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, and the miscellaneous methods: Grid search, Simplex, and Levenberg-Marquardt.
Release Notes: This release added Python 3 support and the logarithmic barrier augmented function constraint algorithm. All of the package, module, class, function, and method docstrings have been updated to Epydoc format, improving the online documentation. A few bugs have been eliminated, and the printouts have been regularised.
Release Tags: major feature release
Tags: Software Development, Libraries, Python Modules, Scientific/Engineering