Advertisement
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C+ Mark Allen Weiss, 2003 In this second edition of his successful book, experienced teacher and author Mark Allen Weiss continues to refine and enhance his innovative approach to algorithms and data structures. Written for the advanced data structures course, this text highlights theoretical topics such as abstract data types and the efficiency of algorithms, as well as performance and running time. Before covering algorithms and data structures, the author provides a brief introduction to C++ for programmers unfamiliar with the language. Dr Weiss's clear writing style, logical organization of topics, and extensive use of figures and examples to demonstrate the successive stages of an algorithm make this an accessible, valuable text. New to this Edition *An appendix on the Standard Template Library (STL) *C++ code, tested on multiple platforms, that conforms to the ANSI ISO final draft standard 0201361221B04062001 |
| data structures and algorithms analysis: A Practical Introduction to Data Structures and Algorithm Analysis Clifford A. Shaffer, 2001 This practical text contains fairly traditional coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C++ Weiss, Weiss Mark Allen, 2007-09 The C++ language is brought up-to-date and simplified, and the Standard Template Library is now fully incorporated throughout the text. Data Structures and Algorithm Analysis in C++ is logically organized to cover advanced data structures topics from binary heaps to sorting to NP-completeness. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C++ Mark Allen Weiss, 2006 Written for advanced courses, the third edition refines and enhances its innovative approach to algorithms and datastructures. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C++, Third Edition Clifford A. Shaffer, 2012-07-26 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses C++ as the programming language. |
| data structures and algorithms analysis: Algorithms and Data Structures Helmut Knebl, 2020-10-31 This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application. This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises. |
| data structures and algorithms analysis: Data Structures, Algorithms, and Software Principles in C Thomas A. Standish, 1995 Using C, this book develops the concepts and theory of data structures and algorithm analysis in a gradual, step-by-step manner, proceeding from concrete examples to abstract principles. Standish covers a wide range of both traditional and contemporary software engineering topics. The text also includes an introduction to object-oriented programming using C++. By introducing recurring themes such as levels of abstraction, recursion, efficiency, representation and trade-offs, the author unifies the material throughout. Mathematical foundations can be incorporated at a variety of depths, allowing the appropriate amount of math for each user. |
| data structures and algorithms analysis: Data Structures and Algorithms: A First Course Iain T. Adamson, 2012-12-06 All young computer scientists who aspire to write programs must learn something about algorithms and data structures. This book does exactly that. Based on lecture courses developed by the author over a number of years the book is written in an informal and friendly way specifically to appeal to students. The book is divided into four parts: the first on Data Structures introduces a variety of structures and the fundamental operations associated with them, together with descriptions of how they are implemented in Pascal; the second discusses algorithms and the notion of complexity; Part III is concerned with the description of successively more elaborate structures for the storage of records and algorithms for retrieving a record from such a structure by means of its key; and finally, Part IV consists of very full solutions to nearly all the exercises in the book. |
| data structures and algorithms analysis: Data Structures and Algorithms in Python Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2013-06-17 Based on the authors' market leading data structures books in Java and C++, this book offers a comprehensive, definitive introduction to data structures in Python by authoritative authors. Data Structures and Algorithms in Python is the first authoritative object-oriented book available for Python data structures. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++. Begins by discussing Python's conceptually simple syntax, which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout the text. Presents each data structure using ADTs and their respective implementations and introduces important design patterns as a means to organize those implementations into classes, methods, and objects. Provides a thorough discussion on the analysis and design of fundamental data structures. Includes many helpful Python code examples, with source code provided on the website. Uses illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, help readers learn how to think like programmers, and reinforce important concepts. Contains many Python-code and pseudo-code fragments, and hundreds of exercises, which are divided into roughly 40% reinforcement exercises, 40% creativity exercises, and 20% programming projects. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in Java Mark A. Weiss, 2011-11-21 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Data Structures and Algorithm Analysis in Java is an “advanced algorithms” book that fits between traditional CS2 and Algorithms Analysis courses. In the old ACM Curriculum Guidelines, this course was known as CS7. This text is for readers who want to learn good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amount of efficiency. Readers should have some knowledge of intermediate programming, including topics as object-based programming and recursion, and some background in discrete math. As the speed and power of computers increases, so does the need for effective programming and algorithm analysis. By approaching these skills in tandem, Mark Allen Weiss teaches readers to develop well-constructed, maximally efficient programs in Java. Weiss clearly explains topics from binary heaps to sorting to NP-completeness, and dedicates a full chapter to amortized analysis and advanced data structures and their implementation. Figures and examples illustrating successive stages of algorithms contribute to Weiss’ careful, rigorous and in-depth analysis of each type of algorithm. A logical organization of topics and full access to source code complement the text’s coverage. |
| data structures and algorithms analysis: Problem Solving with Algorithms and Data Structures Using Python Bradley N. Miller, David L. Ranum, 2011 Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms. |
| data structures and algorithms analysis: Advanced Algorithms and Data Structures Marcello La Rocca, 2021-08-10 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 |
| data structures and algorithms analysis: Data Structures and Algorithms in C++ Adam Drozdek, 2012-08-27 Strengthen your understanding of data structures and their algorithms for the foundation you need to successfully design, implement and maintain virtually any software system. Theoretical, yet practical, DATA STRUCUTRES AND ALGORITHMS IN C++, 4E by experienced author Adam Drosdek highlights the fundamental connection between data structures and their algorithms, giving equal weight to the practical implementation of data structures and the theoretical analysis of algorithms and their efficiency. This edition provides critical new coverage of treaps, k-d trees and k-d B-trees, generational garbage collection, and other advanced topics such as sorting methods and a new hashing technique. Abundant C++ code examples and a variety of case studies provide valuable insights into data structures implementation. DATA STRUCTURES AND ALGORITHMS IN C++ provides the balance of theory and practice to prepare readers for a variety of applications in a modern, object-oriented paradigm. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data structures and algorithms analysis: Think Data Structures Allen B. Downey, 2017-07-07 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. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C Weiss, 1997-09 In The Second Edition Of This Best-Selling Book, The Author Continues To Refine And Enhance His Innovative Approach To Algorithms And Data Structures. Using A C Implementation, He Highlights Conceptual Topics, Focusing On Adts And The Analysis Of Algorithms For Efficiency As Well As Performance And Running Time. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in Java Mark Allen Weiss, 2013 |
| data structures and algorithms analysis: Data Structures and Algorithms in Java Michael T. Goodrich, Roberto Tamassia, Michael H. Goldwasser, 2014-03-27 The design and analysis of efficient data structures has long been recognized as a key component of the Computer Science curriculum. Goodrich, Tomassia and Goldwasser's approach to this classic topic is based on the object-oriented paradigm as the framework of choice for the design of data structures. For each ADT presented in the text, the authors provide an associated Java interface. Concrete data structures realizing the ADTs are provided as Java classes implementing the interfaces. The Java code implementing fundamental data structures in this book is organized in a single Java package, net.datastructures. This package forms a coherent library of data structures and algorithms in Java specifically designed for educational purposes in a way that is complimentary with the Java Collections Framework. |
| data structures and algorithms analysis: Open Data Structures Pat Morin, 2013 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 analysis: Algorithms and Data Structures in VLSI Design Christoph Meinel, Thorsten Theobald, 2012-12-06 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. |
| data structures and algorithms analysis: Algorithms and Data Structures With Applications to Graphics and Geometry Jurg Nievergelt, Klaus Hinrichs, 2011 |
| data structures and algorithms analysis: Codeless Data Structures and Algorithms Armstrong Subero, 2020-02-13 In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but classes in the subject can be dry or spend too much time on theory and unnecessary readings. Regardless of your programming language background, Codeless Data Structures and Algorithms has you covered. In this book, author Armstrong Subero will help you learn DSAs without writing a single line of code. Straightforward explanations and diagrams give you a confident handle on the topic while ensuring you never have to open your code editor, use a compiler, or look at an integrated development environment. Subero introduces you to linear, tree, and hash data structures and gives you important insights behind the most common algorithms that you can directly apply to your own programs. Codeless Data Structures and Algorithms provides you with the knowledge about DSAs that you will need in the professional programming world, without using any complex mathematics or irrelevant information. Whether you are a new developer seeking a basic understanding of the subject or a decision-maker wanting a grasp of algorithms to apply to your projects, this book belongs on your shelf. Quite often, a new, refreshing, and unpretentious approach to a topic is all you need to get inspired. What You'll LearnUnderstand tree data structures without delving into unnecessary details or going into too much theoryGet started learning linear data structures with a basic discussion on computer memory Study an overview of arrays, linked lists, stacks and queues Who This Book Is ForThis book is for beginners, self-taught developers and programmers, and anyone who wants to understand data structures and algorithms but don’t want to wade through unnecessary details about quirks of a programming language or don’t have time to sit and read a massive book on the subject. This book is also useful for non-technical decision-makers who are curious about how algorithms work. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in C: For Anna University, 2/e , |
| data structures and algorithms analysis: Data Structures and Algorithms in C++ Michael T. Goodrich, Roberto Tamassia, David M. Mount, 2011-02-22 An updated, innovative approach to data structures and algorithms Written by an author team of experts in their fields, this authoritative guide demystifies even the most difficult mathematical concepts so that you can gain a clear understanding of data structures and algorithms in C++. The unparalleled author team incorporates the object-oriented design paradigm using C++ as the implementation language, while also providing intuition and analysis of fundamental algorithms. Offers a unique multimedia format for learning the fundamentals of data structures and algorithms Allows you to visualize key analytic concepts, learn about the most recent insights in the field, and do data structure design Provides clear approaches for developing programs Features a clear, easy-to-understand writing style that breaks down even the most difficult mathematical concepts Building on the success of the first edition, this new version offers you an innovative approach to fundamental data structures and algorithms. |
| data structures and algorithms analysis: Algorithms, Data Structures, and Problem Solving with C++ Mark Allen Weiss, 1996 Providing a complete explanation of problem solving and algorithms using C++, the author's theoretical perspective emphasizes software engineering and object-oriented programming, and encourages readers to think abstractly. Numerous code examples and case studies are used to support the algorithms presented. |
| data structures and algorithms analysis: Data Structures and Algorithm Analysis in Java, Third Edition Clifford A. Shaffer, 2012-09-06 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses Java as the programming language. |
| data structures and algorithms analysis: Mastering Algorithms with C Kyle Loudon, 1999 Implementations, as well as interesting, real-world examples of each data structure and algorithm, are shown in the text. Full source code appears on the accompanying disk. |
| data structures and algorithms analysis: An Introduction to Data Structures and Algorithms J.A. Storer, 2012-12-06 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 |
| data structures and algorithms analysis: Data Structures and Algorithms Aho Alfred V., Aho, 1983 |
| data structures and algorithms analysis: Algorithms and Data Structures Kurt Mehlhorn, Peter Sanders, 2008-05-27 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. |
| data structures and algorithms analysis: Space-Efficient Data Structures, Streams, and Algorithms Andrej Brodnik, Alejandro Lopez-Ortiz, Venkatesh Raman, Alfredo Viola, 2013-08-13 This Festschrift volume, published in honour of J. Ian Munro, contains contributions written by some of his colleagues, former students, and friends. In celebration of his 66th birthday the colloquium Conference on Space Efficient Data Structures, Streams and Algorithms was held in Waterloo, ON, Canada, during August 15-16, 2013. The articles presented herein cover some of the main topics of Ian's research interests. Together they give a good overall perspective of the last 40 years of research in algorithms and data structures. |
| data structures and algorithms analysis: Data Structures and Network Algorithms Robert Endre Tarjan, 1983-01-01 There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms. |
| data structures and algorithms analysis: R Data Structures and Algorithms Dr. PKS Prakash, Achyutuni Sri Krishna Rao, 2016-11-21 Increase speed and performance of your applications with efficient data structures and algorithms About This Book See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples Find out about important and advanced data structures such as searching and sorting algorithms Understand important concepts such as big-o notation, dynamic programming, and functional data structured Who This Book Is For This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected. What You Will Learn Understand the rationality behind data structures and algorithms Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis Get to know the fundamentals of arrays and linked-based data structures Analyze types of sorting algorithms Search algorithms along with hashing Understand linear and tree-based indexing Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm Understand dynamic programming (Knapsack) and randomized algorithms In Detail In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. Style and approach This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples. |
| data structures and algorithms analysis: Data Structures and Algorithms in Ruby Hemant Jain, 2018-02-15 This book is about the usage of Data Structures and Algorithms in computer programming. GitHub Link: https://github.com/Hemant-Jain-Author/ We will be studying complexity analysis. Then will look into the various data structures and their algorithms. We will be studying Linked-List, Stack, Queue, Trees, Heap, Hash Table and Graphs. We will be studying Sorting & Searching techniques. Then we will be looking into algorithm analysis, we will be looking into Brute Force algorithms, Greedy algorithms, Divide & Conquer algorithms, Dynamic Programming, Reduction, and Backtracking. TABLE OF CONTENTS CHAPTER 0: HOW TO USE THIS BOOK CHAPTER 1: ALGORITHMS ANALYSIS CHAPTER 2: APPROACH TO SOLVE ALGORITHM DESIGN PROBLEMS CHAPTER 3: ABSTRACT DATA TYPE & RUBY COLLECTIONS CHAPTER 4: SEARCHING CHAPTER 5: SORTING CHAPTER 6: LINKED LIST CHAPTER 7: STACK CHAPTER 8: QUEUE CHAPTER 9: TREE CHAPTER 10: PRIORITY QUEUE CHAPTER 11: HASH-TABLE CHAPTER 12: GRAPHS CHAPTER 13: STRING ALGORITHMS CHAPTER 14: ALGORITHM DESIGN TECHNIQUES CHAPTER 15: BRUTE FORCE ALGORITHM CHAPTER 16: GREEDY ALGORITHM CHAPTER 17: DIVIDE-AND-CONQUER, DECREASE-AND-CONQUER CHAPTER 18: DYNAMIC PROGRAMMING CHAPTER 19: BACKTRACKING CHAPTER 20: COMPLEXITY THEORY AND NP COMPLETENESS |
| data structures and algorithms analysis: A Common-Sense Guide to Data Structures and Algorithms, Second Edition Jay Wengrow, 2020-08-10 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. |
| data structures and algorithms analysis: The Algorithm Design Manual Steven S Skiena, 2009-04-05 This newly expanded and updated second edition of the best-selling classic continues to take the mystery out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW war stories relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java |
| data structures and algorithms analysis: Sequential and Parallel Algorithms and Data Structures Peter Sanders, Kurt Mehlhorn, Martin Dietzfelbinger, Roman Dementiev, 2019-08-31 This textbook is a concise introduction to the basic toolbox of structures that allow efficient organization and retrieval of data, key algorithms for problems on graphs, and generic techniques for modeling, understanding, and solving algorithmic problems. The authors aim for a balance between simplicity and efficiency, between theory and practice, and between classical results and the forefront of research. 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, optimization, collective communication and computation, and load balancing. The authors also discuss important issues such as algorithm engineering, memory hierarchies, algorithm libraries, and certifying algorithms. Moving beyond the sequential algorithms and data structures of the earlier related title, this book takes into account the paradigm shift towards the parallel processing required to solve modern performance-critical applications and how this impacts on the teaching of algorithms. The book is suitable for undergraduate and graduate students and professionals familiar with programming and basic mathematical language. Most chapters have the same basic structure: the authors discuss a problem as it occurs in a real-life situation, they illustrate the most important applications, and then they introduce simple solutions as informally as possible and as formally as necessary so the reader really understands the issues at hand. As they move to more advanced and optional issues, their approach gradually leads to a more mathematical treatment, including theorems and proofs. The book includes many examples, pictures, informal explanations, and exercises, and the implementation notes introduce clean, efficient implementations in languages such as C++ and Java. |
| data structures and algorithms analysis: Data Structures And Algorithms Shi-kuo Chang, 2003-09-29 This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise. |
| data structures and algorithms analysis: Algorithms Robert Sedgewick, Kevin Wayne, 2014-02-01 This book is Part I of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part I contains Chapters 1 through 3 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the Online Course link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience. |
| data structures and algorithms analysis: Data Structures & Their Algorithms Harry R. Lewis, Larry Denenberg, 1991 Using only practically useful techniques, this book teaches methods for organizing, reorganizing, exploring, and retrieving data in digital computers, and the mathematical analysis of those techniques. The authors present analyses that are relatively brief and non-technical but illuminate the important performance characteristics of the algorithms. Data Structures and Their Algorithms covers algorithms, not the expression of algorithms in the syntax of particular programming languages. The authors have adopted a pseudocode notation that is readily understandable to programmers but has a simple syntax. |
| data structures and algorithms analysis: Data Structure and Algorithms Using C++ Sachi Nandan Mohanty, Pabitra Kumar Tripathy, 2021-01-12 Everyone knows that programming plays a vital role as a solution to automate and execute a task in a proper manner. Irrespective of mathematical problems, the skills of programming are necessary to solve any type of problems that may be correlated to solve real life problems efficiently and effectively. This book is intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it's designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output. Keeping in mind the learner’s sentiments and requirements, the exemplary programs are narrated with a simple approach so that it can lead to creation of good programs that not only executes properly to give the output, but also enables the learners to incorporate programming skills in them. The style of writing a program using a programming language is also emphasized by introducing the inclusion of comments wherever necessary to encourage writing more readable and well commented programs. As practice makes perfect, each chapter is also enriched with practice exercise questions so as to build the confidence of writing the programs for learners. The book is a complete and all-inclusive handbook of C++ that covers all that a learner as a beginner would expect, as well as complete enough to go ahead with advanced programming. This book will provide a fundamental idea about the concepts of data structures and associated algorithms. By going through the book, the reader will be able to understand about the different types of algorithms and at which situation and what type of algorithms will be applicable. |
Data Structures and Algorithm Analysis - Virginia Tech
1 Data Structures and Algorithms 3 1.1 A Philosophy of Data Structures 4 1.1.1 The Need for Data Structures 4 1.1.2 Costs and Benefits 6 1.2 Abstract Data Types and Data Structures 8 1.3 Design Patterns 12 1.3.1 Flyweight 13 1.3.2 Visitor 13 1.3.3 Composite 14 1.3.4 Strategy 15 …
Advanced Data Structures and Algorithm Analysis - GitHub Pages
Data Structures • Data structures represent dynamic sets of instances. • dynamic means the set can change. • can be ordered or unordered. • Data structures are abstractions: supporting …
Data Structures and Analysis - Department of Computer Science ...
1.Motivate a new abstract data type or data structure with some examples and reflection of previous knowledge. 2.Introduce a data structure, discussing both its mechanisms for how it …
Data Structures and Algorithms - Heriot-Watt University
Analysis of Algorithms 6 Comparing Algorithms Given 2 or more algorithms to solve the same problem, how do we select the best one? Some criteria for selecting an algorithm 1) Is it easy …
Data Structures and Algorithms - GitHub Pages
1.1 Runtime Analysis 1.1.1 Asymptotic Analysis (Big O) We can measure the growth rate of the time or space complexity of an algorithm using an upper bound (O(f)), lower bound ((f)) or a …
Algorithms Illuminated - Stanford University
Algorithms Illuminated, Part 1 provides an introduction to and basic literacy in the following four topics. Asymptotic analysis and big-O notation. Asymptotic notation provides the basic …
15-451 Algorithms Lectures 1-10 - CMU School of Computer Science
This course is about the design and analysis of algorithms — how to design correct, efficient algorithms, and how to think clearly about analyzing correctness and running time. What is an …
CS1020 Data Structures and Algorithms I Lecture Note #13
What do we mean by Analysis of Algorithms? How to find the complexity of a program? You are expected to know... What is an algorithm? A step-by-step procedure for solving a problem. …
Design and Analysis of Algorithms Chapter 2 - Computer Science …
I Basic operation: the operation that contributes most towards the running time of the algorithm. Problem: Given a list of n elements and a search key K, find an element equal to K, if any. …
CSE373: Data Structures and Algorithms - University of Washington
• Deeply understand the basic structures used in all software – Understand the data structures and their trade-offs – Rigorously analyze the algorithms that use them (math!) – Learn how to …
Virginia Tech
PDF-1.4 %ÐÔÅØ 1 0 obj /S /GoTo /D (section*.2) >> endobj 4 0 obj (Preface) endobj 5 0 obj /S /GoTo /D (part.1) >> endobj 8 0 obj (I Preliminaries) endobj 9 0 obj /S /GoTo /D (chapter.1) >> …
The - Department of Computer Science
This is a course on the design and analysis of algorithms in tended for rst y ear graduate studen ts in computer science Its purp oses are mixed on the one hand w e wish to co v ... tal data …
Data Structures and Algorithms - Lagout.org
We have attempted to cover data structures and algorithms in the broader context of solving problems using computers. We use abstract data types informally in the
Data Structures and Algorithms in Python - GitHub Pages
Data Structures and Algorithms in Python provides an introduction to data structures and algorithms, including their design, analysis, and implementation. This book is designed for use …
Problem Solving with Algorithms and Data Structures - Auckland
Problem Solving with Algorithms and Data Structures, Release 3.0 Control constructs allow algorithmic steps to be represented in a convenient yet unambiguous way.
Advanced Data Structures and Algorithm Analysis - GitHub Pages
Aggregate analysis ☞ Accounting method ☞ Potential method Amortized Analysis
Algorithms Algorithm Analysis 1 - Virginia Tech
Algorithm Analysis Data Structures & Algorithms 3 CS@VT ©2000-2020 WD McQuain Design Considerations Given a particular problem, there are typically a number of different algorithms …
CSE373: Data Structures and Algorithms Lecture 4: Asymptotic …
Analysis can be about: • The problem or the algorithm (usually algorithm) • Time or space (usually time) – Or power or dollars or … • Best-, worst-, or average-case (usually worst) • Upper-, …
ANALYSIS OF ALGORITHMS - Purdue University
Analysis of Algorithms 8 What is Pseudo-Code? • A mixture of natural language and high-level programming concepts that describes the main ideas behind a generic implementation of a …
CSE373: Data Structures and Algorithms Lecture 3: Asymptotic …
Winter 2014 CSE373: Data Structure & Algorithms 15 Recurrence is T(n) = O(1) + 2T(n/2) – 1 + 2 + 4 + 8 + … for log n times – 2(log n) – 1 which is proportional to n (definition of logarithm) …
Data Structures and Algorithm Analysis - Virginia Tech
1 Data Structures and Algorithms 3 1.1 A Philosophy of Data Structures 4 1.1.1 The Need for Data Structures 4 1.1.2 Costs and Benefits 6 1.2 Abstract Data Types and Data Structures 8 …
Advanced Data Structures and Algorithm Analysis - GitHub Pages
Data Structures • Data structures represent dynamic sets of instances. • dynamic means the set can change. • can be ordered or unordered. • Data structures are abstractions: supporting …
Data Structures and Analysis - Department of Computer Science ...
1.Motivate a new abstract data type or data structure with some examples and reflection of previous knowledge. 2.Introduce a data structure, discussing both its mechanisms for how it …
Data Structures and Algorithms - Heriot-Watt University
Analysis of Algorithms 6 Comparing Algorithms Given 2 or more algorithms to solve the same problem, how do we select the best one? Some criteria for selecting an algorithm 1) Is it easy …
Data Structures and Algorithms - GitHub Pages
1.1 Runtime Analysis 1.1.1 Asymptotic Analysis (Big O) We can measure the growth rate of the time or space complexity of an algorithm using an upper bound (O(f)), lower bound ((f)) or a …
Algorithms Illuminated - Stanford University
Algorithms Illuminated, Part 1 provides an introduction to and basic literacy in the following four topics. Asymptotic analysis and big-O notation. Asymptotic notation provides the basic …
15-451 Algorithms Lectures 1-10 - CMU School of Computer …
This course is about the design and analysis of algorithms — how to design correct, efficient algorithms, and how to think clearly about analyzing correctness and running time. What is an …
CS1020 Data Structures and Algorithms I Lecture Note #13
What do we mean by Analysis of Algorithms? How to find the complexity of a program? You are expected to know... What is an algorithm? A step-by-step procedure for solving a problem. …
Design and Analysis of Algorithms Chapter 2 - Computer …
I Basic operation: the operation that contributes most towards the running time of the algorithm. Problem: Given a list of n elements and a search key K, find an element equal to K, if any. …
CSE373: Data Structures and Algorithms - University of …
• Deeply understand the basic structures used in all software – Understand the data structures and their trade-offs – Rigorously analyze the algorithms that use them (math!) – Learn how to …
Virginia Tech
PDF-1.4 %ÐÔÅØ 1 0 obj /S /GoTo /D (section*.2) >> endobj 4 0 obj (Preface) endobj 5 0 obj /S /GoTo /D (part.1) >> endobj 8 0 obj (I Preliminaries) endobj 9 0 obj /S /GoTo /D (chapter.1) >> …
The - Department of Computer Science
This is a course on the design and analysis of algorithms in tended for rst y ear graduate studen ts in computer science Its purp oses are mixed on the one hand w e wish to co v ... tal data …
Data Structures and Algorithms - Lagout.org
We have attempted to cover data structures and algorithms in the broader context of solving problems using computers. We use abstract data types informally in the
Data Structures and Algorithms in Python - GitHub Pages
Data Structures and Algorithms in Python provides an introduction to data structures and algorithms, including their design, analysis, and implementation. This book is designed for use …
Problem Solving with Algorithms and Data Structures - Auckland
Problem Solving with Algorithms and Data Structures, Release 3.0 Control constructs allow algorithmic steps to be represented in a convenient yet unambiguous way.
Advanced Data Structures and Algorithm Analysis - GitHub Pages
Aggregate analysis ☞ Accounting method ☞ Potential method Amortized Analysis
Algorithms Algorithm Analysis 1 - Virginia Tech
Algorithm Analysis Data Structures & Algorithms 3 CS@VT ©2000-2020 WD McQuain Design Considerations Given a particular problem, there are typically a number of different algorithms …
CSE373: Data Structures and Algorithms Lecture 4: Asymptotic Analysis
Analysis can be about: • The problem or the algorithm (usually algorithm) • Time or space (usually time) – Or power or dollars or … • Best-, worst-, or average-case (usually worst) • Upper-, …
ANALYSIS OF ALGORITHMS - Purdue University
Analysis of Algorithms 8 What is Pseudo-Code? • A mixture of natural language and high-level programming concepts that describes the main ideas behind a generic implementation of a …
CSE373: Data Structures and Algorithms Lecture 3: Asymptotic Analysis
Winter 2014 CSE373: Data Structure & Algorithms 15 Recurrence is T(n) = O(1) + 2T(n/2) – 1 + 2 + 4 + 8 + … for log n times – 2(log n) – 1 which is proportional to n (definition of logarithm) …
