How to Think About Algorithms

# Publisher: Cambridge University Press; illustrated edition edition (May 19, 2008)
# Language: English
# ISBN-10: 0521614104
# ISBN-13: 978-0521614108


Review
"Edmonds intends this text for use in advanced undergraduate courses in algorithms. The author encourages abstract thinking through exercises informed by real-world scenarios to help students access and master algorithm concepts, including proofs of correctness, with greater ease. Twenty-one chapters under the main themes of iterative algorithms and loop invariants (section one), recursion (section two), and optimization problems (section three) cover topics that include: measures of progress and loop invariants, abstract data types, binary search, iterative sorting algorithms, abstractions and theory, recursion on trees, recursive images, graph search algorithms, network flows and linear programming, greedy algorithms, and dynamic programming algorithms. Seven additional chapters in the appendix consider specific algorithms such as those measuring time complexity and asymptomatic growth."
Book News, Inc.

"Reading this is like sitting at the feet of the master: it leads an apprentice from knowing how to program to understanding deep principles of algorithms. Presentation [is] informed and friendly."
Harold Thimbleby, Times Higher Education

"I believe this book could be considered a must-read for every teacher of algorithms. The book would also be invaluable to researchers who wish to gain a deeper understanding on how algorithms work, and to undergraduates who with to develop their algorithmic thought... it has the potential to be considered a classic."
Kyriakos N. Sgarbas for SIGACT News

Product Description
There are many algorithm texts that provide lots of well-polished code and proofs of correctness. This book is not one of them. Instead, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. By looking at both the big picture and easy step-by-step methods for developing algorithms, the author helps students avoid the common pitfalls. He stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms. Part of the goal is to teach the students to think abstractly. Without getting bogged with formal proofs, the book fosters a deeper understanding of how and why each algorithm works. These insights are presented in a slow and clear manner accessible to second- or third-year students of computer science, preparing them to find their own innovative ways to solve problems.

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