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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Editorial Reviews
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. 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
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
Book Description
Rather than provide lots of well-polished code and proofs of correctness, this book presents insights, notations, and analogies to help the novice describe and think about algorithms like an expert. It stresses paradigms such as loop invariants and recursion to unify a huge range of algorithms into a few meta-algorithms.
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Most Helpful Customer Reviews
6 of 6 people found the following review helpful:
5.0 out of 5 stars
fantastic,
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Amazon Verified Purchase(What's this?)
This review is from: How to Think About Algorithms (Paperback)
Jeff's book is a life changer. Instead of focusing purely on studying well-know algorithms, he presents the problems, goes over a few ways to solve it (where intuition contradicts reality) and then explain the right solution. He dives into analysis of algorithms and proofs in a very clear and easy way. His students are probably very fortunate!
The way he explains how to approach a problem by defining the right pre/post conditions, loop invariants and where to focus to get it started changed the way I approach CS in general. Thanks for this great work!
1 of 1 people found the following review helpful:
3.0 out of 5 stars
Jumps about a lot in an attempt to divide the subject into iterative and recursive algorithms,
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Amazon Verified Purchase(What's this?)
This review is from: How to Think About Algorithms (Paperback)
This is a good book if you have already gone through another algorithm and data structure book first. The first chapter starts off by talking about iterative algorithms and how you would go about designing one (step by step), then in an attempt to give examples of iterative algorithms, chapter two covers everything from "plane cutting" to "finite automata" to "graph" problems! This is extremely confusing and unnerving.
Other chapters follow the same pattern. The chapter on recursion starts off really well, but in an attempt to give examples of recursion, it jumps all over the place. To be fair, topics like graphs, trees, greedy/dynamic algorithms are given their own dedicated chapters, but the book never goes into great depths to explain these concepts. I would say buy a proper algorithm book like "Introduction to Algorithms" or "Algorithm Design Manual" (my personal favorite) and buy this book to look at algorithms from a different perspective. To give credit where its due, the author does mention that this is not a reference book, but should be treated as a secondary resource to your primary algorithm book and to that end, this book will definitely make a good addition to your library.
9 of 13 people found the following review helpful:
5.0 out of 5 stars
Used this textbook in class,
By
This review is from: How to Think About Algorithms (Paperback)
Excellent textbook. Very informative. Plenty of material for all sorts of computer science related classes. The mathematical foundations were relevant and readily available. I appreciate the level of rigor he applies to various subjects in the book. It is clearly written, easy to understand, and filled with examples.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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leveled graph, exercise solutions, narrowing the search space, graph search algorithms, recurrence relations, initial code, abstract data types, bounded tail, induction hypothesis, augmentation graph, recursive backtracking algorithm, particular subinstance, current subinstance, best binary search tree, loop invariant states, sorted wrt, recursive image, generic search algorithm, greedy criterion, handled path, small local maximum, event scheduling problem, augmenting graph, augmentation capacity, shortest weighted path Key Phrases - Capitalized Phrases (CAPs): (learn more)
Optimization Problems, Measure of Success, Basic Steps, Maintain Loop Invariant, Printing Neatly, Main Steps, Dynamic Programming Algorithms, Special Cases, Greedy Algorithms, Some Simple Examples of Recursive Algorithms, Examples of Dynamic Programs, Making Change Example, More of the Output, Exiting Loop, More of the Input, Adding-Made-Easy Approximations, Examples Included, Formal Proof, Case Analysis, Table Indexed, Context-Free Grammars, Proof of Correctness, Examples Not Included, Fill the Table, Left Size Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
leveled graph, exercise solutions, narrowing the search space, graph search algorithms, recurrence relations, initial code, abstract data types, bounded tail, induction hypothesis, augmentation graph, recursive backtracking algorithm, particular subinstance, current subinstance, best binary search tree, loop invariant states, sorted wrt, recursive image, generic search algorithm, greedy criterion, handled path, small local maximum, event scheduling problem, augmenting graph, augmentation capacity, shortest weighted path Key Phrases - Capitalized Phrases (CAPs): (learn more)
Optimization Problems, Measure of Success, Basic Steps, Maintain Loop Invariant, Printing Neatly, Main Steps, Dynamic Programming Algorithms, Special Cases, Greedy Algorithms, Some Simple Examples of Recursive Algorithms, Examples of Dynamic Programs, Making Change Example, More of the Output, Exiting Loop, More of the Input, Adding-Made-Easy Approximations, Examples Included, Formal Proof, Case Analysis, Table Indexed, Context-Free Grammars, Proof of Correctness, Examples Not Included, Fill the Table, Left Size Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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