The Algorithm Design Manual (Texts in Computer Science)

Product Information

This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques , provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources , is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. The book teaches you how to extract the relevant information from a problem, how to transform a given problem into a well-researched problem, how to select the best data structure for the job and how to really improve algorithms. .. Many algorithms are presented with actual code (written in C)-- Provides comprehensive references to both survey articles and the primary literature I've read through the first half of the book, and am now at the catalog of algorithms that makes up the second half, and I've really enjoyed the book so far.

The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography.For someone who's never taken CS101, this book an eye-opener into the hows and whys of basic data structures like linked lists, trees, hash tables, and arrays, as well as sorting techniques and more advanced practices like dynamic programming. Clear explainers are interspersed with practical war stories, where Skiena explains how he applied the technique just discussed to solve a previously intractable problem. It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!"

I'll be upfront that I'm a pragmatist as a programmer. I some actual training in data science and machine learning, which is arcane enough on it's own, and a few years experience to call myself Good With Pandas, but the thing about being an autodidact solving a limited set of business problems in Python is that you miss the big picture. In a mature ecosystem like Python, a lot of the time the right answer is just "pip install magiclib. from magiclib import incantation. bar = incantation(foo)" Except sometimes magiclib doesn't exist yet. At the end of the day, computers are all Turing machines, they all solve the same sets of problems, but some approaches are algorithmically tractable, and some will leave you lost in the Swamp of Sadness. These are older school programming interview questions and have been largely phased out. It’s better to spend time elsewhere.I would give this book 4 1/2 stars; it has a fair share of typos, and sometimes problems are duplicated in different sections; this probably reflects how the book was updated. The book is rather lighter on proofs than, say Cormen/Leiserson/Rivest/etc., and so one should probably have a more rigorous book at hand to fill in some details when necessary. The choice of topics and the style reflect the author's extensive consulting experience, as well has his work on contest programming (he wrote another entire book dedicated to Programming Challenges). The fact that this book focuses on working source code in examples (as opposed to just pseudo-code) makes it extremely useful for drilling for programming interviews. The remainder of his advice is also invaluable. Here are a couple of other very valuable resources: When the chapters switch to real world examples, you can move on to other resources. This section is titled The Hitchhiker's Guide to Algorithms. This part of the book is incredibly interesting, but not the best use of time when preparing for programming interviews. The "no theorem-proof" style provides a uniquely accessible and intuitive approach to a challenging subject

I also love that the example code is in C. Too many books give example code in languages with a lot of overhead, like Java, and end up obscuring the important parts with a ton of object-oriented crap. Yes - OOP is nice, but unless I'm reading a book on OOP, I don't want to dig through 30 lines of irrelevant boilerplate just to find the 10 lines relevant to the algorithm. It is wonderful to open to a random spot and discover an interesting algorithm. This is the only textbook I felt compelled to bring with me out of my student days.... The color really adds a lot of energy to the new edition of the book!" (Cory Bart, University of Delaware) I love the exercises presented in this book, they are well crafted problems, very helpful in reinforcing the learning process. Steven Skiena’s Algorithm Design Manual retains its title as the best and most comprehensive practical algorithm guide to help identify and solve problems. … Every programmer should read this book, and anyone working in the field should keep it close to hand. … This is the best investment … a programmer or aspiring programmer can make."As an example, older calculators always started "high" when finding quadratic factors or square roots. Skiena (p. 134) shows that three possible "front ends" can make subsequent iteration (trial and error until you're done) faster and more efficient: 1. The older always start high method 2. A common bisection technique-- split the problem, then, by "divide and conquer" go higher or lower and 3. Use interpolation to get closer sooner, then iterate. Data Structures: Repeatedly extracts the smallest remaining element from the unsorted part of the set but will take a total of O(n The reader-friendly Algorithm Design Manualprovides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Practical Algorithm Design, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, the Hitchhiker's Guide to Algorithms, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations, and an extensive bibliography.