I’m pleased to announce the first release of one of my projects. This scikits is based on a generic framework that can support unconstrained cost function minimization. It is based on a separation principle and is also completely object oriented.

Several optimizers are available:

  • Nelder-Mead or simplex minimization
  • Unconstrained gradient-based minimization

The usual criterias can be used:

  • Iteration limit
  • Parameter change (relative and absolute)
  • Cost function changer (relative and absolute)
  • Composite criterion generation (AND/OR)

Different direction searches are available:

  • Gradient
  • Several conjugate-gradient (Fletcher-Reeves, …)
  • Decorators for selecting part of the gradient
  • Marquardt step

Finally several line searches (1D minimization) were coded:

  • Fibonacci and gold number methods (exact line searches)
  • Wolfe-Powell soft and strong rules
  • Goldstein line search
  • Cubic interpolation

Additional helper classes can be used:

  • Finite difference differentation (central and forward)
  • Quadratic cost (for least square estimation)
  • Levenberg-Marquardt approximation for least square estimation

Although it is the 0.1 version, the code is quite stable and is used in the learn scikit.

The package can be easy-installed or can be found on PyPI.

Several tutorials are available or will be available on the future at the following locations:

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