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Introduction To The Analysis Of Algorithms (Fourth Edition)
An Introduction to the Analysis of Algorithms is a comprehensive textbook that presents the fundamental methods for designing and analyzing computational algorithms through rigorous mathematical frameworks and practical implementation guidance. The book systematically explores major algorithmic paradigms including greedy algorithms, divide and conquer, dynamic programming, online algorithms, randomized algorithms, and parallel algorithms in linear algebra, providing detailed analysis of correctness and performance for each approach.The text emphasizes algorithm design techniques and formal analysis using pre/post-conditions and loop invariants, while covering essential computational foundations including automata theory, regular expressions, and complexity analysis. A new chapter on machine learning introduces students to this rapidly growing field, covering both supervised learning methods like regression and classification, and unsupervised techniques such as clustering, providing a bridge between traditional algorithmic thinking and modern data-driven approaches. The book also addresses practical considerations such as algorithm implementation, optimization techniques, and real-world applications across various domains.Intended for undergraduate and graduate students in computer science and mathematics, the self-contained presentation includes all necessary background material, worked examples, and extensive problem sets, making it suitable as both a classroom textbook and a comprehensive reference for anyone seeking to master algorithmic problem-solving and analysis.
R 5,572.18
Introduction To The Analysis Of Algorithms, An (3rd Edition)
A successor to the first and second editions, this updated and revised book is a leading companion guide for students and engineers alike, specifically software engineers who design algorithms. While succinct, this edition is mathematically rigorous, covering the foundations for both computer scientists and mathematicians with interest in the algorithmic foundations of Computer Science.Besides expositions on traditional algorithms such as Greedy, Dynamic Programming and Divide & Conquer, the book explores two classes of algorithms that are often overlooked in introductory textbooks: Randomised and Online algorithms — with emphasis placed on the algorithm itself. The book also covers algorithms in Linear Algebra, and the foundations of Computation.The coverage of Randomized and Online algorithms is timely: the former have become ubiquitous due to the emergence of cryptography, while the latter are essential in numerous fields as diverse as operating systems and stock market predictions.While being relatively short to ensure the essentiality of content, a strong focus has been placed on self-containment, introducing the idea of pre/post-conditions and loop invariants to readers of all backgrounds, as well as all the necessary mathematical foundations. The programming exercises in Python will be available on the web (see www.msoltys.com/book for the companion web site).
R 3,513.76
Introduction To The Analysis Of Algorithms, An (2nd Edition)
A successor to the first edition, this updated and revised book is a great companion guide for students and engineers alike, specifically software engineers who design reliable code. While succinct, this edition is mathematically rigorous, covering the foundations of both computer scientists and mathematicians with interest in algorithms.Besides covering the traditional algorithms of Computer Science such as Greedy, Dynamic Programming and Divide & Conquer, this edition goes further by exploring two classes of algorithms that are often overlooked: Randomised and Online algorithms — with emphasis placed on the algorithm itself. The coverage of both fields are timely as the ubiquity of Randomised algorithms are expressed through the emergence of cryptography while Online algorithms are essential in numerous fields as diverse as operating systems and stock market predictions.While being relatively short to ensure the essentiality of content, a strong focus has been placed on self-containment, introducing the idea of pre/post-conditions and loop invariants to readers of all backgrounds. Containing programming exercises in Python, solutions will also be placed on the book's website.
R 2,121.17
Introduction To The Analysis Of Algorithms, An
This textbook covers the mathematical foundations of the analysis of algorithms. The gist of the book is how to argue, without the burden of excessive formalism, that a given algorithm does what it is supposed to do. The two key ideas of the proof of correctness, induction and invariance, are employed in the framework of pre/post-conditions and loop invariants.The algorithms considered are the basic and traditional algorithms of computer science, such as Greedy, Dynamic and Divide & Conquer. In addition, two classes of algorithms that rarely make it into introductory textbooks are discussed. Randomized algorithms, which are now ubiquitous because of their applications to cryptography; and Online algorithms, which are essential in fields as diverse as operating systems (caching, in particular) and stock-market predictions.This self-contained book is intended for undergraduate students in computer science and mathematics.
R 1,461.93
