POWELL

class POWELL(maxiter=None, maxfev=1000, disp=False, xtol=0.0001, tol=None)

Powell optimizer.

The Powell algorithm performs unconstrained optimization; it ignores bounds or constraints. Powell is a conjugate direction method: it performs sequential one-dimensional minimization along each directional vector, which is updated at each iteration of the main minimization loop. The function being minimized need not be differentiable, and no derivatives are taken.

Uses scipy.optimize.minimize Powell. 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.

• xtol (float) – Relative error in solution xopt acceptable for convergence.

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

Attributes

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

Methods

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