[Scilab-Dev] documentation translation

[Michaël Baudin]

Hi,

Thank you for your interest in this document.

This day, we released four documents under the terms of the
"Creative Commons Attribution-ShareAlike 3.0 Unported License":
 * Introduction to Scilab
 * Introduction to Discrete Probabilities with Scilab
 * Scilab is Not Naive
 * Nelder-Mead User's Manual
The sources of these documents are hosted in the Scilab forge.
See the PS of this message for a complete list.
Anyone willing to translate these document, or contribute to these 
projects,
is welcome. Critisizing their content is another possible form
of contribution to these documents (up to a certain level !).

The document you are specifically interested in at:

http://forge.scilab.org/index.php/p/docintrotoscilab/

If you will, you can create an account on the forge.
This way, I can add you to the list of developpers of this
project, so that you can create the russian translation and
make the translated version available through the Forge.
Feel free to proceed as you wish, since using the forge
is not mandatory.

Please contact me for any information you might need.

Best regards,

Michaël Baudin

PS
You will find below the abstract of each document and
various urls of the forge.

Introduction to Scilab
In this article, we present an introduction to discrete probabilities 
with Scilab. Numerical experiments are based on Scilab. The first 
section present discrete random variables and conditionnal 
probabilities. In the second section, we present combinations problems, 
tree diagrams and Bernouilli trials. In the third section, we present 
simulation of random processes with Scilab.
http://forge.scilab.org/index.php/p/docintrotoscilab/
http://forge.scilab.org/index.php/p/docintrotoscilab/source/tree/HEAD/
http://forge.scilab.org/index.php/p/docintrodiscrprobas/downloads/

Introduction to Discrete Probabilities with Scilab
In this article, we present an introduction to discrete probabilities 
with Scilab. Numerical experiments are based on Scilab. The first 
section present discrete random variables and conditionnal 
probabilities. In the second section, we present combinations problems, 
tree diagrams and Bernouilli trials. In the third section, we present 
simulation of random processes with Scilab.
http://forge.scilab.org/index.php/p/docintrodiscrprobas/
http://forge.scilab.org/index.php/p/docintrodiscrprobas/source/tree/HEAD/
http://forge.scilab.org/index.php/p/docintrodiscrprobas/downloads/

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.
http://forge.scilab.org/index.php/p/docscilabisnotnaive/
http://forge.scilab.org/index.php/p/docscilabisnotnaive/source/tree/HEAD/
http://forge.scilab.org/index.php/p/docscilabisnotnaive/downloads/

Nelder-Mead User's Manual
In this document, we present the Nelder-Mead component provided in Scilab.
The introduction gives a brief overview of the optimization features of the
component and present an introductory example. Then we present some theory
associated with the simplex, a geometric concept which is central in
the Nelder-Mead algorithm. We present several method to compute an
initial simplex. Then we present Spendley's et al. fixed shape 
unconstrained
optimization algorithm. Several numerical experiments are provided, which
shows how this algorithm performs on well-scaled and badly scaled 
quadratics.
In the final section, we present the Nelder-Mead variable shape
unconstrained optimization algorithm. Several numerical experiments are 
presented,
where some of these are counter examples, that is cases where the 
algorithms
fails to converge on a stationnary point. In the appendix of this document,
the interested reader will find a bibliography of simplex-based algorithms,
along with an analysis of the various implementations which are available
in several programming languages.
http://forge.scilab.org/index.php/p/docneldermead/
http://forge.scilab.org/index.php/p/docneldermead/source/tree/HEAD/
http://forge.scilab.org/index.php/p/docneldermead/downloads/




Artem Glebov a écrit :
> Hello,
>
> I would like to translate the document "Introduction to Scilab" (by Michaël
> Baudin) into Russian.
>
> Please tell me:
> 1) if anybody is doing this job already (translating tutorials from English
> to Russian)?
> 2) where I can find the template (LaTeX, I guess) for this document and
> picture files?
>
> Thank you.
> Artem Glebov
>
>   


-- 
Michaël Baudin
Ingénieur de développement
michael.baudin at scilab.org
-------------------------
Consortium Scilab - Digiteo
Domaine de Voluceau - Rocquencourt
B.P. 105 - 78153 Le Chesnay Cedex
Tel. : 01 39 63 56 87 - Fax : 01 39 63 55 94