ampgo

SeparableModelResult.ampgo(params=None, **kws)

Find the global minimum of a multivariate function using AMPGO.

AMPGO stands for ‘Adaptive Memory Programming for Global Optimization’ and is an efficient algorithm to find the global minimum.

Parameters:

• params (Parameters, optional) – Contains the Parameters for the model. If None, then the Parameters used to initialize the Minimizer object are used.
• **kws (dict, optional) –

  Minimizer options to pass to the ampgo algorithm, the options are listed below:

  local: str (default is 'L-BFGS-B')
      Name of the local minimization method. Valid options are:
      - 'L-BFGS-B'
      - 'Nelder-Mead'
      - 'Powell'
      - 'TNC'
      - 'SLSQP'
  local_opts: dict (default is None)
      Options to pass to the local minimizer.
  maxfunevals: int (default is None)
      Maximum number of function evaluations. If None, the optimization will stop
      after `totaliter` number of iterations.
  totaliter: int (default is 20)
      Maximum number of global iterations.
  maxiter: int (default is 5)
      Maximum number of `Tabu Tunneling` iterations during each global iteration.
  glbtol: float (default is 1e-5)
      Tolerance whether or not to accept a solution after a tunneling phase.
  eps1: float (default is 0.02)
      Constant used to define an aspiration value for the objective function during
      the Tunneling phase.
  eps2: float (default is 0.1)
      Perturbation factor used to move away from the latest local minimum at the
      start of a Tunneling phase.
  tabulistsize: int (default is 5)
      Size of the (circular) tabu search list.
  tabustrategy: str (default is 'farthest')
      Strategy to use when the size of the tabu list exceeds `tabulistsize`. It
      can be 'oldest' to drop the oldest point from the tabu list or 'farthest'
      to drop the element farthest from the last local minimum found.
  disp: bool (default is False)
      Set to True to print convergence messages.
  

Returns:

Object containing the parameters from the ampgo method, with fit parameters, statistics and such. The return values (x0, fval, eval, msg, tunnel) are stored as ampgo_<parname> attributes.

Return type:

MinimizerResult

New in version 0.9.10.

Notes

The Python implementation was written by Andrea Gavana in 2014 (http://infinity77.net/global_optimization/index.html).

The details of the AMPGO algorithm are described in the paper “Adaptive Memory Programming for Constrained Global Optimization” located here:

http://leeds-faculty.colorado.edu/glover/fred%20pubs/416%20-%20AMP%20(TS)%20for%20Constrained%20Global%20Opt%20w%20Lasdon%20et%20al%20.pdf