choosing the right database engine for your application Evaluating and designing efficient on-disk data structures and algorithms Understanding the algorithmic trade-offs involved in massive-scale systems Deriving basic statistics from streaming data Correctly sampling streaming data Computing percentiles with limited space resources Algorithms and Data Structures for Massive Datasets reveals a toolbox of new methods that are perfect for handling modern big data applications. You’ll explore the novel data structures and algorithms that underpin Google, Facebook, and other enterprise applications that work with truly massive amounts of data. These effective techniques can be applied to any discipline, from finance to text analysis. Graphics, illustrations, and hands-on industry examples make complex ideas practical to implement in your projects—and there’s no mathematical proofs to puzzle over. Work through this one-of-a-kind guide, and you’ll find the sweet spot of saving space without sacrificing your data’s accuracy. About the technology Standard algorithms and data structures may become slow—or fail altogether—when applied to large distributed datasets. Choosing algorithms designed for big data saves time, increases accuracy, and reduces processing cost. This unique book distills cutting-edge research papers into practical techniques for sketching, streaming, and organizing massive datasets on-disk and in the cloud. About the book Algorithms and Data Structures for Massive Datasets introduces processing and analytics techniques for large distributed data. Packed with industry stories and entertaining illustrations, this friendly guide makes even complex concepts easy to understand. You’ll explore real-world examples as you learn to map powerful algorithms like Bloom filters, Count-min sketch, HyperLogLog, and LSM-trees to your own use cases. What's inside Probabilistic sketching data structures Choosing the right database engine Designing efficient on-disk data structures and algorithms Algorithmic tradeoffs in massive-scale systems Computing percentiles with limited space resources About the reader Examples in Python, R, and pseudocode. About the author Dzejla Medjedovic earned her PhD in the Applied Algorithms Lab at Stony Brook University, New York. Emin Tahirovic earned his PhD in biostatistics from University of Pennsylvania. Illustrator Ines Dedovic earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Introduction PART 1 HASH-BASED SKETCHES 2 Review of hash tables and modern hashing 3 Approximate membership: Bloom and quotient filters 4 Frequency estimation and count-min sketch 5 Cardinality estimation and HyperLogLog PART 2 REAL-TIME ANALYTICS 6 Streaming data: Bringing everything together 7 Sampling from data streams 8 Approximate quantiles on data streams PART 3 DATA STRUCTURES FOR DATABASES AND EXTERNAL MEMORY ALGORITHMS 9 Introducing the external memory model 10 Data structures for databases: B-trees, Bε-trees, and LSM-trees 11 External memory sorting
Author: Chris Okasaki
Publisher: Cambridge University Press
Published: 1999-06-13
Total Pages: 236
ISBN-13: 9780521663502
DOWNLOAD EBOOK →This book describes data structures and data structure design techniques for functional languages.
