Algorithms in a Nutshell

This is my first Safari book that has the free online edition for 45 days. I have a pretty good collection of books on algorithms. Many O'Reilly books are among the collection. Yet I am most impressed with" Algorithms in a Nutshell "a desktop quick reference. I won't go into a lot of detail as anybody who purchases this book was already know what they're looking for. I am impressed however that for such a small book this goes into a lot of deep concepts and gives you practical solutions. The best way to see if this book is useful compared to others is to look at sorting algorithms that you know by heart such as median sort and quick sort. If this book tells you what you already know or even does a better job of explaining what you know this is the book for you. This is definitely the book for me. Even with these examples, that take very little adjustment to put into the real world, you may want to supplement this book with "Sorting and Searching (The Art of Computer Programming, Volume 3)" by Donald E Knuth, Richard S Varga, and Michael A Harrison. Even if you are not a programmer this book can help you to understand what programmers and or coders are accomplishing with their programs. For people taking any math discipline school this makes a fantastic supplement to understanding math from a different angle. Sorting and Searching (The Art of Computer Programming, Volume 3)

The book does not aim to be an introduction to, nor the definitive encyclopedia on, the subject of algorithms. It is intended, as advertised on the cover, as "A Desktop Quick Reference". In the Preface it states that the goal is to provide a useful tool for working programmers to find good solutions to the problems they solve. As a self-taught programmer I am finding this book interesting to better understand the various ways that the same problem can be solved, and the pros and cons of each. While the book is mainly intended to help programmers review and select appropriate algorithms for a problem at hand, I am using it as a study guide and have enjoyed it as such thus far. While it doesn't exactly make for light reading, each algorithm is considered individually, which makes for a decent size chunk of information which can be considered independently. Multiple languages (C, C++, Java, Ruby) are used throughout the book in demonstrating the algorithms, solutions are not provided in each language for each algorithm. However, if you are using this as a study guide, this can provide a good exercise to translate the solution into the language of your liking.

The preface states that the main purpose of this book is to provide usable implementations of some of the most useful algorithms. To illustrate the superiority of this approach as compared to similar books, the authors present the pseudo-code of the Ford-Fulkerson algorithm as given in Wikipedia and Cormen's textbook. Then they write that such listings are basically useless for a software engineer, who cannot produce working programs from them. He needs a book with real, working, verified implementations rather than pseudo-code and proofs of correctness. This is that book. After reading this part of the preface in a local bookstore, I read the table of contents and picked at random the section on Linear Programming for a closer look at the contents. This 2-page section provides no code whatsoever. Not even pseudo-code. It uses a commercial mathematical software to solve a problem and advises the readers to do the same. Don't implement anything here because it's too complicated, just use a commercial package. I found this message a bit amusing, and somehow opposed to the stated purpose of the book. Maybe I was unlucky, and other sections are different. You'll have to check that for yourselves. The book seems otherwise compact in its field, reasonably priced, rich in tables, examples, illustrations and other attention-grabbers. I'd say buy it, but take its promises with a grain of salt. Incidentally, the Wikipedia article on Ford-Fulkerson provides a Python implementation in addition to the pseudo-code. It might have been added after this book was published. Nevertheless, this shows how quickly some parts of a 2008 book can become outdated.

In recent years I have found most other non-textbooks on algorithms to be uninteresting mainly for two reasons. First, there are books that are re-released periodically in a new "programming language du jour" without adding real value (some moving from Pascal to C/C++ to Python, Java or C#). The second group are books that are rather unimaginative collections of elementary information, often augmenting their bulk with lengthy pages of source code (touted as "ready-to-use", but never actually usable). I almost didn't pay any attention to this book because its title struck me as rather mundane and pedestrian .... what an uncommonly false first-impression that turned out to be! The is a well-written book and a great practical and usable one for working software developers at any skill or experience level. It starts with a condensed set of introductory material. It then covers the gamut of common and some not-so-common algorithms grouped by problems/tasks that do come up in a variety of real-world applications. I particularly appreciate the concise and thoughtful - and concise - descriptions -- chock-full of notes on applicability and usability -- with absolutely no fluff! If nothing else, this book can be a good quick index or a chit sheet before culling through more standard textbooks (many of which, in fact, mentioned as further references in each section). I believe the authors have identified a valid "hole" in the technical bookshelves - and plugged it quite well! Regarding the book's title, ... now I feel it's just appropriately simple, honest, and down-to-earth.

This new book on algorithms from O'Reilly is a breath of fresh air. Most books on the subject fall into two categories: very dense tomes full of math and heavy on sometime unintelligible pseudocode, or books that basically just give you recipes without much understanding. The second category is the "give a man a fish" type, the first type is the "teach a man to fish, but use ALGOL to do it". Even the author, in his preface, recognizes that this is not the one book on algorithms you'd need if you were on a desert island. On a desert island you have plenty of time and you can carefully digest Cormen's Introduction to Algorithms. However, you're not on a desert island, are you? Thus this book is the link between Cormen's careful theoretical approach that takes time, and books that amount to code dumps. The first six chapters amount to supplements on the basics of Theory of Algorithm courses: mathematics foundations, sorting, searching, and graphing algorithms. The mathematics here is somewhat lacking, but then the author is assuming you have other books on the subject - this is a book for ramping up quickly. The rest of the book is rather specialized, considering specific families of algorithms that are topical in these times such as path finding in AI, computational geometry, and network flow. They fill in the blanks missing in the standard textbooks. Plus there is plenty of code - real code, not pseudocode - that you can put to work quickly. The product description lacks the table of contents, so I list that next: Part I: I Chapter 1. Algorithms Matter Section 1.1. Understand the Problem Section 1.2. Experiment if Necessary Section 1.3. Side Story Section 1.4. The Moral of the Story Section 1.5. References Chapter 2. The Mathematics of Algorithms Section 2.1. Size of a Problem Instance Section 2.2. Rate of Growth of Functions Section 2.3. Analysis in the Best, Average, and Worst Cases Section 2.4. Performance Families Section 2.5. Mix of Operations Section 2.6. Benchmark Operations Section 2.7. One Final Point Section 2.8. References Chapter 3. Patterns and Domains Section 3.1. Patterns: A Communication Language Section 3.2. Algorithm Pattern Format Section 3.3. Pseudocode Pattern Format Section 3.4. Design Format Section 3.5. Empirical Evaluation Format Section 3.6. Domains and Algorithms Section 3.7. Floating-Point Computations Section 3.8. Manual Memory Allocation Section 3.9. Choosing a Programming Language Section 3.10. References Part II: II Chapter 4. Sorting Algorithms Section 4.1. Overview Section 4.2. Insertion Sort Section 4.3. Median Sort Section 4.4. Quicksort Section 4.5. Selection Sort Section 4.6. Heap Sort Section 4.7. Counting Sort Section 4.8. Bucket Sort Section 4.9. Criteria for Choosing a Sorting Algorithm Section 4.10. References Chapter 5. Searching Section 5.1. Overview Section 5.2. Sequential Search Section 5.3. Binary Search Section 5.4. Hash-based Search Section 5.5. Binar