Algorithms in a Nutshell: A Practical Guide

I don't know if there's a better reference available, but this one has a lot of problems. I've been working as a software engineer for 8 years now. I came into this book with a working knowledge of all the major data structures and most basic algorithms you might run into in a typical interview. I wasn't a CS major. I got this book to prepare for a round of technical interviews.The book says it has a goal of "introduc[ing] just enough mathematics." But it uses lots of mathematical notation that it never bothers to explain, and so much of it is really unnecessary. Consider this from page 16:"If Sn is the set of instances of si of size n, and t() is a function that measures the work done by an algorithm on each instance, then work done by an algorithm on Sn in the worst case is the maximum of t(si) over all si ∈ Sn. Denoting this worst-case performance on Sn by Twc(n), the rate of growth of Twc(n) defines the worst-case complexity of the algorithm."All of this is just to say, "The worst case complexity is the largest number of steps an algorithm might take to complete for an input of size n, in terms of n." There's really nothing gained in describing it in more abstract terms than that.Worse, it's inaccurate. It says you should always use omega notation (Ω(n)) when describing the best-case runtime of an algorithm, and Big-O notation when describing the worst-case. But worst-case and best-case have nothing to do with omega and Big-O, which could each be used to describe the upper bound or lower bound of the function you're describing. Upper and lower bound simply describe a mathematical function. Best-case and worst-case describe performance. If this all sounds Greek, suffice it to say that this is a blatant mistake.In the opening chapters it also refers frequently to algorithms that are toward the back of the book, assuming that you already understand them.If you're like me, you'll read each paragraph, exhaust your brain for thirty minutes trying to understand it, then turn to Google for a better explanation. Learning algorithms does not need to be this complicated. I don't know if a better or more straightforward book on algorithms exists, but I sure hope so.

Gives you the what, why and how information you need to know on the algorithms it covers. Excellent reference with just the right amount of information for both the newcomer and seasoned Engineer wanting a refresh. Both will find this book an exceptional reference!If you can help it, don't buy it from Amazon Prime (or, rather, from any vendor that says "fulfilled by Amazon"), as Amazon will just stuff the book in an unprotected envelope and let the mail service bend it up.

A welcome update. The original was a great addition to the generally execellent "Nutshell" series. It's not the "perfect" algorithms book -- multi-language examples (Python at least) and wider algorithm coverage are still needed -- but it's well written and concise. Heavy on practical application with reasonable discusdion of theory, resource utilization, and performance. A good discussion of benchmarking and testing make this a "go to" book for many enterprise developers needing quick on specific algorithms and implementation factors. Recommended.

Beautiful in explaining the art of algorithm and all their beauty.

Good, practical and fairly concise guide to the most common algorithms. Not too math-y. Great for getting back up to speed or starting out.

I bought this book because I was hoping for a cheaper, and more to the point, alternative to the big popular books on algorithms / data structures that are like 1000 pages and $80 and doubtless full of padding to increase the page count.It was a poor choice - even if it was for free, this book is not worth your time. I read this book at the same time as several other algorithm books and this stood out by far as the worst, even compared to free books like a competitive programming handbook that was popular on hackernews at the time.(1) The writer spends WAY too much time on examples, and on a couple particular example problems he's in love with. I would say maybe 20% of the book is the actual content you came here to learn (generalizable lessons about algorithms and data structure) and the other 80% are these examples that are obviously intended to train you to "think in algorithms" but are too poorly structured to do so.(2) The worst part is that the wheat is completely buried in the chaff. There's no consistent delineation of concepts and examples - to get to the general principles you will have to wade through the poorly written examples. Skimming is impossible with this book(3) When there are general concepts explained, they are explained in a "this is right because it is" way - there is no attempt to connect concepts, the ideas and takeaways that are thrown at you are seemingly random.(4) The writing is ultimately just bad and doesn't feel topical. The writer does not make an effort to explain themselves clearly and most of the time it feels like they're not trying to teach about algorithms as a broad subject, or prepare you for recognizing and solving these problems; instead they just have a few whiteboard problems they really like to talk about, and along the way they'll pepper in a couple general points about algorithms.

Simply one of the best books on algorithms you will ever find. Period.

Algorithms in a Nutshell is exactly that. A book that has just the right amount of theory and math to quickly help you find the solution to an every day problem.Years ago I attended WPI, and took an Analysis of Algorithms class from Stanley Selkow. Stanley taught an excellent course that went beyond the topics in this book. However, many of the concepts he taught found their way into this compact book.I have almost a dozen algorithms books including the classics by Cormer, Knuth and Sedgewick; but often need a quick answer. The books in the nutshell series have always been a great resource. This book fits that need, and has a space on the shelf in my office.

Nice book, not a complete theoretic treatment of algorithms, but good to have alongside 'grokking algorithms'.

O melhor livro para quem precisa de um referência clara e direta. Estou muito satisfeito.O texto é simples, sem rodeios.Recomendo usar com um livro texto mais teórico como o Sedgewick.

Ottimo il prezzo. L’ho usato per un regalo ed ho fatto un figurone.

Simple, fast, complete, detailed.