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minfx 1.0.7
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).

Release Notes: This is a minor feature release which tunes the log barrier iterative constraint algorithm for better constrained optimisation. The constrained optimisation space is now much less perturbed by the algorithm.

Screenshot

Release Tags: minor feature release

Tags: Software Development, Libraries, Scientific/Engineering, Python package

Licenses: GPLv3+


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