[Scilab-users] Scicolpack toolbox

[Stéphane Mottelet]

Hello,

With the help of Antoine we now have a Windows build. Moreover some 
glitches have been fixed (get rid of OpenMP and a failing load under 
macOS) and a new version 0.2 is now online on Atoms. To give it a try:

--> atomsInstall scicolpack

Best,

S.

Le 14/12/2020 à 16:50, Stéphane Mottelet a écrit :
> Hi all,
>
> A new toolbox has been uploaded at 
> https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/atoms.scilab.org/toolboxes/scicolpack:
>
> Scicolpack is is the Scilab interface to CSCsw/ColPack 
> (https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/github.com/CSCsw/ColPack), 
> a Graph Coloring Algorithm Package applied to efficient computation of 
> sparse Jacobian and Hessian.
>
> When you have to compute the Jacobian of a function f or the Hessian 
> of f knowing its gradient g, once you know its sparsity pattern, even 
> if you have derived it symbolically, it may still be faster to 
> estimate it by using the techniques which are used by Colpack. Roughly 
> speaking, this works by building a graph where each vertex is 
> associated to a column of the Jacobian or Hessian, and an edge 
> connects to vertices/columns if they are not structurally orthogonal, 
> i.e. have at least one non-zero term in a common row. Then a proper 
> coloring is done on this graph: at least, two adjacent vertices cannot 
> have the same color, but more properties of the coloring may be 
> expected. The coloring defines a partition of the columns under the 
> form of p subsets and the Jacobian (resp. Hessian) can be recovered 
> from only p evaluation of directional derivatives of f (resp. g). For 
> example, for a tridiagonal matrix the value of p is 3. In the Scilab 
> interface these directional derivatives are approximated by using 
> finite differences (the toolbox allows to compute them by using the 
> complex step technique up to machine precision).
>
> This toolbox can be an nice addon to SciIpopt toolbox 
> (https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/atoms.scilab.org/toolboxes/sci_ipopt) 
> where the Interior Point algorithm can be greatly accelerated when the 
> Hessian of the Lagrangian is sparse. When I have time I will update 
> the demo section of the module to add such an example.
>
> Don't hesitate to report successful uses, bugs or whishes. For the 
> moment the toolbox is available under OSX and Linux. Any help for a 
> Windows build is welcome !
>
> Best,
>
-- 
Stéphane Mottelet
Ingénieur de recherche
EA 4297 Transformations Intégrées de la Matière Renouvelable
Département Génie des Procédés Industriels
Sorbonne Universités - Université de Technologie de Compiègne
CS 60319, 60203 Compiègne cedex
Tel : +33(0)344234688
http://www.utc.fr/~mottelet