Tutorials update

[Michaël Baudin]

Hi,

We have recently updated two documents which are provided to the Scilab 
community under the "Creative Commons License". These documents are 
provided in the Tutorials section of the scilab.org website :

http://www.scilab.org/support/documentation/tutorials

In "Introduction to Scilab" (updated 11/2010), we have added many 
exercises, and their answers. More than 10 sections were added, 
including "Issues with floating point integers", "Levels in the call 
stack", "Matrices are dynamic", "Debugging with pause", "Contour plots", 
and many more. Many thanks to Artem Glebov, who translated this document 
into Russian and proofread the document.

In "Scilab is not naive" (updated 12/2010), we have added many exercises 
in "Complex Division" and "Quadratic equation", with their answers. We 
have udpated the "Why sin(pi) is rounded" and fixed many typos.

Best regards,

Michaël Baudin

PS

"Introduction to Scilab"

Overview of Scilab features to get familiar with this environment.
The goal is to present the core of skills necessary to start with 
Scilab. In the first part, we present how to get and install this 
software on our computer. We also present how to get some help with the 
provided in-line documentation and also thanks to web resources and 
forums. In the remaining sections, we present the Scilab language, 
especially its structured programming features. We present an important 
feature of Scilab, that is the management of real matrices and overview 
the linear algebra library. The definition of functions and the 
elementary management of input and output variables is presented. We 
present Scilab graphics features and show how to create a 2D plot, how 
to configure the title and the legend and how to export that plot into a 
vectorial or bitmap format.


"Scilab is not naive"

Most of the time, the mathematical formula is directly used in the 
Scilab source code. But, in many algorithms, some additional work is 
performed, which takes into account the fact that the computer does not 
process mathematical real values, but performs computations with their 
floating point representation. The goal of this article is to show that, 
in many situations, Scilab is not naive and use algorithms which have 
been specifically tailored for floating point computers. We analyze in 
this article the particular case of the quadratic equation, the complex 
division and the numerical derivatives. In each example, we show that 
the naive algorithm is not sufficiently accurate, while Scilab 
implementation is much more robust.

-- 
Michaël Baudin
Ingénieur de développement
michael.baudin at scilab.org
-------------------------
Consortium Scilab - Digiteo
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