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專案描述

The minfx project is a Python package for numerical optimization. It provides a large collection of standard minimization algorithms, including the line search methods (steepest descent, back-and-forth coordinate descent, quasi-Newton BFGS, Newton, and Newton-CG), the trust-region methods (Cauchy point, dogleg, CG-Steihaug, and exact trust region), the conjugate gradient methods (Fletcher-Reeves, Polak-Ribiere, Polak-Ribiere +, and Hestenes-Stiefel), the miscellaneous methods (Grid search, Simplex, and Levenberg-Marquardt), and the augmented function constraint algorithms (logarithmic barrier and method of multipliers).

System Requirements

System requirement is not defined
Information regarding Project Releases and Project Resources. Note that the information here is a quote from Freecode.com page, and the downloads themselves may not be hosted on OSDN.

2011-01-27 18:04
1.0.3

This release introduces a preliminary simulated annealing package based on scipy, and heavily modifies and improves the grid search algorithm.
標籤: major feature addition

2009-07-31 20:54
1.0.2

Support for Python 2.6 has been added and a rare bug in the backtracking step selection subalgorithm has been removed.
標籤: Stable, minor bug fix

2008-09-28 20:48
1.0.1

This release involves the inevitable switch from
Numeric python to numpy, a few improvements in how
missing gradients and models with no parameters
are handled, and a switch from GPLv2 to GPLv3.
標籤: Minor feature enhancements

2007-12-09 16:47
1.0.0

This code originated as part of the relax project within the 'minimise/' directory. Minfx is complete, very stable, well tested, and has been spun off as its own project for the benefit of other scientific, analytical, or numerical projects.
標籤: Initial freshmeat announcement

Project Resources