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Algorithms Unlocked (The MIT Press)
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For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.
Have you ever wondered how your GPS can find the fastest way to your destination, selecting one route from seemingly countless possibilities in mere seconds? How your credit card account number is protected when you make a purchase over the Internet? The answer is algorithms. And how do these mathematical formulations translate themselves into your GPS, your laptop, or your smart phone? This book offers an engagingly written guide to the basics of computer algorithms. In Algorithms Unlocked, Thomas Cormen―coauthor of the leading college textbook on the subject―provides a general explanation, with limited mathematics, of how algorithms enable computers to solve problems.
Readers will learn what computer algorithms are, how to describe them, and how to evaluate them. They will discover simple ways to search for information in a computer; methods for rearranging information in a computer into a prescribed order (“sorting”); how to solve basic problems that can be modeled in a computer with a mathematical structure called a “graph” (useful for modeling road networks, dependencies among tasks, and financial relationships); how to solve problems that ask questions about strings of characters such as DNA structures; the basic principles behind cryptography; fundamentals of data compression; and even that there are some problems that no one has figured out how to solve on a computer in a reasonable amount of time.
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Thomas Cormen helps readers to achieve a broad understanding of the key algorithms underlying much of computer science. For computer science students and practitioners, it is a great review of key algorithms that every computer scientist must understand. For non-practitioners, it truly unlocks the world of algorithms at the heart of the tools we use every day.―G. Ayorkor Korsah, Computer Science Department, Ashesi University College
About the Author
- Publisher : The MIT Press (March 1, 2013)
- Language : English
- Paperback : 240 pages
- ISBN-10 : 0262518805
- ISBN-13 : 978-0262518802
- Reading age : 18 years and up
- Grade level : 12 and up
- Item Weight : 12 ounces
- Dimensions : 9.25 x 5.69 x 0.48 inches
- Best Sellers Rank: #530,933 in Books (See Top 100 in Books)
- #37 in Computer Algorithms
- #136 in Programming Algorithms
- Customer Reviews:
About the author
Reviewed in the United States on January 26, 2022
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1. It has a section on "Algorithms on Strings". Out of many books that I perused (Algorithms in a nutshell, Skiena, Eva Tardos etc.), this book, CLRS, Algorithms by Sedgwick has a section on Strings. There are dedicated books on String algorithms but "string problems" appear practically in almost every software engineer's career that I think any general algorithms book should cover a basic portion of it. This one does.
2. Chatty but neither boring nor tedious. It uses enough words to convey the concept efficiently.
3. It contains math for sections on complexity but algorithm concepts are supported with pictures, sample algorithm runs. One just needs to follow logical arguments as it is explained.
4. All the chapters except last few pages in chapter 9, 10 are gems.
5. The length of the book is ~222. This cannot be overstated. The faster u reach towards the end of the book the better you will feel about yourself and the more you will like to finish it.
6. Pretty good paper quality and print. Love this about MIT press.
7. Price is cheap.
8. No exercises. Yes this is a good thing actually. It would have affected the flow of the book. If you need exercises then go to Big Cormen (CLRS).
1. Typos/Errors. 14 when I counted. I actually thought it cannot have any because some reviewer here said it was copyedited by someone who is a stickler for perfection so I didn't bother to check the errata page (my bad but only 14 errata’s is still impressive) until I stumbled on a possible typo (it turns out it is not) and tried to contact the author by going to the book's website. One should make sure to correct it in the book before reading.
2. Chapter 9 - section on LZW compression/decompression could have been little clearer. Chapter 10 - section on Hamiltonian cycle to Hamiltonian path reduction, subset sum reduction could have been little clearer because the explanation had more gaps in logic than usual. The author did say in the preface that he couldn't control getting into more details near the very end of the book but I felt the explanation was unclear because it is rushed than more technical details are employed.
All in all this is a solid book that treats you as an intelligent human being than a space alien or a brick.
As stated in other reviews & in the book's intro, the author assumes no prior programming knowledge.
It's confusing to consider Arrays as starting a 1 & having to add/subtract everywhere, but ok I can deal with it...
What wasn't clear at purchase time was the assumption of familiarity with math at let's say "Algebra II" or pre-calc levels.
If you don't understand or remember how to deal with factorials, polynomials, exponents & logarithms, you'll need to look those up.
These are not particularly difficult or high-level, but my last prolonged exposure to these concepts was more than a decade ago.
I do appreciate the need to keep the concepts abstract enough to apply across various applications, but it would be nice to see some concrete implementation. I'd have a much easier time following code examples in a language/syntax I've seen more recently.
I'm considering the "Algorithms 4th Ed" book by Sedgewick & Wayne for that reason: practical application w/examples in Java.
Modern programming best practices described in books like "The Pragmatic Programmer" or "Code Complete" also clearly emphasize the need to use unique/meaningful names for variables and functions so that people can easily follow your work. It also helps YOU to follow your own work. This book instead takes you back to math class where everything is a meaningless jumble of alphabet soup - "x,y,z,q,p,r,a" - and you have to constantly bounce back & forth (sometimes over several pages) to keep track of what's what.
Which example gives you a better idea what's going on?:
sortedBooksArray = unsortedBooksArray[x]
... OR ...
A[q-1] = R[p]
Yes, algorithms should be abstract, but if the goal is to explain them clearly they need to first be presented in a concrete way.
This is why so many people hate & don't understand math - those who love & understand it fail to describe the practical uses for it in understandable terms. The notation & abstraction work against the stated goal of making algorithms more accessible.
Perhaps that's by design, because I'm going to wind up re-writing the psuedocode with my own examples to gain a better understanding. This weekend I will have stacks of books strewn about the living room while I attempt to implement QuickSort for real, and will thereafter come up with some scenarios where I can implement it in code myself. But in that case, I probably could've gotten the same effect from an online tutorial...
So far, it's not a bad book - I just haven't really learned anything practical, or that I didn't already know from other online sources that present the topic in a more understandable manner.
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The code examples are not great either. The indexes of arrays start at 1 to make it easier to understand for non-programmers but makes it harder for programmers, and yet it is highly mathematical and abstract even for me as a programmer. I find it hard to believe a non-programmer can read this book and make sense of most of it.
Pourquoi les étoiles en moins alors ? Malheureusement l'auteur s'est enfermé dans la posture énervante de rédiger un teaser pour CLRS. Il est normal de renvoyer à CLRS pour les sujets non traités, mais il arrive trop souvent, à chaque chapitre en fait, que l'auteur renvoie à CLRS pour les sujets traités, et pas pour des raffinements qui n'auraient pas leur place dans une introduction, mais pour des éléments qui y ont toute leur place comme la bibliographie ou l'histoire des algorithmes décrits. Introduire un lecteur à l'algorithmique c'est aussi guider ses premiers pas dans la bibliographie et lui expliquer un petit peu comment se sont développées les idées dans ce domaine ; ça ne peut pas se réduire à « allez-voir le livre sacré » !