NELDER_MEAD

class NELDER_MEAD(maxiter=None, maxfev=1000, disp=False, xatol=0.0001, tol=None, adaptive=False)

Nelder-Mead optimizer.

The Nelder-Mead algorithm performs unconstrained optimization; it ignores bounds or constraints. It is used to find the minimum or maximum of an objective function in a multidimensional space. It is based on the Simplex algorithm. Nelder-Mead is robust in many applications, especially when the first and second derivatives of the objective function are not known.

However, if the numerical computation of the derivatives can be trusted to be accurate, other algorithms using the first and/or second derivatives information might be preferred to Nelder-Mead for their better performance in the general case, especially in consideration of the fact that the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points.

Uses scipy.optimize.minimize Nelder-Mead. For further detail, please refer to See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

Parameters

• maxiter (Optional[int]) – Maximum allowed number of iterations. If both maxiter and maxfev are set, minimization will stop at the first reached.

• maxfev (int) – Maximum allowed number of function evaluations. If both maxiter and maxfev are set, minimization will stop at the first reached.

• disp (bool) – Set to True to print convergence messages.

• xatol (float) – Absolute error in xopt between iterations that is acceptable for convergence.

• tol (Optional[float]) – Tolerance for termination.

• adaptive (bool) – Adapt algorithm parameters to dimensionality of problem.

Attributes

NELDER_MEAD.bounds_support_level	Returns bounds support level
NELDER_MEAD.gradient_support_level	Returns gradient support level
NELDER_MEAD.initial_point_support_level	Returns initial point support level
NELDER_MEAD.is_bounds_ignored	Returns is bounds ignored
NELDER_MEAD.is_bounds_required	Returns is bounds required
NELDER_MEAD.is_bounds_supported	Returns is bounds supported
NELDER_MEAD.is_gradient_ignored	Returns is gradient ignored
NELDER_MEAD.is_gradient_required	Returns is gradient required
NELDER_MEAD.is_gradient_supported	Returns is gradient supported
NELDER_MEAD.is_initial_point_ignored	Returns is initial point ignored
NELDER_MEAD.is_initial_point_required	Returns is initial point required
NELDER_MEAD.is_initial_point_supported	Returns is initial point supported
NELDER_MEAD.setting	Return setting

Methods

NELDER_MEAD.get_support_level()	Return support level dictionary
NELDER_MEAD.gradient_num_diff(x_center, f, …)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
NELDER_MEAD.optimize(num_vars, …[, …])	Perform optimization.
NELDER_MEAD.print_options()	Print algorithm-specific options.
NELDER_MEAD.set_max_evals_grouped(limit)	Set max evals grouped
NELDER_MEAD.set_options(**kwargs)	Sets or updates values in the options dictionary.
NELDER_MEAD.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.