[Python-modules-team] Bug#752056: RFP: python-minfx -- large collection of standard minimisation algorithms

[Rogério Brito]

Package: wnpp
Severity: wishlist

* Package name    : python-minfx
  Version         : 1.0.7
  Upstream Author : Edward d'Auvergne <edward at 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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