George T. Heineman - Algorithms in a nutshell

Figure 1. Example of pseudocode commonly found in textbooks

The algorithm description in this figure comes from Wikipedia ( to see our code listing by comparison. We use only documented, well-designed code to describe the algorithms. Use the code we provide as-is, or include its logic in your own programming language and software system.

Some algorithm textbooks do have full real-code solutions in C or Java. Often the purpose of these textbooks is to either teach the language to a beginner or to explain how to implement abstract data types. Additionally, to include code listings within the narrow confines of a textbook page, authors routinely omit documentation and error handling, or use shortcuts never used in practice. We believe programmers can learn much from documented, well-designed code, which is why we dedicated so much effort to develop actual solutions for our algorithms.

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 , 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 18 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."