Section 1.4: Chapter 1 shows how information is represented in a computer memory. The limited size of the memry may cause curious rounding problems, usually neglected. This section shows some of the effects that can be produced by these errors.
Section 3.4: Chapter 3 shows how to use the C-language, but not starting from the usual "Hello, world!" example. In this book the first program is intended to convert a temperature from a scale to another. The section shown here illustrates the I/O functions of the C-language.
Section 4.2: prior to the illustration of the syntax of the instructions for selection, structured programming is presented in general. We give useful hints to avoid common mistakes.
Section 4.5: this is an example of how rounding problems are continuously recalled within the text. The discussion allows us to introduce non usual topics like the Kahan summing algorithm: a very useful one, though difficult to find in classic manuals.
Section 6.4: another example about how to use the language in a scientific, realistic context. Pointer arithmetics is shown as a way to improve program efficiency. We show bidimensional array representation, too: another topic that is rarely discussed in details.
Section 7.6: in order to illustrate the syntax for functions of functions, we show how to compute the derivative of a function, still discussing precision issues.
Section 8.2: scientists need programming to compute. This section shows several numerical integration methods, discussing the possible rounding problems, as usual. C-language algorithms are also fully discussed in the text.
Sections 13.1 e 13.2: unions, a virtual experiment, lists, a dictionary and recurrence.
Sections 16.3.1 e 16.3.2: in Chapter 16, about data structure, binary trees represent a must. Their use exploits recurrence in C-language and shows struct applications.
Section 17.2: in Chapter 17, after introducing the basic concepts on graphs, we show how to represent them in computer data structures.
Section 19.2.5: ergodicity breaking is one of the most important aspects of Monte Carlo simulations. We show how ergodicity breaking occurs in a unidimensional Markov chain.
Sections 20.3.1 e 20.3.2: for a correct analyis of the results of a simulation, it is important to know statistical methods used to estimate the errors on the measurements. In these sections we show two common methods.