To be sure to be well prepared for an interview, I decided to read several **Algorithms book**. I also chosen books in order to have information about data structures. I chose these books to read:

- Data Structures & Algorithm Analysis in C++, Third Edition, by Clifford A. Shaffer
- Algorithms in a Nutshell, by George T. Heineman, Gary Pollice and Stanley Selkow
- Algorithms, Fourth Edition, by Robert Sedgewick and Kevin Wayne
- Introduction to Algorithms, by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein. I have to say that I have only read most of it, not completely, because some chapters were not interesting for me at the current time, but I will certainly read them later.

As some of my comments are about the presentation of the books, it has to be noted that I have read the three first books on my Kindle.

In this post, you will find my point of view about all these books.

### Data Structures & Algorithm Analysis in C++

This book is really great. It contains a lot of data structures and algorithms. Each of them is very clearly presented. It is not hard to understand the data structures and the algorithms.

Each data structure is first presented as an ADT (Abstract Data Structure) and then several possible implementations are presented. Each implementation is precisely defined and analyzed to find its sweet pots and worst cases. Other implementations are also presented with enough references to know where to start with them.

I have found that some other books about algorithms are writing too much stuff for a single thing. This is not the case with this book. Indeed, each interesting thing is clearly and succinctly explained.

About the presentation, the code is well presented and the content of the book is very well written. A good think would have been to add a summary of the most important facts about each algorithm and data structure. If you want to know these facts, you have to read several pages (but the facts are always here).

The book contains very good explanation about the complexity analysis of algorihtms. It also contains a very interesting chapter about limits to computation where it treats P, NP, NP-Complete and NP-Hard complexity classes.

This book contains a large number of exercises and projects that can be used to improve even more your algorithmic skills. Moreover, there are very good references at the end of each chapters if you want more documentation about a specific subject.

I had some difficulty reading it on my Kindle. Indeed, it’s impossible to switch chapters directly with the Kindle button. If you want quick access to the next chapter, you have to use the table of contents.

### Algorithms in a Nutshell

This book is much shorter than the previous one. Even if it could be a good book for beginners, I didn’t liked this book a lot. The explanations are a bit messy sometimes and it could contain more data structures (even if I know that this is not the subject of the book). The analysis of the different algorithms are a bit short too. Even if it looks normal for a book that short, it has to be known that this book has no exercise.

However, this book has also several good points. Each algorithm is very well presented in a single panel. The complexity of each algorithm is directly given alongside its code. It helps finding quickly an algorithm and its main properties.

Another thing that I found good is that the author included empiric benchmarks as well as complexity analysis. The chapters about Path Finding in AI and computational geometry were very interesting, especially because it is not widely dealt with in other books.

It also has very good references for each chapter.

This book was perfect to read with Kindle, the navigation was very easy.

### Algorithms

This book is a good book, but suffers from several drawbacks regarding to other books. First, the book covers a lot of data structures and algorithms. Then, it also has very good explanations about complexity classes. It also has a lot of exercises. I also liked a lot the chapter about string algorithms that was lacking in previous books.

Most of the time, the explanations are good, but sometimes, I found them quite hard to understand. Moreover, some parts of code are also hard to follow. The author included Java runs of some of programs. In my opinion, this is quite useless, empiric benchmarks could have been useful, but not single runs of the program. Some of the diagrams were also hard to read, but that’s perhaps a consequence of the Kindle.

A think that disappointed me a bit is that the author doesn’t use big Oh notation. Even, if we have enough information to easily get the Big Oh equivalent, I don’t understand why a book about algorithms doesn’t use this notation.

Just like the first book, there is no simple view of a given algorithm that contains all the information about an algorithm. Another think that disturbed me is that the author takes time to describe an API around the algorithms and data structures and about the Java API. Again, in my opinion only, it takes a too large portion of the book.

Again, this book was perfect to read with Kindle, the navigation was very easy.

### Introduction to Algorithms

This book is the most complete I read about algorithms and data structures by a large factor. It has very complete explanations about complexity analysis: big Oh, Big Theta, Small O. For each data structure and algorithm, the complexity analysis is very detailed and very well explained. The pieces of code are written in a very good pseudo code manner.

As I said before, the complexity analysis are very complete and sometimes very complex. This can be either an advantage or a disadvantage, depending of what you awaits from the book. For example, the analysis is made using several notations Big Oh, Big Theta or even small Oh. Sometimes, it is a bit hard to follow, but it provides very good basis for complexity analysis in general.

The book was also the one with the best explanations about linear time sorting algorithms. In the other books, I found difficult to understand sorts like counting sort or bucket sort, but in this book, the explanations are very clear. It also includes multithreaded algorithm analysis, number theoretic algorithms, polynomials and a very complete chapter about linear programming.

The book contains a huge number of exercises for each chapters and sub chapters.

This book will not only help you find the best suited algorithm for a given problem, it will also help you understand how to write your own algorithm for a problem or how to analyze deeply an existing solution.

### Algorithms Book Wrap-up

As I read all these Algorithms books in order, it’s possible that my review is a bit subjective regarding to comparisons to other books.

If you plan to work in C++ and need more knowledge in algorithms and C++, I advice you to read **Data Structures & Algorithm Analysis in C++**, that is really awesome. If you want a very deep knowledge about algorithm analysis and algorithms in general and have good mathematical basis, you should really take a deep look at **Introduction to Algorithms**. If you want short introduction about algorithms and don’t care about the implementation language, you can read **Algorithms in a Nutshell**. **Algorithms** is like a master key, it will gives you good starting knowledge about algorithm analysis and a broad range of algorithms and data structures.

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