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In this section, we outline the framework within which mathematical analysis can play a role in the process of comparing the performance of algorithms and thus lay a foundation for us to be able to consider basic analytic results as they apply to the fundamental algorithms that we consider throughout the tutorial. We shall consider the basic mathematical tools that are used in the analysis of algorithms, both to allow us to study classical analyses of fundamental algorithms and to make use of results from the research literature that help us understand the performance characteristics of our algorithms.

The following are among the reasons that we perform mathematical analysis of algorithms:

- To compare different algorithms for the same task
- To predict performance in a new environment
- To set values of algorithm parameters

We shall see many examples of each of these reasons throughout the tutorial. Empirical analysis might suffice for some of these tasks, but mathematical analysis can be more informative (and less expensive!), as we shall see.

The analysis of algorithms can be challenging indeed. Some of the algorithms in this tutorial are well understood, to the point that accurate mathematical formulas are known that can be used to predict running time in practical situations. People develop such formulas by carefully studying the program to find the running time in terms of fundamental mathematical quantities and then doing a mathematical analysis of the quantities involved. On the other hand, the performance properties of other algorithms in this tutorial are not fully understood—perhaps their analysis leads to unsolved mathematical questions, or perhaps known implementations are too complex for a detailed analysis to be reasonable, or (most likely) perhaps the types of input that they encounter cannot be characterized accurately.

Several important factors in a precise analysis are usually outside a given programmer's domain of influence. First, Java programs are translated into bytecode, and the bytecode is interpreted or translated into runtime code on a virtual machine (VM). The compiler, translator, and VM implementations all have an effect on which instructions on an actual machine are executed, so it can be a challenging task to figure out exactly how long even one Java statement might take to execute. In an environment where resources are being shared, even the same program can have varying performance characteristics at two different times. Second, many programs are extremely sensitive to their input data, and performance might fluctuate wildly depending on the input. Third, many programs of interest are not well understood, and specific mathematical results may not be available. Finally, two programs might not be comparable at all: one may run much more efficiently on one particular kind of input and the second may run efficiently under other circumstances.

All these factors notwithstanding, it is often possible to predict precisely how long a particular program will take, or to know that one program will do better than another in particular situations. Moreover, we can often acquire such knowledge by using one of a relatively small set of mathematical tools. It is the task of the algorithm analyst to discover as much information as possible about the performance of algorithms; it is the task of the programmer to apply such information in selecting algorithms for particular apps. In this and the next several sections, we concentrate on the idealized world of the analyst. To make effective use of our best algorithms, we need to be able to step into this world, on occasion.

The first step in the analysis of an algorithm is to identify the abstract operations on which the algorithm is based in order to separate the analysis from the implementation. Thus, for example, we separate the study of how many times one of our union–find imple-mentations executes the code fragment `i = a[i]` from the analysis of how many nanoseconds might be required to execute that particular code fragment on our computer. We need both these elements to determine the actual running time of the program on a particular computer. The former is determined by properties of the algorithm; the latter by properties of the computer. This separation often allows us to compare algorithms in a way that is independent of particular implementations or of particular computers.

Although the number of abstract operations involved can be large, in principle, the performance of an algorithm typically depends on only a few quantities, and typically the most important quantities to analyze are easy to identify. One way to identify them is to use a profiling mechanism (a mechanism available in many Java implementations that gives instruction-frequency counts) to determine the most frequently executed parts of the program for some sample runs. Or, like the union–find algorithms of , our implementation might be built on a few abstract operations. In either case, the analysis amounts to determining the frequency of execution of a few fundamental operations. Our modus operandi will be to look for rough estimates of these quantities, secure in the knowledge that we can undertake a fuller analysis for important programs when necessary. Moreover, as we shall see, we can often use approximate analytic results in conjunction with empirical studies to predict performance accurately.

We also have to study the data and to model the input that might be presented to the algorithm. Most often, we consider one of two approaches to the analysis: we either assume that the input is random and study the average-case performance of the program, or we look for perverse input and study the worst-case performance of the program. The process of characterizing random inputs is difficult for many algorithms, but for many other algorithms it is straightforward and leads to analytic results that provide useful information. The average case might be a mathematical fiction that is not representative of the data on which the program is being used, and the worst case might be a bizarre construction that would never occur in practice, but these analyses give useful information on performance in most cases. For example, we can test analytic results against empirical results (see ). If they match, we have increased confidence in both; if they do not match, we can learn about the algorithm and the model by studying the discrepancies.

In the next three sections, we briefly survey the mathematical tools that we shall be using throughout the tutorial. This material is outside our primary narrative thrust, and readers with a strong background in mathematics or readers who are not planning to check our mathematical statements on the performance of algorithms in detail may wish to skip to and to refer back to this material when warranted later in the tutorial. The mathematical underpinnings that we consider, however, are generally not difficult to comprehend, and they are too close to core issues of algorithm design to be ignored by anyone wishing to use a computer effectively.

First, in , we consider the mathematical functions that we commonly need to describe the performance characteristics of algorithms. Next, in , we consider the O-notation, and the notion of is proportional to, which allow us to suppress detail in our mathematical analyses. Then, in , we consider recurrence relations, the basic analytic tool that we use to capture the performance characteristics of an algorithm in a mathematical equation. Following this survey, we consider examples where we use the basic tools to analyze specific algorithms, in .

2.3 Develop an expression of the form c

_{0}+ c_{1}N + c_{2}N^{2}+ c_{3}N^{3}that accurately describes the running time of your program from Exercise 2.2. Compare the times predicted by this expression with actual times, for N = 10, 100, and 1000.2.4 Develop an expression that accurately describes the running time of Program 1.1 in terms of M and N.

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