Difference between revisions of "Minfx"
(Submission of the minfx project.)
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Revision as of 18:30, 8 March 2013
The minfx project is a Python package for numerical optimisation, being a large collection of standard minimisation algorithms. This includes the line search methods: steepest descent, back-and-forth coordinate descent, quasi-Newton BFGS, Newton, Newton-CG; the trust-region methods: Cauchy point, dogleg, CG-Steihaug, exact trust region; the conjugate gradient methods: Fletcher-Reeves, Polak-Ribiere, Polak-Ribiere +, Hestenes-Stiefel; the miscellaneous methods: Grid search, Simplex, Levenberg-Marquardt; and the augmented function constraint algorithms: logarithmic barrier and method of multipliers (or augmented Lagrangian method).
DocumentationThe API documentation with full descriptions of all the optimisation algorithms is available at http://home.gna.org/minfx/.
released on 9 March 2013
|License||Verified by||Verified on||Notes|
|GNU GPLv3+||Jgay||8 March 2013|
Leaders and contributors
|Edward d'Auvergne||Project Admin|
Resources and communication
|Developer||Mailing List Info/Archive||https://mail.gna.org/public/minfx-commits/|
|User||Mailing List Subscribe||http://gna.org/mail/?group=minfx|
|Developer||VCS Repository Webview||http://svn.gna.org/viewcvs/minfx/|
|User||Mailing List Info/Archive||https://mail.gna.org/public/minfx-announce/|
|Developer||Mailing List Info/Archive||https://mail.gna.org/public/minfx-devel/|
|Required to use||Python|
|Required to use||numpy|
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