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jeff edmonds how to think about algorithms: How to Think About Algorithms Jeff Edmonds, 2008-05-19 This textbook, for second- or third-year students of computer science, presents insights, notations, and analogies to help them describe and think about algorithms like an expert, without grinding through lots of formal proof. Solutions to many problems are provided to let students check their progress, while class-tested PowerPoint slides are on the web for anyone running the course. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author guides students around the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. The book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a careful and clear way, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems. |
jeff edmonds how to think about algorithms: Algorithms Jeff Erickson, 2019-06-13 Algorithms are the lifeblood of computer science. They are the machines that proofs build and the music that programs play. Their history is as old as mathematics itself. This textbook is a wide-ranging, idiosyncratic treatise on the design and analysis of algorithms, covering several fundamental techniques, with an emphasis on intuition and the problem-solving process. The book includes important classical examples, hundreds of battle-tested exercises, far too many historical digressions, and exaclty four typos. Jeff Erickson is a computer science professor at the University of Illinois, Urbana-Champaign; this book is based on algorithms classes he has taught there since 1998. |
jeff edmonds how to think about algorithms: 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 |
jeff edmonds how to think about algorithms: Algorithm Design Michael T. Goodrich, Roberto Tamassia, 2001-10-15 Are you looking for something different in your Algorithms text? Are you looking for an Algorithms text that offers theoretical analysis techniques as well as design patterns and experimental methods for the engineering of algorithms? Michael Goodrich and Roberto Tamassia, authors of the successful, Data Structures and Algorithms in Java, 2/e, have written Algorithm Design, a text designed to provide a comprehensive introduction to the design, implementation and analysis of computer algorithms and data structures from a modern perspective. Written for an undergraduate, junior-senior algorithms course this text offers several implementation case studies and uses Internet applications to motivate many topics such as hashing, sorting and searching. |
jeff edmonds how to think about algorithms: Too Fast to Think Chris Lewis, 2016-10-03 Our lives are getting faster and faster. We are engulfed in constant distraction from email, social media and our 'always on' work culture. We are too busy, too overloaded with information and too focused on analytical left-brain thinking processes to be creative. Too Fast to Think exposes how our current work practices, media culture and education systems are detrimental to innovation. The speed and noise of modern life is undermining the clarity and quiet that is essential to power individual thought. Our best ideas are often generated when we are free to think diffusely, in an uninterrupted environment, which is why moments of inspiration so often occur in places completely separate to our offices. To reclaim creativity, Too Fast to Think teaches you how to retrain your brain into allowing creative ideas to emerge, before they are shut down by interruption, distraction or the self-doubt of your over-rational brain. This is essential reading for anyone who wants to maximize their creative potential, as well as that of their team. Supported by cutting-edge research from the University of the Arts London and insightful interviews with business leaders, academics, artists, politicians and psychologists, Chris Lewis takes a holistic approach to explain the 8 crucial traits that are inherently linked to creation and innovation. |
jeff edmonds how to think about algorithms: Introduction to Algorithms, fourth edition Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, 2022-04-05 A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learning New material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays 140 new exercises and 22 new problems Reader feedback–informed improvements to old problems Clearer, more personal, and gender-neutral writing style Color added to improve visual presentation Notes, bibliography, and index updated to reflect developments in the field Website with new supplementary material Warning: Avoid counterfeit copies of Introduction to Algorithms by buying only from reputable retailers. Counterfeit and pirated copies are incomplete and contain errors. |
jeff edmonds how to think about algorithms: Math in Society David Lippman, 2022-07-14 Math in Society is a survey of contemporary mathematical topics, appropriate for a college-level topics course for liberal arts major, or as a general quantitative reasoning course. This book is an open textbook; it can be read free online at http://www.opentextbookstore.com/mathinsociety/. Editable versions of the chapters are available as well. |
jeff edmonds how to think about algorithms: What Can Be Computed? John MacCormick, 2018-05-01 An accessible and rigorous textbook for introducing undergraduates to computer science theory What Can Be Computed? is a uniquely accessible yet rigorous introduction to the most profound ideas at the heart of computer science. Crafted specifically for undergraduates who are studying the subject for the first time, and requiring minimal prerequisites, the book focuses on the essential fundamentals of computer science theory and features a practical approach that uses real computer programs (Python and Java) and encourages active experimentation. It is also ideal for self-study and reference. The book covers the standard topics in the theory of computation, including Turing machines and finite automata, universal computation, nondeterminism, Turing and Karp reductions, undecidability, time-complexity classes such as P and NP, and NP-completeness, including the Cook-Levin Theorem. But the book also provides a broader view of computer science and its historical development, with discussions of Turing's original 1936 computing machines, the connections between undecidability and Gödel's incompleteness theorem, and Karp's famous set of twenty-one NP-complete problems. Throughout, the book recasts traditional computer science concepts by considering how computer programs are used to solve real problems. Standard theorems are stated and proven with full mathematical rigor, but motivation and understanding are enhanced by considering concrete implementations. The book's examples and other content allow readers to view demonstrations of—and to experiment with—a wide selection of the topics it covers. The result is an ideal text for an introduction to the theory of computation. An accessible and rigorous introduction to the essential fundamentals of computer science theory, written specifically for undergraduates taking introduction to the theory of computation Features a practical, interactive approach using real computer programs (Python in the text, with forthcoming Java alternatives online) to enhance motivation and understanding Gives equal emphasis to computability and complexity Includes special topics that demonstrate the profound nature of key ideas in the theory of computation Lecture slides and Python programs are available at whatcanbecomputed.com |
jeff edmonds how to think about algorithms: Mastering Algorithms with Perl Jarkko Hietaniemi, John Macdonald, Jon Orwant, 1999-08-18 Many programmers would love to use Perl for projects that involve heavy lifting, but miss the many traditional algorithms that textbooks teach for other languages. Computer scientists have identified many techniques that a wide range of programs need, such as: Fuzzy pattern matching for text (identify misspellings!) Finding correlations in data Game-playing algorithms Predicting phenomena such as Web traffic Polynomial and spline fitting Using algorithms explained in this book, you too can carry out traditional programming tasks in a high-powered, efficient, easy-to-maintain manner with Perl.This book assumes a basic understanding of Perl syntax and functions, but not necessarily any background in computer science. The authors explain in a readable fashion the reasons for using various classic programming techniques, the kind of applications that use them, and -- most important -- how to code these algorithms in Perl.If you are an amateur programmer, this book will fill you in on the essential algorithms you need to solve problems like an expert. If you have already learned algorithms in other languages, you will be surprised at how much different (and often easier) it is to implement them in Perl. And yes, the book even has the obligatory fractal display program.There have been dozens of books on programming algorithms, some of them excellent, but never before has there been one that uses Perl.The authors include the editor of The Perl Journal and master librarian of CPAN; all are contributors to CPAN and have archived much of the code in this book there.This book was so exciting I lost sleep reading it. Tom Christiansen |
jeff edmonds how to think about algorithms: Data Structures & Algorithms in Python Robert Lafore, Alan Broder, John Canning, 2022-09-06 LEARN HOW TO USE DATA STRUCTURES IN WRITING HIGH PERFORMANCE PYTHON PROGRAMS AND ALGORITHMS This practical introduction to data structures and algorithms can help every programmer who wants to write more efficient software. Building on Robert Lafore's legendary Java-based guide, this book helps you understand exactly how data structures and algorithms operate. You'll learn how to efficiently apply them with the enormously popular Python language and scale your code to handle today's big data challenges. Throughout, the authors focus on real-world examples, communicate key ideas with intuitive, interactive visualizations, and limit complexity and math to what you need to improve performance. Step-by-step, they introduce arrays, sorting, stacks, queues, linked lists, recursion, binary trees, 2-3-4 trees, hash tables, spatial data structures, graphs, and more. Their code examples and illustrations are so clear, you can understand them even if you're a near-beginner, or your experience is with other procedural or object-oriented languages. Build core computer science skills that take you beyond merely “writing code” Learn how data structures make programs (and programmers) more efficient See how data organization and algorithms affect how much you can do with today's, and tomorrow's, computing resources Develop data structure implementation skills you can use in any language Choose the best data structure(s) and algorithms for each programming problem—and recognize which ones to avoid Data Structures & Algorithms in Python is packed with examples, review questions, individual and team exercises, thought experiments, and longer programming projects. It's ideal for both self-study and classroom settings, and either as a primary text or as a complement to a more formal presentation. |
jeff edmonds how to think about algorithms: Semialgebraic Proofs and Efficient Algorithm Design Noah Fleming, Pravesh Kothari, Toniann Pitassi, 2019-12-10 The book provides the advanced reader with a deep insight into the exciting line of research, namely, proof that a solution exists has enabled an algorithm to find that solution itself with applications in many areas of computer science. It will inspire readers in deploying the techniques in their own further research. |
jeff edmonds how to think about algorithms: Extracting Spatial Information from Historical Maps Benedikt Budig, 2018-11-23 Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable. This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale. The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century. |
jeff edmonds how to think about algorithms: 101 Algorithms Questions You Must Know Amrinder Arora, 2018-12-29 101 Algorithms Questions You Must Know presents 101 asymptotic complexity Questions and Answers, organized by Algorithm Design Techniques. Serving as a useful accompaniment to Analysis and Design of Algorithms (ISBN 978-1516513086), the questions are distributed as follows: 9 Warm up Questions on Math Basics, 19 Questions on Asymptotic Analysis and Asymptotic Notation, 3 Questions on Data Structures, 17 Questions on Divide and Conquer, 8 Questions on Greedy Algorithms, 18 Questions on Dynamic Programming, 5 Questions on Graph Traversal (BFS/DFS), 4 Questions on Branch and Bound, 9 Questions on NP-Completeness 3 Questions on Lower Bounds, and 6 Questions on Graph Theory.Covering many questions used by major technology companies as their interview questions, this book serves both software professionals as well as graduate students in the field. |
jeff edmonds how to think about algorithms: Introduction to the Theory of Computation Michael Sipser, 2005-02-15 This highly anticipated revision builds upon the strengths of the previous edition. Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
jeff edmonds how to think about algorithms: Good Economics for Hard Times Abhijit V. Banerjee, Esther Duflo, 2019-11-12 The winners of the Nobel Prize show how economics, when done right, can help us solve the thorniest social and political problems of our day. Figuring out how to deal with today's critical economic problems is perhaps the great challenge of our time. Much greater than space travel or perhaps even the next revolutionary medical breakthrough, what is at stake is the whole idea of the good life as we have known it. Immigration and inequality, globalization and technological disruption, slowing growth and accelerating climate change--these are sources of great anxiety across the world, from New Delhi and Dakar to Paris and Washington, DC. The resources to address these challenges are there--what we lack are ideas that will help us jump the wall of disagreement and distrust that divides us. If we succeed, history will remember our era with gratitude; if we fail, the potential losses are incalculable. In this revolutionary book, renowned MIT economists Abhijit V. Banerjee and Esther Duflo take on this challenge, building on cutting-edge research in economics explained with lucidity and grace. Original, provocative, and urgent, Good Economics for Hard Times makes a persuasive case for an intelligent interventionism and a society built on compassion and respect. It is an extraordinary achievement, one that shines a light to help us appreciate and understand our precariously balanced world. |
jeff edmonds how to think about algorithms: Randomized Algorithms Rajeev Motwani, Prabhakar Raghavan, 1995-08-25 For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students. |
jeff edmonds how to think about algorithms: Foundations of Algorithms Richard Neapolitan, Kumarss Naimipour, 2009-12-28 Foundations of Algorithms, Fourth Edition offers a well-balanced presentation of algorithm design, complexity analysis of algorithms, and computational complexity. The volume is accessible to mainstream computer science students who have a background in college algebra and discrete structures. To support their approach, the authors present mathematical concepts using standard English and a simpler notation than is found in most texts. A review of essential mathematical concepts is presented in three appendices. The authors also reinforce the explanations with numerous concrete examples to help students grasp theoretical concepts. |
jeff edmonds how to think about algorithms: Datatype-Generic Programming Roland Backhouse, 2007-11-30 This tutorial book presents six carefully revised lectures given at the Spring School on Datatype-Generic Programming, SSDGP 2006. This was held in Nottingham, UK, in April 2006. It was colocated with the Symposium on Trends in Functional Programming (TFP 2006), and the Conference of the Types Project (TYPES 2006). All the lectures have been subjected to thorough internal review by the editors and contributors, supported by independent external reviews. |
jeff edmonds how to think about algorithms: The Design and Analysis of Computer Algorithms Alfred V. Aho, John E. Hopcroft, 1974-09 |
jeff edmonds how to think about algorithms: Theoretical Aspects of Local Search Wil Michiels, Emile Aarts, Jan Korst, 2007-01-17 Local search has been applied successfully to a diverse collection of optimization problems. However, results are scattered throughout the literature. This is the first book that presents a large collection of theoretical results in a consistent manner. It provides the reader with a coherent overview of the achievements obtained so far, and serves as a source of inspiration for the development of novel results in the challenging field of local search. |
jeff edmonds how to think about algorithms: Geometric Folding Algorithms Erik D. Demaine, Joseph O'Rourke, 2008-08-21 How can linkages, pieces of paper, and polyhedra be folded? The authors present hundreds of results and over 60 unsolved 'open problems' in this comprehensive look at the mathematics of folding, with an emphasis on algorithmic or computational aspects. Folding and unfolding problems have been implicit since Albrecht Dürer in the early 1500s, but have only recently been studied in the mathematical literature. Over the past decade, there has been a surge of interest in these problems, with applications ranging from robotics to protein folding. A proof shows that it is possible to design a series of jointed bars moving only in a flat plane that can sign a name or trace any other algebraic curve. One remarkable algorithm shows you can fold any straight-line drawing on paper so that the complete drawing can be cut out with one straight scissors cut. Aimed primarily at advanced undergraduate and graduate students in mathematics or computer science, this lavishly illustrated book will fascinate a broad audience, from high school students to researchers. |
jeff edmonds how to think about algorithms: Algorithms Unlocked Thomas H. Cormen, 2013-03-01 For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms. Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen—coauthor of the leading college textbook on the subject—provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems. Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order (“sorting”); how to solve basic problems that can be modeled in a computer with a mathematical structure called a “graph” (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time. |
jeff edmonds how to think about algorithms: Precalculus David Lippman, Melonie Rasmussen, 2017-06-26 The first half of the second edition of Precalculus: An Investigation of Functions. This is an open textbook, available free online. This first portion of the book (Chapters 1-4) is an investigation of functions, exploring the graphical behavior of, interpretation of, and solutions to problems involving linear, polynomial, rational, exponential, and logarithmic functions. An emphasis is placed on modeling and interpretation, as well as the important characteristics needed in calculus. |
jeff edmonds how to think about algorithms: Computational and Cognitive Models of Creative Design VI John Steven Gero, Mary Lou Maher, 2005 |
jeff edmonds how to think about algorithms: Extremal Combinatorics Stasys Jukna, 2013-03-09 Combinatorial mathematics has been pursued since time immemorial, and at a reasonable scientific level at least since Leonhard Euler (1707-1783). It ren dered many services to both pure and applied mathematics. Then along came the prince of computer science with its many mathematical problems and needs - and it was combinatorics that best fitted the glass slipper held out. Moreover, it has been gradually more and more realized that combinatorics has all sorts of deep connections with mainstream areas of mathematics, such as algebra, geometry and probability. This is why combinatorics is now apart of the standard mathematics and computer science curriculum. This book is as an introduction to extremal combinatorics - a field of com binatorial mathematics which has undergone aperiod of spectacular growth in recent decades. The word extremal comes from the nature of problems this field deals with: if a collection of finite objects (numbers, graphs, vectors, sets, etc. ) satisfies certain restrictions, how large or how small can it be? For example, how many people can we invite to a party where among each three people there are two who know each other and two who don't know each other? An easy Ramsey-type argument shows that at most five persons can attend such a party. Or, suppose we are given a finite set of nonzero integers, and are asked to mark an as large as possible subset of them under the restriction that the sum of any two marked integers cannot be marked. |
jeff edmonds how to think about algorithms: Programming Challenges Steven S Skiena, Miguel A. Revilla, 2006-04-18 There are many distinct pleasures associated with computer programming. Craftsmanship has its quiet rewards, the satisfaction that comes from building a useful object and making it work. Excitement arrives with the flash of insight that cracks a previously intractable problem. The spiritual quest for elegance can turn the hacker into an artist. There are pleasures in parsimony, in squeezing the last drop of performance out of clever algorithms and tight coding. The games, puzzles, and challenges of problems from international programming competitions are a great way to experience these pleasures while improving your algorithmic and coding skills. This book contains over 100 problems that have appeared in previous programming contests, along with discussions of the theory and ideas necessary to attack them. Instant online grading for all of these problems is available from two WWW robot judging sites. Combining this book with a judge gives an exciting new way to challenge and improve your programming skills. This book can be used for self-study, for teaching innovative courses in algorithms and programming, and in training for international competition. The problems in this book have been selected from over 1,000 programming problems at the Universidad de Valladolid online judge. The judge has ruled on well over one million submissions from 27,000 registered users around the world to date. We have taken only the best of the best, the most fun, exciting, and interesting problems available. |
jeff edmonds how to think about algorithms: Python for Everybody : Exploring Data Using Python 3 , 2009 |
jeff edmonds how to think about algorithms: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
jeff edmonds how to think about algorithms: Algorithm Design Jon Kleinberg, Éva Tardos, 2012-02-28 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. Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age. |
jeff edmonds how to think about algorithms: Algorithms in Java Robert Sedgewick, 2003 In these volumes, Robert Sedgewick focuses on practical applications, giving readers all the information, diagrams and real code they need to confidently implement, debug and use the algorithms he presents. |
jeff edmonds how to think about algorithms: Mobile Mapping Clancy Wilmott, 2020 This book argues for a theory of mobile mapping, a situated and spatial approach towards researching how everyday digital mobile media practices are bound up in global systems of knowledge and power. Drawing from literature in media studies and geography -- and the work of Michel Foucault and Doreen Massey -- it examines how geographical and historical material, social, and cultural conditions are embedded in the way in which contemporary (digital) cartographies are read, deployed, and engaged. This is explored through seventeen walking interviews in Hong Kong and Sydney, as potent discourses like cartographic reason continue to transform and weave through the world in ways that haunt mobile mapping and bring old conflicts into new media. In doing so, Mobile Mapping offers an interdisciplinary rethinking about how multiple translations of spatial knowledges between rational digital epistemologies and tacit ways of understanding space and experience might be conceptualized and researched. |
jeff edmonds how to think about algorithms: Communication Complexity Eyal Kushilevitz, Noam Nisan, 1997 Surveys the mathematical theory and applications such as computer networks, VLSI circuits, and data structures. |
jeff edmonds how to think about algorithms: Algorithm Design and Applications Michael T. Goodrich, Roberto Tamassia, 2014-11-03 ALGORITHM DESIGN and APPLICATIONS “This is a wonderful book, covering both classical and contemporary topics in algorithms. I look forward to trying it out in my algorithms class. I especially like the diversity in topics and difficulty of the problems.” ROBERT TARJAN, PRINCETON UNIVERSITY “The clarity of explanation is excellent. I like the inclusion of the three types of exercises very much.” MING-YANG KAO, NORTHWESTERN UNIVERSITY “Goodrich and Tamassia have designed a book that is both remarkably comprehensive in its coverage and innovative in its approach. Their emphasis on motivation and applications, throughout the text as well as in the many exercises, provides a book well-designed for the boom in students from all areas of study who want to learn about computing. The book contains more than one could hope to cover in a semester course, giving instructors a great deal of flexibility and students a reference that they will turn to well after their class is over.” MICHAEL MITZENMACHER, HARVARD UNIVERSITY “I highly recommend this accessible roadmap to the world of algorithm design. The authors provide motivating examples of problems faced in the real world and guide the reader to develop workable solutions, with a number of challenging exercises to promote deeper understanding.” JEFFREY S. VITTER, UNIVERSITY OF KANSAS DidYouKnow? This book is available as a Wiley E-Text. The Wiley E-Text is a complete digital version of the text that makes time spent studying more efficient. Course materials can be accessed on a desktop, laptop, or mobile device—so that learning can take place anytime, anywhere. A more affordable alternative to traditional print, the Wiley E-Text creates a flexible user experience: Access on-the-go Search across content Highlight and take notes Save money! The Wiley E-Text can be purchased in the following ways: Via your campus bookstore: Wiley E-Text: Powered by VitalSource® ISBN 9781119028796 *Instructors: This ISBN is needed when placing an order. Directly from: www.wiley.com/college/goodrich |
jeff edmonds how to think about algorithms: Unlearn Your Pain Howard Schubiner, Michael Betzold, 2010-03-19 |
jeff edmonds how to think about algorithms: Computational Complexity , 1998 |
jeff edmonds how to think about algorithms: Graph Theory Karin R. Saoub, 2021 Graph Theory: An Introduction to Proofs, Algorithms, and Applications Graph theory is the study of interactions, conflicts, and connections. The relationship between collections of discrete objects can inform us about the overall network in which they reside, and graph theory can provide an avenue for analysis. This text, for the first undergraduate course, will explore major topics in graph theory from both a theoretical and applied viewpoint. Topics will progress from understanding basic terminology, to addressing computational questions, and finally ending with broad theoretical results. Examples and exercises will guide the reader through this progression, with particular care in strengthening proof techniques and written mathematical explanations. Current applications and exploratory exercises are provided to further the reader's mathematical reasoning and understanding of the relevance of graph theory to the modern world. Features The first chapter introduces graph terminology, mathematical modeling using graphs, and a review of proof techniques featured throughout the book The second chapter investigates three major route problems: eulerian circuits, hamiltonian cycles, and shortest paths. The third chapter focuses entirely on trees - terminology, applications, and theory. Four additional chapters focus around a major graph concept: connectivity, matching, coloring, and planarity. Each chapter brings in a modern application or approach. Hints and Solutions to selected exercises provided at the back of the book. Author Karin R. Saoub is an Associate Professor of Mathematics at Roanoke College in Salem, Virginia. She earned her PhD in mathematics from Arizona State University and BA from Wellesley College. Her research focuses on graph coloring and on-line algorithms applied to tolerance graphs. She is also the author of A Tour Through Graph Theory, published by CRC Press. |
jeff edmonds how to think about algorithms: The Machine as Art/ The Machine as Artist , 2020 The articles collected in this volume from the two companion Arts Special Issues, “The Machine as Art (in the 20th Century)” and “The Machine as Artist (in the 21st Century)”, represent a unique scholarly resource: analyses by artists, scientists, and engineers, as well as art historians, covering not only the current (and astounding) rapprochement between art and technology but also the vital post-World War II period that has led up to it; this collection is also distinguished by several of the contributors being prominent individuals within their own fields, or as artists who have actually participated in the still unfolding events with which it is concerned. |
jeff edmonds how to think about algorithms: Optimization for Data Analysis Stephen J. Wright, Benjamin Recht, 2021 Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s; problems of this type drove developments in unconstrained optimization in the latter half of the 20th century. Mangasarian's papers in the 1960s on pattern separation using linear programming made an explicit connection between machine learning and optimization in the early days of the former subject. During the 1990s, optimization techniques (especially quadratic programming and duality) were key to the development of support vector machines and kernel learning. The period 1997-2010 saw many synergies emerge between regularized / sparse optimization, variable selection, and compressed sensing. In the current era of deep learning, two optimization techniques-stochastic gradient and automatic differentiation (a.k.a. back-propagation)-are essential-- |
jeff edmonds how to think about algorithms: Data Structures and Problem Solving Using Java Mark Allen Weiss, 2010-01 A practical and unique approach to data structures that separates interface from implementation, this book provides a practical introduction to data structures with an emphasis on abstract thinking and problem solving, as well as the use of Java. |
Jeff Bezos - Wikipedia
Jeffrey Preston Bezos (/ ˈbeɪzoʊs / BAY-zohss; [2] né Jorgensen; born January 12, 1964) is an American businessman best known as the founder, executive chairman, and former president …
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Aug 25, 2023 · Jeff Bezos began his career as a hedge funder in New York before leaving to start Amazon—and became one of the world's richest people. Read more about Bezos, here.
Jeff Bezos - Forbes
5 days ago · Jeff Bezos founded e-commerce giant Amazon in 1994 out of his Seattle garage. Bezos stepped down as CEO to become executive chairman in 2021. He owns a bit less than …
Jeff Bezos - Wikiwand
Jeffrey Preston Bezos (/ ˈbeɪzoʊs / BAY-zohss; [2] né Jorgensen; born January 12, 1964) is an American businessman best known as the founder, executive chairman, and former president …
Jeffrey P. Bezos | Academy of Achievement
Jan 17, 2025 · Jeff Bezos, the King of cyber-commerce, and founder of the world’s largest online shopping retailer. (Amazon.com)
Jeff Bezos - Latest News and Updates - WSJ.com
Jeff Bezos is executive chairman of Amazon, the company he founded in 1994. He was chief executive of the tech giant until 2021, when Andy Jassy took over.
Jeff Bezos - Wikipedia
Jeffrey Preston Bezos (/ ˈbeɪzoʊs / BAY-zohss; [2] né Jorgensen; born January 12, 1964) is an American businessman best known as the founder, executive chairman, and former president …
Jeff Bezos | Biography, Amazon, & Facts | Britannica Money
4 days ago · Jeff Bezos (born January 12, 1964, Albuquerque, New Mexico, U.S.) is an American entrepreneur who played a key role in the growth of e-commerce as the founder and chief …
Jeff Bezos: Biography, Amazon Founder, Blue Origin Founder
May 23, 2023 · American entrepreneur Jeff Bezos is the founder of Amazon and space exploration company Blue Origin. His successful business ventures have made him one of the …
A Dream Wedding for Jeff Bezos in Venice? No Way, Locals Say
2 days ago · If a bunch of irate Venetians have their way, the star-studded dream wedding that Jeff Bezos, the Amazon founder, and his fiancée, Lauren Sánchez, are planning in the ancient …
Bloomberg Billionaires Index - Jeff Bezos
6 days ago · Jeff Bezos is a former Wall Street computer engineer who created Amazon in 1994 to sell books online. After an initial public offering in 1997, Amazon stock shot up almost 40 …
Who Is Jeff Bezos? Inside the Billionaire's Career Path, Life ...
Aug 25, 2023 · Jeff Bezos began his career as a hedge funder in New York before leaving to start Amazon—and became one of the world's richest people. Read more about Bezos, here.
Jeff Bezos - Forbes
5 days ago · Jeff Bezos founded e-commerce giant Amazon in 1994 out of his Seattle garage. Bezos stepped down as CEO to become executive chairman in 2021. He owns a bit less than …
Jeff Bezos - Wikiwand
Jeffrey Preston Bezos (/ ˈbeɪzoʊs / BAY-zohss; [2] né Jorgensen; born January 12, 1964) is an American businessman best known as the founder, executive chairman, and former president …
Jeffrey P. Bezos | Academy of Achievement
Jan 17, 2025 · Jeff Bezos, the King of cyber-commerce, and founder of the world’s largest online shopping retailer. (Amazon.com)
Jeff Bezos - Latest News and Updates - WSJ.com
Jeff Bezos is executive chairman of Amazon, the company he founded in 1994. He was chief executive of the tech giant until 2021, when Andy Jassy took over.