Название: How to Think about Algorithms, 2nd EditionАвтор: Jeff Edmonds
Издательство: Cambridge University Press
Год: 2024
Страниц: 618
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Understand algorithms and their design with this revised student-friendly textbook. Unlike other algorithms books, this one is approachable, the methods it explains are straightforward, and the insights it provides are numerous and valuable. Without grinding through lots of formal proof, students will benefit from step-by-step methods for developing algorithms, expert guidance on common pitfalls, and an appreciation of the bigger picture. Revised and updated, this second edition includes a new chapter on Machine Learning algorithms, and concise key concept summaries at the end of each part for quick reference. Also new to this edition are more than 150 new exercises: selected solutions are included to let students check their progress, while a full solutions manual is available online for instructors. No other text explains complex topics such as loop invariants as clearly, helping students to think abstractly and preparing them for creating their own innovative ways to solve problems.
This book is designed to be used in a twelve-week, third-year algorithms course. The goal is to teach students to think abstractly about algorithms and about the key algorithmic techniques used to develop them.
Meta-Algorithms: Students must learn so many algorithms that they are sometimes overwhelmed. In order to facilitate their understanding, most textbooks cover the standard themes of iterative algorithms, recursion, greedy algorithms, and dynamic programming. Generally, however, when it comes to presenting the algorithms themselves and their proofs of correctness, the concepts are hidden within optimized code and slick proofs. One goal of this book is to present a uniform and clean way of thinking about algorithms. We do this by focusing on the structure and proof of correctness of iterative and recursive meta-algorithms, and within these the greedy and dynamic programming meta-algorithms. By learning these and their proofs of correctness, most actual algorithms can be easily understood. The challenge is that thinking about meta-algorithms requires a great deal of abstract thinking.
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