The Algorithm Design Manual (Texts in Computer Science)

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Use at your own risk. The author, Springer, and the State University of New York make no representations, express or implied, with respect to any software or documentation we describe. The authors, Springer, and the State University of New York shall in no event be liable for any indirect, incidental, or consequential damages. 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. Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them

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.This book has covered most, if not all, of the fundamental topics in computer science at Bachelor of Science level, some selected topics in graduate level. Dr. Skiena did a very good job of explaining many of the subjects (concepts, data structures and algorithms) in a clear and concise manner. I like the most about his coverage on Data Structure and Graph. Some of the highlights of this book: This is a chapter you should read, and do a few problems for, but you don’t want to let this chapter kill your pacing. 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. Many algorithms are presented with actual code (written in C)-- Provides comprehensive references to both survey articles and the primary literature 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. ..

This book, however, takes a different approach, and serves as a guide book for using algorithms in the real world. There's a heavy emphasis on formulating problems in terms of existing, solved problems. If you can "map" your problem to one with a known solution, then you can use the proven, existing solution to solve your problem. To emphasize that point, roughly the entire second half of the book is a catalog of known problems and solutions, with references to software libraries, books and other sources of information. This newly expanded and updated second edition 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. 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. 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."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. Errata Solution Wiki Algorithm Repository Programs Lecture Notes Consulting Services Credits Second Edition 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.

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!"New and expanded coverageof randomized algorithms, hashing, divide and conquer, approximation algorithms, and quantum computing