NELDER_MEAD¶

class NELDER_MEAD(maxiter=None, maxfev=1000, disp=False, xatol=0.0001, tol=None, adaptive=False, options=None, **kwargs)[código fonte]¶

Bases: SciPyOptimizer

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

Parâmetros

maxiter (int | None) – 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 (float | None) – Tolerance for termination.
adaptive (bool) – Adapt algorithm parameters to dimensionality of problem.
options (dict | None) – A dictionary of solver options.
kwargs – additional kwargs for scipy.optimize.minimize.

Methods

`get_support_level`	Return support level dictionary
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`minimize`	Minimize the scalar function.
`print_options`	Print algorithm-specific options.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	Wrap the function to implicitly inject the args at the call of the function.

Attributes

bounds_support_level¶: Returns bounds support level

gradient_support_level¶: Returns gradient support level

initial_point_support_level¶: Returns initial point support level

is_bounds_ignored¶: Returns is bounds ignored

is_bounds_required¶: Returns is bounds required

is_bounds_supported¶: Returns is bounds supported

is_gradient_ignored¶: Returns is gradient ignored

is_gradient_required¶: Returns is gradient required

is_gradient_supported¶: Returns is gradient supported

is_initial_point_ignored¶: Returns is initial point ignored

is_initial_point_required¶: Returns is initial point required

is_initial_point_supported¶: Returns is initial point supported

setting¶: Return setting

settings¶