This booklet, realized with the support of INRIA and co-written by the Scilab Consortium and teachers is a practical introduction to Scilab with examples based on French high school mathematics programs.
Introduction to discrete probabilities with Scilab
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...).
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.
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...)
