---
product_id: 28912831
title: "Algorithms in a Nutshell: A Practical Guide"
brand: "george t. heinemangary pollicestanley selkow"
price: "KD 29.21"
currency: KWD
in_stock: true
reviews_count: 12
url: https://www.desertcart.com.kw/products/28912831-algorithms-in-a-nutshell-a-practical-guide
store_origin: KW
region: Kuwait
---

# Algorithms in a Nutshell: A Practical Guide

**Brand:** george t. heinemangary pollicestanley selkow
**Price:** KD 29.21
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Algorithms in a Nutshell: A Practical Guide by george t. heinemangary pollicestanley selkow
- **How much does it cost?** KD 29.21 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.com.kw](https://www.desertcart.com.kw/products/28912831-algorithms-in-a-nutshell-a-practical-guide)

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## Customer Reviews

### ⭐ 







  
  
    Needlessly obtuse and inaccurate
  

*by C***S on Reviewed in the United States on June 2, 2021*

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.

### ⭐⭐⭐⭐ 







  
  
    Excellent reference guide!
  

*by S***Y on Reviewed in the United States on December 3, 2019*

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 to a "Mini-Classic"
  

*by L***” on Reviewed in the United States on July 4, 2017*

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.

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*Product available on Desertcart Kuwait*
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*Last updated: 2026-03-16*