Overview

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.

Full Product Details

9780511808241

Table of Contents

Reviews

'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.' Harold Thimbleby, The Times Higher Education Supplement '... a great book to learn how to design and create new algorithms ... a good book that the reader will appreciate in the first and subsequent reads, and it will make better developers and programmers.' Journal of Functional Programming 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. Even if he reads things he already knows, he will be able to view them from different angles and in the process get some very useful ideas on how to explain algorithms in class. The book would also be invaluable to researchers who wish to gain a deeper understanding on how algorithms work, and to undergraduate students who wish to develop their algorithmic thought... it has the potential to be considered a classic. Kyriakos N. Sgarbas for SIGACT News All in all this is a great book to learn how to design and create new algorithms. The author teaches you how to think about algorithms step by step, building the necessary knowledge and illustrating the process with common algorithms. This is a good book that the reader will appreciate in the first and subsequent reads, it will make better developers and programmers. Journal of Fuctional Programming

'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.' Harold Thimbleby, Times Higher Education Supplement

Author Information

Jeff Edmonds received his Ph.D. in 1992 at University of Toronto in theoretical computer science. His thesis proved that certain computation problems require a given amount of time and space. He did his postdoctorate work at the ICSI in Berkeley on secure multi-media data transmission and in 1995 became an Associate Professor in the Department of Computer Science at York University, Canada. He has taught their algorithms course thirteen times to date. He has worked extensively at IIT Mumbai, India, and University of California San Diego. He is well published in the top theoretical computer science journals in topics including complexity theory, scheduling, proof systems, probability theory, combinatorics, and, of course, algorithms.

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