Algorithms

by Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh Vazirani

This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features The use of boxes to strengthen the pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated.Carefully chosen advanced topics that can be skipped in a standard one-semester course, but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text, DasGupta also offers a Solutions Manual, which is available on the Online Learning Center." Algorithms is an outstanding undergraduate text, equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel, it is a joy to read." Tim Roughgarden Stanford University

Genres: Computer Science, Programming, Algorithms, Technology, Nonfiction, Mathematics, Software

336 pages, Paperback

First published September 13, 2006

Reviews

[Max Savchenko]

Да, все труЪ программеры спят с томом Кнута или Кормена под подушкой (и обязательно хвалятся этим при каждом удобном случае). Но если у вас из-за толщины этих инородных тел под головой регулярно затекает шея, то положите вместо них эту вот книгу. Она тоньше и читается легче, позволяя при этом вам гордо говорить всем (и писать в вашем резюме программиста), что у вас есть "базовая алгоритмическая подготовка".

[Cathy Wu]

Best text I've seen for algorithms at an undergraduate level. The focus of this book is on providing intuition and succeeds in communicating points without getting bogged down in technical details. Some readers may find the language too informal, so for the active learner, this book can be supplemented with other texts as well. I especially enjoyed the quantum computing chapter at the end, which beautifully presents a very complex approach to factoring in polynomial time.

[Harlen]

Goes over basic algorithms and has tons of questions. However there aren't any solutions (even online). The lack of answers makes this book a pretty much useless.

Save yourself the time and don't use this book.

[harshini]

Quite the experience to read a book authored by the person teaching you.

Also is this cheating if I am trying to hit my GR goal? You tell me.

[Abdussami Tayyab]

During the entire time I read this book, I was amazed by the simplicity in the content. It's so nicely written. I keep wondering why is it so under-rated, rather than Cormen's which has wretched code snippets and it's a bit difficult to understand. Although this book is not for beginners as compared to Cormen's but you've got to read this book sometime.

[Houssem]

This is a better book than CLRS, brief and straight to the point, an undergrad can actually read it in a semester.

[Lucille Nguyen]

Readable little textbook that covers a bunch of material on algorithms, linear and dynamic programming, P vs NP, search, the like. Plenty of practice problems.

[Arvind]

Sheer poetry. Who knew math could be communicated this beautifully? Being taught by the legendary Professor Papadimitriou himself, it was like listening to a samurai master (with a strong Greek accent) talk casually about his algorithmic achievements that have changed humanity's trajectory. I consider it a lifetime honour.

[Alex]

This is a fairly decent textbook, with the caveat that it is probably best suited to be the basis of a course in the subject, not so much a reference text. A couple of the chapters feel somewhat out of place considering the subject, only to crop up again towards the end as part of a loose "narrative" structure. While going through this, I would often refer to Cormen et al. for further clarification on particular topics (or topics that I needed information on, but which were not covered at all here). Works for what it is, though, and the chapters on NP completeness were quite strong.

[Jagdeep Pani]

It is showing an abstract view of Algorithms.
Beginners may find difficult to understand..
But its a nice book with beautiful exercises

[Gavin]

Does not do a good job of clearly explaining the algorithms. Sometimes the authors don't say what a variable means in an algorithm. There are some mistakes as well in the book.

[Jacob Williams]

I read this because it’s the textbook for the OMSCS algorithms class. The book is relatively short and does a pretty good job with the material it covers. Probably the most memorable thing that was new to me was the so-called “master theorem” for determining the runtime of recursive algorithms.

I have no idea what the quantum computing chapter was talking about, but that’s probably more a reflection of me not being ready to dive into that topic right now than of any deficiency in the book.

(crosspost)

[yu]

This is a nice and concise book covering the more theoretical spectrum of algorithms. Unlike the popular CLRS, you can easily read this book cover to cover due to its compactness. Aside from the usual algorithms materials, this book contains two additional chapters dedicated to cryptography and quantum computing, which is quite interesting (but difficult) to read.

Would recommend this book rather than CLRS for undergraduate computer science students.

[Minervas]

I read this textbook because it's free and recommended by Tim Roughgarden in his excellent Coursera courses (https://www.coursera.org/specializati...). Compared to the online courses, the textbook is more systematic and provides additional discussion on linear programming and Cryptography. It even includes a chapter on algorithm based on quantum computing, which I skipped. If your goal is to crack coding interviews, the coursera courses mentioned above will help you better.

[Jackson Eshbaugh]

An absolutely excellent text for anyone studying computer science or algorithms. The authors are incredibly talented at explaining complex topics simply. They break down these algorithms, mathematical proofs, and concepts into bite-sized pieces that are easy to digest. As far as textbooks go, this one reads in such a pleasant way, as though the authors are personally guiding you through the world of algorithms.

[Tareef Mando]

درسنا الكتاب ضمن مقرر تحليل الخوارزميات CS 3304

من أصعب المواد التي درستها في الجامعة إلى الآن، لكن بالتركيز على الصورة العريضة والمفاهيم الكبيرة وتجنب الغرق في التفاصيل تمكنت من تحقيق A-
الكتاب جيد، لكنه يخوض بالكثير من التفاصيل الدقيقة التي تبين أنها لم تلزم كثيرا في إنجاز الوظائف والنجاح في الاختبارات
المادة صعبة لكنها مرضية من الناحية العقلية

[Jack Graves]

One of the best textbooks I've ever read. It read like a novel; I was hooked the entire time. The algorithms in this book are beautiful, and I would recommend this as a near must-read to anyone learning computer science.

[James Wilson]

One of the better textbooks out there. Well though out chapters and exercises that break down the core themes across algorithms such as use cases, designs, and runtimes. Practice problems and solutions great. Made for a straightforward education in my graduate algorithms course this semester!

[Shayne]

Will come back to this one for reference and study. Solid stuff.

[Rotsoc]

This book has a whole lot of information that I don't understand at all.

[Yijia Chen]

Good book on algorithms.

[Sayed Hisham]

Succinct and beautifully written.

[Robert]

I taught from CLRS for years, but I've come around to thinking this is much more suitable for undergrads.

[Ajinkya Borade]

This text explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Better than the "Introduction to Algorithms" book

[NoName66]

This book is concise and easy to read, perfect book for the student who learn algorithm in the first time.

[Jordan Krishnayah]

this book made me cry

not tears of joy but tears of misery holy shit i hate this class sm

it explains things really well though

10/10 book so far will update rating throughout the semester

[Riya Roshan]

I fucking hate this class but a book is a book

[Divy]

Book is very succinct and ties together a lot of mathematical tools and rigor into algorithmic theory. It’s a good book to possibly read front to back, but sometimes presupposes background from the reader. I stopped doing the exercises—they were usually too mathematically based, or not worded well. without a reliable place to find solutions it doesn’t make sense to do them. i’ll continue reading the book with other texts & resources to supplement.

[Joshua Anderson]

A wonderful well-writen text for an intoduction to Algorithms. I learned more from this book's 7ish page description of the simplex algorithm than I did from an entire semester of an optimization course.

[Alex Ptakhin]

Very good introduction to algorithms. With small size it shows algoritms, real examples for them and brief proofs omitting some corners. It can't replace such foundamental books e.g. Knuth or Cormen, but it's ok to show algorithms beauty without conclusive mathematical proof.