Algorithms Illuminated Part 4

Algorithms Illuminated  Part 4
Author: Tim Roughgarden
Publsiher: Unknown
Total Pages: 272
Release: 2020-07-20
Genre: Computers
ISBN: 0999282964

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Algorithms Illuminated

Algorithms Illuminated
Author: Tim Roughgarden
Publsiher: Unknown
Total Pages: 209
Release: 2018
Genre: Electronic Book
ISBN: 099928293X

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Algorithms Illuminated Part 3

Algorithms Illuminated  Part 3
Author: Tim Roughgarden
Publsiher: Unknown
Total Pages: 230
Release: 2019-05-09
Genre: Computers
ISBN: 0999282948

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Accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Part 3 covers greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, shortest paths, optimal search trees).

Beyond the Worst Case Analysis of Algorithms

Beyond the Worst Case Analysis of Algorithms
Author: Tim Roughgarden
Publsiher: Cambridge University Press
Total Pages: 704
Release: 2021-01-14
Genre: Computers
ISBN: 9781108494311

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Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Introduction to Stochastic Search and Optimization

Introduction to Stochastic Search and Optimization
Author: James C. Spall
Publsiher: John Wiley & Sons
Total Pages: 618
Release: 2005-03-11
Genre: Mathematics
ISBN: 9780471441908

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A unique interdisciplinary foundation for real-world problemsolving Stochastic search and optimization techniques are used in a vastnumber of areas, including aerospace, medicine, transportation, andfinance, to name but a few. Whether the goal is refining the designof a missile or aircraft, determining the effectiveness of a newdrug, developing the most efficient timing strategies for trafficsignals, or making investment decisions in order to increaseprofits, stochastic algorithms can help researchers andpractitioners devise optimal solutions to countless real-worldproblems. Introduction to Stochastic Search and Optimization: Estimation,Simulation, and Control is a graduate-level introduction to theprinciples, algorithms, and practical aspects of stochasticoptimization, including applications drawn from engineering,statistics, and computer science. The treatment is both rigorousand broadly accessible, distinguishing this text from much of thecurrent literature and providing students, researchers, andpractitioners with a strong foundation for the often-daunting taskof solving real-world problems. The text covers a broad range of today’s most widely usedstochastic algorithms, including: Random search Recursive linear estimation Stochastic approximation Simulated annealing Genetic and evolutionary methods Machine (reinforcement) learning Model selection Simulation-based optimization Markov chain Monte Carlo Optimal experimental design The book includes over 130 examples, Web links to software anddata sets, more than 250 exercises for the reader, and an extensivelist of references. These features help make the text an invaluableresource for those interested in the theory or practice ofstochastic search and optimization.

How to Think About Algorithms

How to Think About Algorithms
Author: Jeff Edmonds
Publsiher: Cambridge University Press
Total Pages: 135
Release: 2008-05-19
Genre: Computers
ISBN: 9781139471756

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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.

Twenty Lectures on Algorithmic Game Theory

Twenty Lectures on Algorithmic Game Theory
Author: Tim Roughgarden
Publsiher: Cambridge University Press
Total Pages: 135
Release: 2016-09-01
Genre: Computers
ISBN: 9781316782095

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Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.

An Introduction to the Analysis of Algorithms

An Introduction to the Analysis of Algorithms
Author: Robert Sedgewick,Philippe Flajolet
Publsiher: Addison-Wesley
Total Pages: 604
Release: 2013-01-18
Genre: Computers
ISBN: 9780133373486

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Despite growing interest, basic information on methods and models for mathematically analyzing algorithms has rarely been directly accessible to practitioners, researchers, or students. An Introduction to the Analysis of Algorithms, Second Edition, organizes and presents that knowledge, fully introducing primary techniques and results in the field. Robert Sedgewick and the late Philippe Flajolet have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures. They emphasize the mathematics needed to support scientific studies that can serve as the basis for predicting algorithm performance and for comparing different algorithms on the basis of performance. Techniques covered in the first half of the book include recurrences, generating functions, asymptotics, and analytic combinatorics. Structures studied in the second half of the book include permutations, trees, strings, tries, and mappings. Numerous examples are included throughout to illustrate applications to the analysis of algorithms that are playing a critical role in the evolution of our modern computational infrastructure. Improvements and additions in this new edition include Upgraded figures and code An all-new chapter introducing analytic combinatorics Simplified derivations via analytic combinatorics throughout The book’s thorough, self-contained coverage will help readers appreciate the field’s challenges, prepare them for advanced results—covered in their monograph Analytic Combinatorics and in Donald Knuth’s The Art of Computer Programming books—and provide the background they need to keep abreast of new research. "[Sedgewick and Flajolet] are not only worldwide leaders of the field, they also are masters of exposition. I am sure that every serious computer scientist will find this book rewarding in many ways." —From the Foreword by Donald E. Knuth