Package: wnpp
Severity: wishlist
* Package name : python-minfx
Version : 1.0.7
Upstream Author : Edward d'Auvergne <edw...@nmr-relax.com>
* URL : https://gna.org/projects/minfx/
* License : GPLv3+
Programming Lang: Python
Description : large collection of standard minimisation algorithms
The minfx project is a Python package for numerical optimisation, being a
large collection of standard minimisation algorithms. The name minfx is
simply a shortening of the mathematical expression min f(x).
Local optimization algorithms
Line search methods
* Steepest descent.
* Back-and-forth coordinate descent.
* Quasi-Newton BFGS.
* Newton.
* Newton-CG.
Trust-region methods
* Cauchy point.
* Dogleg.
* CG-Steihaug.
* Exact trust region.
Conjugate gradient methods
* Fletcher-Reeves.
* Polak-Ribiere.
* Polak-Ribiere +.
* Hestenes-Stiefel.
Miscellaneous
* Grid search.
* Simplex.
* Levenberg-Marquardt.
Auxiliary algorithms
Step selection subalgorithms
* Backtracking line search.
* Nocedal and Wright interpolation based line search.
* Nocedal and Wright line search for the Wolfe conditions.
* More and Thuente line search.
Hessian modifications
* Eigenvalue modification.
* Cholesky with added multiple of the identity.
* Gill, Murray, and Wright modified Cholesky algorithm (GMW81).
* The Schnabel and Eskow 1999 algorithm (SE99).
Constraint algorithms
All minimization methods can be constrained by the Method of Multipliers
(also known as the Augmented Lagrangian).
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