They say the workman is only as good as his tools; in experimental algorithmics the workman must often build his tools.

The test environment is the collection of programs and files assembled together to support computational experiments on algorithms. This collection includes test programs that implement the algorithms of interest, code to generate input instances and files containing instances; scripts to control and document tests, tools for measuring performance, and data analysis software.

This chapter presents tips for assembling and building these components to create a reliable, efficient, and flexible test environment. We start with a survey of resources available to the experimenter. Section 5.1 surveys aspects of test program design, and Section 5.2 presents a cookbook of methods for generating random numbers and combinatorial objects to use as test inputs or inside randomized algorithms.

Most algorithm researchers prefer to work in Unix-style operating systems, which provide excellent tools for conducting experiments, including:

• Utilities such as time and gprof for measuring elapsed and CPU times.

• Shell scripts and makefiles. Shell scripting makes it easy to automate batches of tests, and makefiles make it easy to mix and match compilation units. Scripts and make files also create a document trail that records the history of an experimental project.

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Information Transmission Theorem

The reliable transmission of information-bearing signals over a noisy communication channel is at the heart of what we call communication. Information theory, founded by Claude E. Shannon in 1948 [Sha48], provides a mathematical framework for the theory of communication. It describes the fundamental limits to how efficiently one can encode information and still be able to recover it with negligible loss.

At its inception, the main role of information theory was to provide the engineering and scientific communities with a mathematical framework for the theory of communication by establishing the fundamental limits on the performance of various communication systems. Its birth was initiated with the publication of the works of Claude E. Shannon, who stated that it is possible to send information-bearing signals at a fixed code rate through a noisy communication channel with an arbitrarily small error probability as long as the code rate is below a certain fixed quantity that depends on the channel characteristics [Sha48]; he “baptized” this quantity with the name of channel capacity (see the discussion in Chapter 6). He further proclaimed that random sources – such as speech, music, or image signals – possess an irreducible complexity beyond which they cannot be compressed distortion-free. He called this complexity the source entropy (see the discussion in Chapter 5). He went on to assert that if a source has an entropy that is less than the capacity of a communication channel, then asymptotically error-free transmission of the source over the channel can be achieved.