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ATOMS : Simplex optimization toolbox details
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Simplex optimization toolbox

This package contains the simplex optimization method
(6256 downloads for this version - 12748 downloads for all versions)
Details
Version
2.0.1
Author
Yann COLLETTE
Owner Organization
Home
Maintainer
Yann Collette
License
Creation Date
September 26, 2012
Source created on
Scilab 5.4.x
Binaries available on
Scilab 5.4.x:
Linux 32-bit Windows 32-bit Windows 64-bit MacOSX Linux 64-bit
Install command
--> atomsInstall("simplex")
Description
            This the simplex optimization method which performs optimization of non linear
function. This optimization method doesn't use the derivative of the objective
function.
There is 2 versions of the simplex:
- optim_nelder_mead: a classic script for optimization
- step_nelder_mead: the call to the objective function is done outside of the
script.
            
Files (6)
[27.74 kB]
Source code archive
makes it available for scilab 5.4
[54.30 kB]
Linux 32-bit binary for Scilab 5.4.x
Linux 32-bit
Automatically generated by the ATOMS compilation chain

[72.81 kB]
Windows 32-bit binary for Scilab 5.4.x
Windows 32-bit
Automatically generated by the ATOMS compilation chain

[72.85 kB]
Windows 64-bit binary for Scilab 5.4.x
Windows 64-bit
Automatically generated by the ATOMS compilation chain

[54.29 kB]
MacOSX binary for Scilab 5.4.x
MacOSX version
Automatically generated by the ATOMS compilation chain

[54.54 kB]
Linux 64-bit binary for Scilab 5.4.x
Linux 64-bit
Automatically generated by the ATOMS compilation chain

News (0)
Comments (1)     Leave a comment 
Comment from Michael Baudin -- February 27, 2016, 11:06:53 AM    
Hi,

This is a message for users searching an derivative-free optimization function. 
Scilab 5 has a fminsearch function with Nelder-Mead algorithm, based on its
"neldermead" 
module. The fminsearch function has full help pages and unit tests, with powerful 
options such as full exit status, full display control, an output function system (with 
a output function implementation), a plot function (with a default implementation), 
etc... 

This is why the "optim_nelder_mead" of the simplex module is very interesting for

understanding the algorithm in detail (e.g. for academic purposes), but is unnecessary 
for industrial practice. 

The "step_nelder_mead" function on the other hand allows to evaluate the
objective 
function outside Scilab, which is an interesting feature (although there are rare 
situations where this is required).

Best regards,

Michaël
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