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Revision as of 08:04, 9 March 2013 by Bugman (Talk | contribs)

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


The API documentation with full descriptions of all the optimisation algorithms is available at


LicenseVerified byVerified onNotes
GNU GPLv3+Jgay8 March 2013

Leaders and contributors

Edward d'Auvergne Project Admin

Resources and communication

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Software prerequisites

Kind Description
Required to use Python
Required to use numpy


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