O'Reilly logo

Algorithms in a Nutshell by Gary Pollice, Stanley Selkow, George T. Heineman

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Principle: Separate the Algorithm from the Problem Being Solved

It is hard to show the implementation for an algorithm "in the general sense" without also involving details of the specific solution. We are critical of books that show a full implementation of an algorithm yet allow the details of the specific problem to become so intertwined with the code for the generic problem that it is hard to identify the structure of the original algorithm. Even worse, many available implementations rely on sets of arrays for storing information in a way that is "simpler" to code but harder to understand. Too often, the reader will understand the concept from the supplementary text but be unable to implement it!

In our approach, we design each implementation to separate the generic algorithm from the specific problem. In Chapter 7, for example, when we describe the A*Search algorithm, we use an example such as the 8-puzzle (a sliding tile puzzle with tiles numbered 1–8 in a three-by-three grid). The implementation of A*Search depends only on a set of well-defined interfaces. The details of the specific 8-puzzle problem are encapsulated cleanly within classes that implement these interfaces.

We use numerous programming languages in this book and follow a strict design methodology to ensure that the code is readable and the solutions are efficient. Because of our software engineering background, it was second nature to design clear interfaces between the general algorithms and the domain-specific solutions. Coding in this way produces software that is easy to test, maintain, and expand to solve the problems at hand. One added benefit is that the modern audience can more easily read and understand the resulting descriptions of the algorithms. For select algorithms, we show how to convert the readable and efficient code that we produced into highly optimized (though less readable) code with improved performance. After all, the only time that optimization should be done is when the problem has been solved and the client demands faster code. Even then it is worth listening to C. A. R. Hoare, who stated, "Premature optimization is the root of all evil."

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required