Each algorithm in this book is presented in its own section where you will find individual performance data on the behavior of the algorithm. In this benchmarking chapter, we present our infrastructure to evaluate algorithm performance. It is important to explain the precise means by which empirical data is computed, to enable the reader to both verify that the results are accurate and understand where the assumptions are appropriate or inappropriate given the context in which the algorithm is intended to be used.

There are numerous ways by which algorithms can be analyzed. Chapter 2 presented the theoretic formal treatment, introducing the concepts of worst-case and average-case analysis. These theoretic results can be empirically evaluated in some cases, though not all. For example, consider evaluating the performance of an algorithm to sort 20 numbers. There are 2.43*10^{18} permutations of these 20 numbers, and one cannot simply exhaustively evaluate each of these permutations to compute the average case. Additionally, one cannot compute the average by measuring the time to sort all of these permutations. We find that we must rely on statistical measures to assure ourselves that we have properly computed the expected performance time of the algorithm.

In this chapter we briefly present the essential points to evaluate the performance of the algorithms. Interested readers should consult any of the large number of available textbooks on ...

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