New Scientific Features in 2008

Introduction

The goal of this page is to summarize the new or scientific features of Scilab and its environment during the year 2008.

Other Scilab events during 2008 :

In summary, the following is a list of new or updated scientific modules during the year 2008 :

UMFPACK in Scilab v5.0

The UMFPACK module provides direct algorithms (as opposed to iterative algorithms) for the management of sparse matrices. It is made of two main components :

This module was introduced into Scilab v5.0 thanks to the work of Bruno Pincon.

The following is a list of the functions provided in the umfpack module.

This module was first described in the "Changes" file for Scilab v5.0 :

http://www.scilab.org/communities/developer_zone/scilab_versions/old_versions/scilab_5.0/changes_4-5/Sparse-LU-factorization

Genetic Algorithms in Scilab v5.0

Genetic algorithms are search algorithms based on the mechanics on natural selection and natural genetics. Genetic algorithms have been introduced in Scilab v5 thanks to a work by Yann Collette. The solver is made of Scilab macros, which enables a high-level programming model for this optimization solver. The problems solved by the current genetic algorithms in Scilab are the following :

The GA macros are based on the "parameters" Scilab module for the management of the (many) optional parameters.

The following is a list of the functions provided by the Genetic Algorithms module.

The previous solvers are making use of the following support functions, which allow to configure the behaviour of the algorithm.

More details on this module are available in the chapter 5 or "Optimization in Scilab", Michael Baudin, Vincent Couvert and Serge Steer, 2010, which is available at :

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

or on the Scilab Forge :

http://forge.scilab.org/index.php/p/docoptimscilab/downloads/

This module was first described in the "Changes" file for Scilab v5.0 :

http://www.scilab.org/communities/developer_zone/scilab_versions/old_versions/scilab_5.0/changes_4-5/Genetic-Algorithms

Simulated annealing in Scilab v5.0

Simulated annealing (SA) is a generic probabilistic meta-algorithm for the global optimization problem, namely locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). Genetic algorithms have been introduced in Scilab v5 thanks to the work by Yann Collette.

The current Simulated Annealing solver aims at nding the solution of bound constrained optimization problems with one objective function.

The solver is made of Scilab macros, which enables a high-level programming model for this optimization solver. The GA macros are based on the "parameters"Scilab module for the management of the (many) optional parameters.

The main function provided by the Simulated Annealing module is the following.

The previous function makes use of the following functions which allow to configure the behaviour of the solver.

More details on this module are available in the chapter 6 or "Optimization in Scilab", Michael Baudin, Vincent Couvert and Serge Steer, 2010, which is available at :

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

or on the Scilab Forge :

http://forge.scilab.org/index.php/p/docoptimscilab/downloads/

This module was first described in the "Changes" file for Scilab v5.0 :

http://www.scilab.org/communities/developer_zone/scilab_versions/old_versions/scilab_5.0/changes_4-5/Simulated-Annealing

Metanet updated in Scilab v5.0

Data structures have been reorganized and made more flexible (user can define and handle its own data fields for nodes and edges) New functions:

These informations were extracted from :

http://www.scilab.org/communities/developer_zone/scilab_versions/old_versions/scilab_5.0/changes_4-5/Metanet-graph-and-network-toolbox

This module has been updated and packaged under ATOMS the 29th of July 2010 :

http://atoms.scilab.org/toolboxes/metanet/0.3

The sources are managed on the Scilab Forge :

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

In order to install this module, use the following statement :

atomsInstall("metanet")

The following figure was produced with the mesh2d function, which performs a triangulation of n points in the plane.

mesh2d.example1.png

FFTW in Scilab v5.0

FFTW (Fastest Fourier Transform in the West) features added in Scilab.

"FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST)."

http://www.fftw.org/

The FFTW package was developed at MIT by Matteo Frigo and Steven G. Johnson.

One of the most interesting features of FFTW is its speed. Indeed, FFTW supports SSE/SSE2/3dNow! CPU instruction sets, which improve its speed. See the benchmarks at: http://www.fftw.org/benchfft/

The features included in Scilab are the following:

These informations are extracted from :

http://www.scilab.org/communities/developer_zone/scilab_versions/old_versions/scilab_5.0/changes_4-5/Signal-processing

Bibliography

[1] "Optimization in Scilab", Michael Baudin, Vincent Couvert and Serge Steer, 2010, http://forge.scilab.org/index.php/p/docoptimscilab/downloads/

[2] "Optimization with scilab, present and future", Michael Baudin and Serge Steer. Proceedings Of 2009 International Workshop On Open-Source Software For Scienti c Computation (Ossc-2009).

public: New Scientific Features in 2008 (last edited 2019-03-12 11:44:57 by astlambert-681-1-52-129)