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# The Algorithm Design Manual

by

Book by Skiena, Steven S

## Get A Copy

Hardcover, 730 pages

Published
April 27th 2011
by Springer
(first published November 14th 1997)

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## Community Reviews

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May 30, 2011
Ivan
rated it
it was amazing

Recommends it for:
anyone interested in algorithms and data structures

Shelves:
computer-science

When you want to read a good introductory book about algorithms and data structures the choice comes down to two books: Introduction to Algorithms, Second Edition and this one. I especially liked The Algorithm Design Manual because of the author's writing style, the "war stories" (that are some clever and practical applications of the data structures and algorithms the author tries to teach you) and the second half part of the book which is a sort of encyclopedia of problems.

I used the "introduc ...more

I used the "introduc ...more

I found the "Hitchhiker's Guide to Algorithms" in the back to be extremely useful if you really find yourself tackling an algorithmic problem in practice.

The main part (maybe skipping/skimming down a few chapters) is a very good preparation for algorithm-heavy job interviews (e.g. Google, Facebook etc ...).

Very ...more

Sep 01, 2013
Josh Davis
rated it
it was ok
·
review of another edition

Shelves:
owned-read-books,
textbooks-fall-2013

I can't think of an occasion when I'd recommend this over Intro to Algorithms (CLRS). It does a fraction of what CLRS does and worse in most cases. And in the rest of the cases, it does them

Speaking of proofs, this is what I hated about Skiena. It has barely any proofs in comparison to CLRS. A l ...more

*exactly*the same. There were some instances (graph algorithms) where the code in Skiena was taken straight out of CLRS. Not only did CLRS explain the algorithm better but it had the proofs to back it up.Speaking of proofs, this is what I hated about Skiena. It has barely any proofs in comparison to CLRS. A l ...more

The book can be used as a reference that you can use to understand a specific topic.

The first half of the book tells you why some things take longer to compute than other things. This helps data scientists / statisticians / analysts who work with large amounts of data.

In the first half, the math and the computer code can get pretty heavy. But I found the text around it was written so you could skim the hard stuff, get the idea, and keep going.

The second half of the book is a reference. As Hadley Wickham said in ...more

This book, like most academic books, is hard to read and comprehend and needs the reader to do more research about the subjects. I wish people who write these books, they come out of thei ...more

Every chapter starts off with a problem statement, then questions are asked to help identify hidden nuances of the problem, followed by a "War story" showing where exactly that particular algorithm found it's application and tricky exercises of course.

Author provides dozens of references to each topic so the reader could study the particular subject in deta ...more

What makes this book better than most other books about the topic is the scrupulous definition of each term, and the absurdly clear explanation of every problem and heuristic that's presented throughout the volume.

Overall, i'd definitely suggest this book to anyone interested in algorit ...more

Something I really like is how he shows how useful graph theory can be. If you can turn your problem into a graph (and you'd be surprised how often you can) there are a lot of non-obvious algorithms that will beat the pants of any non-graphical algorithm. I got a B.A. in math, the most useful class I took was graph theory.

Aug 10, 2019
Elkhan Ibrahimov
rated it
really liked it
·
review of another edition

Shelves:
algorithm

explanation and examples are really good

Aug 10, 2019
Sebastian Baker
marked it as to-read

My

It is amazing how often the reason you can't find a convincing explanation for something is because your conclusion is wrong.

This book makes you feel actually thrilled about using algorithms to solve practical scale problems and feel invigorated, not all bored, about software eng ...more

The only downside is the author's glib dismissal of the importance of parallel computing and the realities of hardw ...more

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