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SimplexAlgorithm

Namespace: MATHLIB  ·  Library: Math Library

Python equivalent

scipy.optimize.fmin in MNE-Python.

#include <math/simplex_algorithm.h>

class UTILSLIB::SimplexAlgorithm

Header-only Nelder–Mead simplex minimiser templated on the scalar type and on the cost / report callables, implementing the Strategy pattern at zero runtime cost (the functors are inlined by the compiler).

Header-only Nelder–Mead simplex minimiser with templated cost and report functors.

Static Methods​

simplex_minimize(p, y, ftol, stol, func, max_eval, neval, report, report_func)​

mne_simplex_fit.c Refactored: mne_simplex_minimize

Minimization with the simplex algorithm. Modified from Numerical Recipes. Supports an optional absolute spatial tolerance (stol) to detect simplex collapse.

Parameters:

  • p : Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > & The initial simplex (npar+1 x npar). On return, row 0 is the best vertex.

  • y : Eigen::Matrix< T, Eigen::Dynamic, 1 > & Function values at the vertices.

  • ftol : T Relative convergence tolerance.

  • stol : T Absolute spatial convergence tolerance (0 to disable).

  • func : CostFunc && The cost function to be evaluated.

  • max_eval : int Maximum number of function evaluations.

  • neval : int & Number of function evaluations performed.

  • report : int How often to report (-1 = no reporting).

  • report_func : ReportFunc && The function to be called when reporting (may be nullptr).

Returns:

  • bool — True when minimization succeeded, false otherwise.

simplex_minimize(p, y, ftol, stol, func, max_eval, neval)​

Overload without report function (no reporting).

Authors of this file​