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Algorithms and Data Structures
The papers in this volume were presented at the Sixth Workshop on Algorithms and Data Structures (WADS '99). The workshop took place August 11 - 14, 1999, in Vancouver, Canada. The workshop alternates with the Scandinavian Workshop on Algorithms Theory (SWAT), continuing the tradition of SWAT and WADS starting with SWAT'88 and WADS'89. In response to the program committee's call for papers, 71 papers were submitted. From these submissions, the program committee selected 32 papers for presentation at the workshop. In addition to these submitted papers, the program committee invited the following researchers to give plenary lectures at the workshop: C. Leiserson, N. Magnenat-Thalmann, M. Snir, U. Vazarani, and 1. Vitter. On behalf of the program committee, we would like to express our appreciation to the six plenary lecturers who accepted our invitation to speak, to all the authors who submitted papers to W ADS'99, and to the Pacific Institute for Mathematical Sciences for their sponsorship. Finally, we would like to express our gratitude to all the people who reviewed papers at the request of the program committee. August 1999 F. Dehne A. Gupta J.-R. Sack R. Tamassia VI Conference Chair: A. Gupta Program Committee Chairs: F. Dehne, A. Gupta, J.-R. Sack, R. Tamassia Program Committee: A. Andersson, A. Apostolico, G. Ausiello, G. Bilardi, K. Clarkson, R. Cleve, M. Cosnard, L. Devroye, P. Dymond, M. Farach-Colton, P. Fraigniaud, M. Goodrich, A.
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Textbook of Data Structures and Algorithms, Volume 2
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.
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Algorithms and Data Structures
This book contains the papers presented at the 1989 Workshop on Algorithms and Data Structures, which succeeds the 1988 Scandinavian Workshop on Algorithm Theory. It presents current research in various areas of algorithms, computational geometry, geometric searching, VLSI placement and routing, graph algorithms, parallel algorithms, distributed algorithms, databases, and text searching.
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Codeless Data Structures and Algorithms
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 queuesWho 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.
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Hands-On Data Structures and Algorithms with Rust
Design and implement professional level programs by exploring modern data structures and algorithms in Rust.Key FeaturesUse data structures such as arrays, stacks, trees, lists and graphs with real-world examplesLearn the functional and reactive implementations of the traditional data structuresExplore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner.Book DescriptionRust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications.What you will learnDesign and implement complex data structures in RustAnalyze, implement, and improve searching and sorting algorithms in RustCreate and use well-tested and reusable components with RustUnderstand the basics of multithreaded programming and advanced algorithm designBecome familiar with application profiling based on benchmarking and testingExplore the borrowing complexity of implementing algorithmsWho this book is forThis book is for developers seeking to use Rust solutions in a practical/professional setting; who wants to learn essential Data Structures and Algorithms in Rust. It is for developers with basic Rust language knowledge, some experience in other programming languages is required.
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Algorithms and Data Structures
This volume presents the proceedings of the Second Workshop on Algorithms and Data Structures (WADS '91), held at Carleton University in Ottawa. The workshop was organized by the School of Computer Science at Carleton University. The workshop alternates with the Scandinavian Workshop on Algorithm Theory (SWAT), continuing the tradition of SWAT '88 (LNCS, Vol. 318), WADS '89 (LNCS, Vol. 382), and SWAT '90 (LNCS, Vol. 447). From 107 papers submitted, 37 were selected for presentation at the workshop. In addition, there were 5 invited presentations.
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Data Science Algorithms in a Week
Build a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well.Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis.By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problemWhat you will learnUnderstand how to identify a data science problem correctlyImplement well-known machine learning algorithms efficiently using PythonClassify your datasets using Naive Bayes, decision trees, and random forest with accuracyDevise an appropriate prediction solution using regressionWork with time series data to identify relevant data events and trendsCluster your data using the k-means algorithmWho this book is forThis book is for aspiring data science professionals who are familiar with Python and have a little background in statistics. You’ll also find this book useful if you’re currently working with data science algorithms in some capacity and want to expand your skill set
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Beginning Java Data Structures and Algorithms
Though your application serves its purpose, it might not be a high performer. Learn techniques to accurately predict code efficiency, easily dismiss inefficient solutions, and improve the performance of your application.Key FeaturesExplains in detail different algorithms and data structures with sample problems and Java implementations where appropriateIncludes interesting tips and tricks that enable you to efficiently use algorithms and data structuresCovers over 20 topics using 15 practical activities and exercises Book DescriptionLearning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems.This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You’ll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications.What you will learnUnderstand some of the fundamental concepts behind key algorithmsExpress space and time complexities using Big O notation.Correctly implement classic sorting algorithms such as merge and quicksortCorrectly implement basic and complex data structuresLearn about different algorithm design paradigms, such as greedy, divide and conquer, and dynamic programmingApply powerful string matching techniques and optimize your application logicMaster graph representations and learn about different graph algorithmsWho this book is forIf you want to better understand common data structures and algorithms by following code examples in Java and improve your application efficiency, then this is the book for you. It helps to have basic knowledge of Java, mathematics and object-oriented programming techniques.
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Algorithms and Data Structures
The papers in this volume were presented at the 9th Workshop on Algorithms and Data Structures (WADS 2005). The workshop took place during August 15 - 17, 2005, at the University of Waterloo, Waterloo, Canada. The workshop alternateswith the ScandinavianWorkshopon Algorithm Theory(SWAT), c- tinuing the traditionof SWAT and WADS startingwith SWAT 1988and WADS 1989. From 90 submissions, the Program Committee selected 37 papers for p- sentation at the workshop. In addition, invited lectures were given by the f- lowing distinguished researchers: Allan Borodin and Max J. Egenhofer. OnbehalfoftheProgramCommittee,wewouldliketoexpressoursincere- preciation to the many persons whose e?ort contributed to making WADS 2005 a success. These include the invited speakers,members of the ste- ing and ProgramCommittees, the authors who submitted papers, and the many referees who assisted the ProgramCommittee. We are indebted to Robert Kane forinstallingandmodifyingthesubmissionsoftware,maintainingthesubmission server and interacting with authors as well as for helping with the preparation of the program. August 2005 Frank Dehne, Alejandro Lop ' ez-Ortiz, and Jorg-R .. u ..diger Sack WADS Organization Organizing Institutions Steering Committee Frank Dehne Carleton University, Canada Ian Munro University of Waterloo, Canada J.. org-Rudig .. er Sack Carleton University, Canada Roberto Tamassia Brown University, Canada Program Co-chairs Frank Dehne Carleton University, Canada Alejandro Lop ' ez-Ortiz University of Waterloo, Canada J.. org-Rudig .. er Sack Carleton University, Canada Conference Chair Alejandro Lop ' ez-Ortiz University of Waterloo, Canada Program Committee Pankaj Agarwal Duke University, USA Michael Atkinson University of Otago, New Zealand Gill Barequet Technion, Israel Mark de Berg Tech.
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Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
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Algorithms and Data Structures
This book constitutes the refereed proceedings of the 13th Algorithms and Data Structures Symposium, WADS 2013, held in London, ON, Canada, August 2013. The Algorithms and Data Structures Symposium - WADS (formerly "Workshop on Algorithms and Data Structures") is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. The 44 revised full papers presented in this volume were carefully reviewed and selected from 139 submissions. The papers present original research on algorithms and data structures in all areas, including bioinformatics, combinatorics, computational geometry, databases, graphics, and parallel and distributed computing.
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Algorithms and Data Structures
This book constitutes the refereed proceedings of the 18th International Symposium on Algorithms and Data Structures, WADS 2023, held during July 31-August 2, 2023. The 47 regular papers, presented in this book, were carefully reviewed and selected from a total of 92 submissions. They present original research on the theory, design and application of algorithms and data structures.
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Data Structures And Algorithms Using Java
With an accessible writing style and manageable amount of content, Data Structures and Algorithms Using Java is the ideal text for your course. This outstanding text correlates to the recommended syllabus put forth by the Association of Computing Machinery standard curriculum guidelines. The author has produced a resource that is more readable and instructional than any other, without compromising the scope of the ACM CS103, Data Structures and Algorithms, course material. The text’s unique, student-friendly pedagogical approach and organizational structure will keep students engaged in the process of self-directed investigative discovery both inside and outside the classroom.The pedagogical features of the text, based on the author’s 30 years of teaching experience, include succinct code examples, a unique common template used as the organizational basis of each chapter, the use of pseudocode to present the major algorithms developed in the text, nearly 300 carefully designed figures, and a concise review of Java.
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Problems on Algorithms
With approximately 2500 problems, this book provides a collection of practical problems on the basic and advanced data structures, design, and analysis of algorithms. To make this book suitable for self-instruction, about one-third of the algorithms are supported by solutions, and some others are supported by hints and comments. This book is intended for students wishing to deepen their knowledge of algorithm design in an undergraduate or beginning graduate class on algorithms, for those teaching courses in this area, for use by practicing programmers who wish to hone and expand their skills, and as a self-study text for graduate students who are preparing for the qualifying examination on algorithms for a Ph.D. program in Computer Science or Computer Engineering. About all, it is a good source for exam problems for those who teach algorithms and data structure. The format of each chapter is just a little bit of instruction followed by lots of problems. This book is intended to augment the problem sets found in any standard algorithms textbook. This book • begins with four chapters on background material that most algorithms instructors would like their students to have mastered before setting foot in an algorithms class. The introductory chapters include mathematical induction, complexity notations, recurrence relations, and basic algorithm analysis methods. • provides many problems on basic and advanced data structures including basic data structures (arrays, stack, queue, and linked list), hash, tree, search, and sorting algorithms. • provides many problems on algorithm design techniques: divide and conquer, dynamic programming, greedy algorithms, graph algorithms, and backtracking algorithms. • is rounded out with a chapter on NP-completeness.
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Data Structures and Algorithms in Java
Learn with confidence with this hands-on undergraduate textbook for CS2 courses. Active-learning and real-world projects underpin each chapter, briefly reviewing programming fundamentals then progressing to core data structures and algorithms topics including recursion, lists, stacks, trees, graphs, sorting, and complexity analysis. Creative projects and applications put theoretical concepts into practice, helping students master the fundamentals. Dedicated project chapters supply further programming practice using real-world, interdisciplinary problems which students can showcase in their own online portfolios. Example Interview Questions sections prepare students for job applications. The pedagogy supports self-directed and skills-based learning with over 250 'Try It Yourself' boxes, many with solutions provided, and over 500 progressively challenging end-of-chapter questions. Written in a clear and engaging style, this textbook is a complete resource for teaching the fundamental skills that today's students need. Instructor resources are available online, including a test bank, solutions manual, and sample code.
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Algorithms and Data Structures
This volume contains the papers presented at the 11th Algorithms and Data Structures Symposium, WADS 2009 (formerly Workshop on Algorithms and Data Structures), held during August 21-23, 2009 in Ban?, Alberta, Canada. WADS alternates with the Scandinavian Workshop on Algorithms Theory (SWAT), continuing the tradition of SWAT and WADS starting with SWAT 1988 and WADS 1989. Inresponsetothecallforpapers,126papersweresubmitted. Fromtheses- missions, the ProgramCommittee selected 49 papers for presentation at WADS 2009. In addition, invited lectures were given by the following distinguished researchers: Erik Demaine, Richard Karp, and Christos Papadimitriou. On behalf of the Program Committee, we would like to express our appre- ation to the invited speakers, reviewers and all authors who submitted papers. May 2009 Frank Dehne Marina Gavrilova J.. org-Ru ..diger Sack Csaba D. T' oth Conference Organization Program Chairs Frank Dehne Marina Gavrilova Joerg-Ruediger Sack Csaba D.Toth Program Committee Sergei Bereg Allan Borodin Gerth Stolting Brodal Timothy Chan Mark de Berg Frank Devai Matt Duckham Rolf Fagerberg Randolph Franklin Joachim Gudmundsson Susanne Hambrusch Rolf Klein Mike Langston Ming Li Friedhelm Meyer auf der Heide Ian Munro Jon Rokne Shashi Shekhar Bettina Speckmann Paul Spirakis Gabor Tardos Je? Vitter Frances F. Yao Local Organization Marina Gavrilova Csaba D. Toth Jon Rokne Maruf Monwar Kushan Ahmadian Table of Contents On the Power of the Semi-Separated Pair Decomposition...1 Mohammad Ali Abam, Paz Carmi, Mohammad Farshi, and Michiel Smid Plane Graphs with Parity Constraints...
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50 Algorithms Every Programmer Should Know - Second Edition
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
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Algorithms and Data Structures
This book constitutes the refereed proceedings of the 5th International Workshop on Algorithms and Data Structures, WADS'97, held in Nova Scotia, Halifax, Canada, in August 1997.The 37 revised full papers presented were carefully selected from a total of 81 submissions. Also included are four abstracts and one full contribution corresponding to the invited talks. Among the topics covered are data structures and algorithmic aspects in a variety of areas like computational geometry, graph theory, networking, load balancing, optimization, approximation, sorting, pattern matching, etc.
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Basic Concepts in Data Structures
Data structures are the fundamental building blocks of any computer program, used for storing, representing and manipulating data in a computer. This text presents the basic concepts of data structures as part of the art of writing computer programs. The author concentrates on the fundamentals of what should be done to solve a problem efficiently rather than technical implementation details. The text includes pseudocode and covers all the most common data structures, such as queues, stacks, trees, heaps, and hash tables, along with the basics of searching, sorting, and coding. This book can supplement any undergraduate algorithms or discrete math course and will also be accessible to students and researchers in other quantitative disciplines. No prerequisites beyond introductory programming and basic mathematics are required, and the accessible, conceptual presentation will appeal to undergraduates in many quantitative fields. Carefully designed problem sets with selected solutions will allow students to test their understanding.
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Data Structures and Algorithms Using Visual Basic.NET
This is the first Visual Basic.NET book to provide a comprehensive discussion of the major data structures and algorithms. Here, instead of having to translate material on C++ or Java, the professional or student VB.NET programmer will find a tutorial on how to use data structures and algorithms and a reference for implementation using VB.NET for data structures and algorithms from the .NET Framework Class Library as well as those which must be developed by the programmer. In an object-oriented fashion, the author presents arrays and arraylists, linked lists, hash tables, dictionaries, trees, graphs, sorting and searching as well as more advanced algorithms, such as probabilistic algorithms and dynamic programming. His approach is very practical, for example using timing tests rather than Big O analysis to compare the performance of data structures and algorithms. This book can be used in both beginning and advanced computer programming courses that use the VB.NET language and, most importantly, by the professional VB programmer.
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Head First Algorithms and Data Structures
What will you learn from this book?Successful software engineers all have this in common: They're fluent in the language of algorithms and data structures. Head First Algorithms and Data Structures is your gateway to becoming a part of this dynamic world. Guided by the expert insights of Eric Freeman and Elisabeth Robson, you'll unravel the complexities of algorithms and data structures through games, puzzles, and other entertaining challenges that will have you coding efficiently, scaling effectively, and acing your technical interviews in no time.What's so special about this book?If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. Whether you're taking your first steps in programming or seeking to hone your skills, you'll solidify your algorithmic thinking through a multisensory experience that engages your mind—not a text-heavy approach that puts you to sleep.
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