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  Ragab:Partial Quicksort and weighted br

  Erscheinungsdatum: 20.03.2015, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Partial Quicksort and weighted branching process, Titelzusatz: Surveys & Analysis, Autor: Ragab, Mahmoud, Verlag: LAP Lambert Academic Publishing, Sprache: Englisch, Rubrik: Mathematik // Sonstiges, Seiten: 100, Informationen: Paperback, Gewicht: 167 gr, Verkäufer: averdo

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  • Price: 44.09 EUR excl. shipping
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  Wild:Java 7's Dual-Pivot Quicksort

  Erscheinungsdatum: 11.12.2014, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Java 7's Dual-Pivot Quicksort, Titelzusatz: An average-case analysis of the new default sorting method of Java and how it compares to classic algorithms, Autor: Wild, Sebastian, Verlag: AV Akademikerverlag, Sprache: Englisch, Rubrik: Informatik, Seiten: 208, Informationen: Paperback, Gewicht: 326 gr, Verkäufer: averdo

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  Random permutation statistics

  Erscheinungsdatum: 21.01.2010, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Random permutation statistics, Titelzusatz: Permutation Group, Random Permutation, Selection Algorithm, Quicksort, Fundamental Theorem of Combinatorial Enumeration, Generating Function, Formal Power Series, Redaktion: Surhone, Lambert M. // Timpledon, Miriam T. // Marseken, Susan F., Verlag: VDM Verlag Dr. Müller e.K., Sprache: Englisch, Rubrik: Mathematik // Sonstiges, Seiten: 144, Informationen: Paperback, Gewicht: 233 gr, Verkäufer: averdo

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  • Price: 40.29 EUR excl. shipping
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  Partial Quicksort and weighted branching process

  Partial Quicksort and weighted branching process ab 49.99 € als Taschenbuch: Surveys & Analysis. Aus dem Bereich: Bücher, Wissenschaft, Mathematik,

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  • Price: 49.99 EUR excl. shipping
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  Java 7's Dual-Pivot Quicksort

  Java 7's Dual-Pivot Quicksort ab 45.99 € als Taschenbuch: An average-case analysis of the new default sorting method of Java and how it compares to classic algorithms. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,

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  • Price: 45.99 EUR excl. shipping
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  Laufzeitvergleich von Such- und Sortieralgorithmen am Beispiel der Binären Suche und Quicksort

  Laufzeitvergleich von Such- und Sortieralgorithmen am Beispiel der Binären Suche und Quicksort ab 12.99 € als pdf eBook: . Aus dem Bereich: eBooks, Belletristik, Erzählungen,

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  • Price: 12.99 EUR excl. shipping
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  Algorithmen kapieren (eBook, PDF)

  Visuelle Erläuterungen mit über 400 erklärenden Bildern Mit anschaulichen Beispielen und zahlreichen Übungen Ausführlich kommentierter Beispielcode in Python Ab sofort sind Algorithmen nicht mehr langweilig und trocken! Mit diesem Buch wird es dir Spaß machen, dich mit Algorithmen zu beschäftigen, und es wird dir leichtfallen zu verstehen, wie diese funktionieren. Du erhältst eine anschauliche Einführung in Algorithmen und lernst visuell und praxisnah, wie du die wichtigsten Algorithmen für Aufgaben einsetzt, die dir bei der Programmierung täglich begegnen. Du beginnst mit einfachen Aufgaben wie Sortieren und Suchen. Mit diesen Grundlagen gerüstet kannst du auch schwierigere Aufgaben wie dynamische Programmierung oder Künstliche Intelligenz in Angriff nehmen. Der Autor erläutert die Funktionsweise der Algorithmen anhand ganz einfacher Beispiele. So verdeutlicht er z.B. den Unterschied zwischen Arrays und verketteten Listen anhand der Aufgabe, mehrere noch freie Plätze in einem Kinosaal zu finden. Solche Beispiele zeigen dir ganz anschaulich, wie und wofür du die jeweiligen Algorithmen effektiv einsetzen kannst. Zu allen Erläuterungen findest du anschauliche Bilder und Diagramme sowie ausführlich kommentierten Beispielcode in Python. Wenn du Algorithmen verstehen möchtest, ohne dich mit komplizierten seitenlangen Beweisen herumzuplagen, ist dieses Buch genau das richtige für dich. Stimmen zum Buch: »Das Buch schafft das Unmögliche: Mathe macht Spaß und ist einfach.« (Sander Rossel, COAS Software Systems) »Möchten Sie Algorithmen auf die gleiche Art und Weise kennenlernen, wie Sie Ihren Lieblingsroman lesen? Dann brauchen Sie dieses Buch.« (Sankar Ramanathan, IBM Analytics »Heutzutage gibt es praktisch keinen Aspekt des Lebens, der nicht durch einen Algorithmus optimiert wird. Lesen Sie zuerst dieses Buch, wenn Sie eine gut erklärte Einführung in dieses Thema suchen.« (Amit Lamba, Tech Overture, LLC »Algorithmen sind nicht langweilig! Die Lektüre des Buchs hat mir und meinen Studenten Spaß gemacht und war lehrreich.« (Christopher Haupt, Mobirobo, Inc) Aus dem Inhalt: Such-, Sortier- und Graphenalgorithmen Performance von Algorithmen analysieren (Landau-Notation) Arrays, verkettete Listen und Hashtabellen Rekursion und Stacks Quicksort und das Teile-und-herrsche-Verfahren Dijkstra-Algorithmus für die Ermittlung des kürzesten Pfads Approximationsalgorithmen und NP-vollständige Probleme Greedy-Algorithmen Dynamische Programmierung Klassifikation und Regression mit dem k-Nächste-Nachbarn-Algorithmus

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  • Price: 25.99 EUR excl. shipping
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  Grokking Algorithms (eBook, ePUB)

  "This book does the impossible: it makes math fun and easy!" - Sander Rossel, COAS Software Systems Grokking Algorithms is a fully illustrated, friendly guide that teaches you how to apply common algorithms to the practical problems you face every day as a programmer. You'll start with sorting and searching and, as you build up your skills in thinking algorithmically, you'll tackle more complex concerns such as data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. Learning about algorithms doesn't have to be boring! Get a sneak peek at the fun, illustrated, and friendly examples you'll find in Grokking Algorithms on Manning Publications' YouTube channel. Continue your journey into the world of algorithms with Algorithms in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/algorithms-?in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An algorithm is nothing more than a step-by-step procedure for solving a problem. The algorithms you'll use most often as a programmer have already been discovered, tested, and proven. If you want to understand them but refuse to slog through dense multipage proofs, this is the book for you. This fully illustrated and engaging guide makes it easy to learn how to use the most important algorithms effectively in your own programs. About the Book Grokking Algorithms is a friendly take on this core computer science topic. In it, you'll learn how to apply common algorithms to the practical programming problems you face every day. You'll start with tasks like sorting and searching. As you build up your skills, you'll tackle more complex problems like data compression and artificial intelligence. Each carefully presented example includes helpful diagrams and fully annotated code samples in Python. By the end of this book, you will have mastered widely applicable algorithms as well as how and when to use them. What's Inside Covers search, sort, and graph algorithms Over 400 pictures with detailed walkthroughs Performance trade-offs between algorithms Python-based code samples About the Reader This easy-to-read, picture-heavy introduction is suitable for self-taught programmers, engineers, or anyone who wants to brush up on algorithms. About the Author Aditya Bhargava is a Software Engineer with a dual background in Computer Science and Fine Arts. He blogs on programming at adit.io. Table of Contents Introduction to algorithms Selection sort Recursion Quicksort Hash tables Breadth-first search Dijkstra's algorithm Greedy algorithms Dynamic programming K-nearest neighbors

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  • Price: 33.72 EUR excl. shipping
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  Algorithms

  Essential Information that Every Serious Programming Student Needs to Know about Algorithms and Data Structures A Classic Reference The latest version of Sedgewick's best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Broad Coverage Full treatment of data structures and algorithms for sorting, searching, graph processing, string processing, and geometric applications, including fifty algorithms every programmer should know. See www.cs.princeton.edu/algs4/top50 . Completely Revised Code New Java implementations written in an accessible, modular programming style, where all of the code is exposed to the reader and ready to use. New versions of quicksort, LZW compression, red-black tree search, RE pattern matching, and many other algorithms. Engages with Applications Algorithms are studied in the context of important scientific, engineering, and commercial applications. Clients and algorithms are expressed in real code, not the pseudo-code found in many other books. Intellectually Stimulating Engages reader interest with clear, concise text, detailed examples with visuals, carefully crafted code, historical and scientific context, and exercises at all levels. A Scientific Approach Develops precise statements about performance, supported by appropriate mathematical models and empirical studies validating those models. Integrated with the Web Visit www.cs.princeton.edu/algs4 for a freely accessible, comprehensive Web site, including text digests, program code, test data, programming projects, exercises, lecture slides, and other resources. Product Description This fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms is the leading textbook on algorithms today and is widely used in colleges and universities worldwide. This book 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. Features + Benefits The #1 practical resource for everyone seeking to run programs faster or solve larger problems Surveys today's most useful algorithms, with copious illustrations and examples Contains many new examples, ranging from physics, biology, and engineering to data compression and web search Contains real (not pseudocode) implementations, with detailed performance insights A companion web site, algs4.cs.princeton.edu , with many more resources for instructors, including text digests, program code, test data, programming projects, exercises, lecture slides, and other resources Preface viii Chapter 1: Fundamentals 3 1.1 Basic Programming Model 8 1.2 Data Abstraction 64 1.3 Bags, Queues, and Stacks 120 1.4 Analysis of Algorithms 172 1.5 Case Study: Union-Find 216 Chapter 2: Sorting 243 2.1 Elementary Sorts 244 2.2 Mergesort 270 2.3 Quicksort 288 2.4 Priority Queues 308 2.5 Applications 336 Chapter 3: Searching 361 3.1 Symbol Tables 362 3.2 Binary Search Trees 396 3.3 Balanced Search Trees 424 3.4 Hash Tables 458 3.5 Applications 486 Chapter 4: Graphs 515 4.1 Undirected Graphs 518 4.2 Directed Graphs 566 4.3 Minimum Spanning Trees 604 4.4 Shortest Paths 638 Chapter 5: Strings 695 5.1 String Sorts 702 5.2 Tries 730 5.3 Substring Search 758 5.4 Regular Expressions 788 5.5 Data Compression 810 Chapter 6: Context 853 Index 933 List of Algorithms 954 List of Clients 955 This fourth edition of Robert Sedgewick and Kevin Wayne's Algorithms is one of the most popular textbooks on algorithms today and is widely used in colleges and universities worldwide. The algorithms in this book - including 50 algorithms every programmer should know - 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 and for students who use computation in the liberal arts. 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 companion web site, algs4.cs.princeton.edu, contains:An online synopsisFull Java implementationsTest dataExercises and answersDynamic visualizationsLecture slidesProgramming assignments with checklistsLinks to related materialThe MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course, offered each fall and spring, 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.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 state-of-the-art textbook, online content, and MOOC, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

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  • Price: 52.99 EUR excl. shipping
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  Algorithm Design: Pearson New International Edition

  August 6, 2009 Author, Jon Kleinberg, was recently cited in the New York Times for his statistical analysis research in the Internet age. 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. Features + Benefits Focus on problem analysis and design techniques. Discussion is grounded in concrete problems and examples rather than abstract presentation of principles, with representative problems woven throughout the text. Over 200 well crafted problems from companies such as Yahoo!® and Oracle®. Each problem has been class tested for usefulness and accuracy in the authors' own undergraduate algorithms courses. Broad coverage of algorithms for dealing with NP-hard problems and the application of randomization, increasingly important topics in algorithms. Algorithm DesignJon Kleinberg and Eva TardosTable of Contents 1 Introduction: Some Representative Problems 1.1 A First Problem: Stable Matching 1.2 Five Representative Problems Solved ExercisesExcercisesNotes and Further Reading2 Basics of Algorithms Analysis 2.1 Computational Tractability 2.2 Asymptotic Order of Growth Notation 2.3 Implementing the Stable Matching Algorithm using Lists and Arrays 2.4 A Survey of Common Running Times 2.5 A More Complex Data Structure: Priority Queues Solved Exercises Exercises Notes and Further Reading3 Graphs 3.1 Basic Definitions and Applications 3.2 Graph Connectivity and Graph Traversal 3.3 Implementing Graph Traversal using Queues and Stacks 3.4 Testing Bipartiteness: An Application of Breadth-First Search 3.5 Connectivity in Directed Graphs 3.6 Directed Acyclic Graphs and Topological Ordering Solved Exercises Exercises Notes and Further Reading 4 Divide and Conquer 4.1 A First Recurrence: The Mergesort Algorithm 4.2 Further Recurrence Relations 4.3 Counting Inversions 4.4 Finding the Closest Pair of Points 4.5 Integer Multiplication 4.6 Convolutions and The Fast Fourier Transform Solved Exercises Exercises Notes and Further Reading5 Greedy Algorithms 5.1 Interval Scheduling: The Greedy Algorithm Stays Ahead 5.2 Scheduling to Minimize Lateness: An Exchange Argument 5.3 Optimal Caching: A More Complex Exchange Argument 5.4 Shortest Paths in a Graph 5.5 The Minimum Spanning Tree Problem 5.6 Implementing Kruskal's Algorithm: The Union-Find Data Structure 5.7 Clustering 5.8 Huffman Codes and the Problem of Data Compression*5.9 Minimum-Cost Arborescences: A Multi-Phase Greedy Algorithm Solved Exercises Excercises Notes and Further Reading 6 Dynamic Programming 6.1 Weighted Interval Scheduling: A Recursive Procedure 6.2 Weighted Interval Scheduling: Iterating over Sub-Problems 6.3 Segmented Least Squares: Multi-way Choices 6.4 Subset Sums and Knapsacks: Adding a Variable 6.5 RNA Secondary Structure: Dynamic Programming Over Intervals 6.6 Sequence Alignment 6.7 Sequence Alignment in Linear Space 6.8 Shortest Paths in a Graph 6.9 Shortest Paths and Distance Vector Protocols *6.10 Negative Cycles in a Graph Solved ExercisesExercisesNotes and Further Reading7 Network Flow 7.1 The Maximum Flow Problem and the Ford-Fulkerson Algorithm 7.2 Maximum Flows and Minimum Cuts in a Network 7.3 Choosing Good Augmenting Paths *7.4 The Preflow-Push Maximum Flow Algorithm 7.5 A First Application: The Bipartite Matching Problem 7.6 Disjoint Paths in Directed and Undirected Graphs 7.7 Extensions to the Maximum Flow Problem 7.8 Survey Design 7.9 Airline Scheduling 7.10 Image Segmentation 7.11 Project Selection 7.12 Baseball Elimination *7.13 A Further Direction: Adding Costs to the Matching Problem Solved ExercisesExercisesNotes and Further Reading 8 NP and Computational Intractability 8.1 Polynomial-Time Reductions 8.2 Reductions via "Gadgets": The Satisfiability Problem 8.3 Efficient Certification and the Definition of NP 8.4 NP-Complete Problems 8.5 Sequencing Problems 8.6 Partitioning Problems 8.7 Graph Coloring8.8 Numerical Problems 8.9 Co-NP and the Asymmetry of NP8.10 A Partial Taxonomy of Hard Problems Solved Exercises Exercises Notes and Further Reading9 PSPACE: A Class of Problems Beyond NP9.1 PSPACE 9.2 Some Hard Problems in PSPACE 9.3 Solving Quantified Problems and Games in Polynomial Space9.4 Solving the Planning Problem in Polynomial Space9.5 Proving Problems PSPACE-Complete Solved ExercisesExercisesNotes and Further Reading 10 Extending the Limits of Tractability 10.1 Finding Small Vertex Covers 10.2 Solving NP-Hard Problem on Trees 10.3 Coloring a Set of Circular Arcs *10.4 Tree Decompositions of Graphs *10.5 Constructing a Tree Decomposition Solved Exercises Exercises Notes and Further Reading11 Approximation Algorithms 11.1 Greedy Algorithms and Bounds on the Optimum: A Load Balancing Problem 11.2 The Center Selection Problem 11.3 Set Cover: A General Greedy Heuristic 11.4 The Pricing Method: Vertex Cover 11.5 Maximization via the Pricing method: The Disjoint Paths Problem 11.6 Linear Programming and Rounding: An Application to Vertex Cover *11.7 Load Balancing Revisited: A More Advanced LP Application 11.8 Arbitrarily Good Approximations: the Knapsack Problem Solved ExercisesExercisesNotes and Further Reading 12 Randomized Algorithms 12.1 A First Application: Contention Resolution 12.2 Finding the Global Minimum Cut 12.3 Random Variables and their Expectations 12.4 A Randomized Approximation Algorithm for MAX 3-SAT 12.5 Randomized Divide-and-Conquer: Median-Finding and Quicksort 12.6 Hashing: A Randomized Implementation of Dictionaries 12.7 Finding the Closest Pair of Points: A Randomized Approach 12.8 Randomized Caching 12.9 Chernoff Bounds 12.10 Load Balancing *12.11 Packet Routing 12.12 Background: Some Basic Probability DefinitionsSolved ExercisesExercisesNotes and Further Reading 13 Local Search 13.1 The Landscape of an Optimization Problem 13.2 The Metropolis Algorithm and Simulated Annealing 13.3 An Application of Local Search to Hopfield Neural Networks 13.4 Maximum Cut Approximation via Local Search 13.5 Choosing a Neighbor Relation *13.6 Classification via Local Search 13.7 Best-Response Dynamics and Nash EquilibriaSolved ExercisesExercisesNotes aAugust 6, 2009 Author, Jon Kleinberg, was recently cited in the for his statistical analysis research in the Internet age. 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.

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  • Price: 94.99 EUR excl. shipping

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