Open source software for numerical computation

## Tutorials

### Xcos for very beginners

08/2013

The purpose of this document is to guide you step by step in exploring the various basic features of Xcos for a user who has never used a hybrid dynamic systems modeler and simulator.

This document also exists in Japanese and in Vietnamese:

### Xcos for engineering sciences teaching

06/2013 – only available in French

This booklet, realized with the support of Inria and co-written by Scilab Enterprises and teachers is a practical introduction to Scilab/Xcos with examples for teaching engineering sciences in French high schools and in higher education programs

livret_Xcos.pdf 7.24 MB

### Scilab for very beginners

02/2013

The purpose of this document is to guide you step by step in exploring the various basic features of Scilab for a user who has never used numerical computation software.

This document also exists in Japanese and in Vietnamese:

### Introduction to Scilab

Updated 11/2010

The goal of this document is to present Scilab features and the core of skills necessary to start with Scilab and get familiar with its environment.

introscilab.pdf 1.19 MB

### Scilab for mathematics teaching

2013 edition – only available in French

This booklet, realized with the support of Inria and co-written by Scilab Enterprises and teachers is a practical introduction to Scilab with examples based on French high school mathematics programs.

### Introduction to discrete probabilities with Scilab

01/2010

In this document, we present an introduction to discrete probabilities with Scilab (discrete random variables and conditional probabilities, combinations problems, tree diagrams and Bernoulli trials, simulation of random processes with Scilab...).

### Scilab is not naive

Updated 12/2010

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. In each example, we show that the naive algorithm is not sufficiently accurate, while Scilab implementation is much more robust.

### Optimization in Scilab

07/2010

This document presents all existing and non-existing optimization features in Scilab (examples of nonlinear optimization, available algorithms to solve quadratic problems, non-linear least squares problems, semidefinite programming, genetic algorithms, simulated annealing and linear matrix inequalities...)

## And also:

Our partner, Openeering publishes lots of turorials on Scilab and Xcos.
Do not hesitate to have a look on http://www.openeering.com/scilab_tutorials