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The Art of Algorithm Design is a complementary perception of all books on algorithm design and is a roadmap for all levels of learners as well as professionals dealing with algorithmic problems. Further, the book provides a comprehensive introduction to algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. All algorithms are described and designed with a "pseudo-code" to be readable by anyone with little knowledge of programming. This book comprises of a comprehensive set of problems and their solutions against each algorithm to demonstrate its executional assessment and complexity, with an objective to: Understand the introductory concepts and design principles of algorithms and their complexities Demonstrate the programming implementations of all the algorithms using C-Language Be an excellent handbook on algorithms with self-explanatory chapters enriched with problems and solutions While other books may also cover some of the same topics, this book is designed to be both versatile and complete as it traverses through step-by-step concepts and methods for analyzing each algorithmic complexity with pseudo-code examples. Moreover, the book provides an enjoyable primer to the field of algorithms. This book is designed for undergraduates and postgraduates studying algorithm design.
Donald Knuth is Professor Emeritus of the Art of Computer Programming at Stanford University, and is well-known worldwide as the creator of the Tex typesetting language. Here he presents the third volume of his guide to computer programming.
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
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
These days, we take for granted that our computer screens—and even our phones—will show us images in vibrant full color. Digital color is a fundamental part of how we use our devices, but we never give a thought to how it is produced or how it came about. Chromatic Algorithms reveals the fascinating history behind digital color, tracing it from the work of a few brilliant computer scientists and experimentally minded artists in the late 1960s and early ‘70s through to its appearance in commercial software in the early 1990s. Mixing philosophy of technology, aesthetics, and media analysis, Carolyn Kane shows how revolutionary the earliest computer-generated colors were—built with the massive postwar number-crunching machines, these first examples of “computer art” were so fantastic that artists and computer scientists regarded them as psychedelic, even revolutionary, harbingers of a better future for humans and machines. But, Kane shows, the explosive growth of personal computing and its accompanying need for off-the-shelf software led to standardization and the gradual closing of the experimental field in which computer artists had thrived. Even so, the gap between the bright, bold presence of color onscreen and the increasing abstraction of its underlying code continues to lure artists and designers from a wide range of fields, and Kane draws on their work to pose fascinating questions about the relationships among art, code, science, and media in the twenty-first century.
Generative Art: Algorithms as Artistic Tool presents both simple programming concepts and generative art principles in the same book. Generative Art, a relatively new form of art, is the art of the algorithm where an artist must carefully design the nature of the work and then implement it as a computer program. This book presents a set of novel approaches to this subject. Existing books on this subject confront the topic through the lens of programming. This book does that, but also presents approaches to creating art using art and design best practices. Content is arranged according to the problem that is to be solved. Readers will have access to code used in the book through the book's web site and video tutorials are also available for each chapter.
The gap between theoretical ideas and messy reality, as seen in Neal Stephenson, Adam Smith, and Star Trek. We depend on—we believe in—algorithms to help us get a ride, choose which book to buy, execute a mathematical proof. It's as if we think of code as a magic spell, an incantation to reveal what we need to know and even what we want. Humans have always believed that certain invocations—the marriage vow, the shaman's curse—do not merely describe the world but make it. Computation casts a cultural shadow that is shaped by this long tradition of magical thinking. In this book, Ed Finn considers how the algorithm—in practical terms, “a method for solving a problem”—has its roots not only in mathematical logic but also in cybernetics, philosophy, and magical thinking. Finn argues that the algorithm deploys concepts from the idealized space of computation in a messy reality, with unpredictable and sometimes fascinating results. Drawing on sources that range from Neal Stephenson's Snow Crash to Diderot's Encyclopédie, from Adam Smith to the Star Trek computer, Finn explores the gap between theoretical ideas and pragmatic instructions. He examines the development of intelligent assistants like Siri, the rise of algorithmic aesthetics at Netflix, Ian Bogost's satiric Facebook game Cow Clicker, and the revolutionary economics of Bitcoin. He describes Google's goal of anticipating our questions, Uber's cartoon maps and black box accounting, and what Facebook tells us about programmable value, among other things. If we want to understand the gap between abstraction and messy reality, Finn argues, we need to build a model of “algorithmic reading” and scholarship that attends to process, spearheading a new experimental humanities.
A laboratory study that investigates how algorithms come into existence. Algorithms--often associated with the terms big data, machine learning, or artificial intelligence--underlie the technologies we use every day, and disputes over the consequences, actual or potential, of new algorithms arise regularly. In this book, Florian Jaton offers a new way to study computerized methods, providing an account of where algorithms come from and how they are constituted, investigating the practical activities by which algorithms are progressively assembled rather than what they may suggest or require once they are assembled.
This coherent anthology presents the state of the art in the booming area of online algorithms and competitive analysis of such algorithms. The 17 papers are carefully revised and thoroughly improved versions of presentations given first during a Dagstuhl seminar in 1996. An overview by the volume editors introduces the area to the reader. The technical chapters are devoted to foundational and methodological issues for the design and analysis of various classes of online algorithms as well as to the detailed evaluation of algorithms for various activities in online processing, ranging from load balancing and scheduling to networking and financial problems. An outlook by the volume editors and a bibliography listing more than 750 references complete the work. The book is ideally suited for advanced courses and self-study in online algorithms. It is indispensable reading for researchers and professionals active in the area.