Projects / minfx

minfx

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).

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Recent releases

  •  17 Jun 2014 15:01

    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.

    •  24 Apr 2014 12:07

      Release Notes: This is a minor feature release that adds support for linear and user supplied constraints in the point array grid search.

      •  27 May 2013 08:58

        Release Notes: This is a minor feature release with improved documentation and support for sparseness in the grid search algorithm.

        •  09 Mar 2013 09:20

          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.

          •  27 Jan 2011 08:48

            Release Notes: This release introduces a preliminary simulated annealing package based on scipy, and heavily modifies and improves the grid search algorithm.

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