SLSQP

class SLSQP(maxiter=100, disp=False, ftol=1e-06, tol=None, eps=1.4901161193847656e-08)

Sequential Least SQuares Programming optimizer.

SLSQP minimizes a function of several variables with any combination of bounds, equality and inequality constraints. The method wraps the SLSQP Optimization subroutine originally implemented by Dieter Kraft.

SLSQP is ideal for mathematical problems for which the objective function and the constraints are twice continuously differentiable. Note that the wrapper handles infinite values in bounds by converting them into large floating values.

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

Parameters

• maxiter (int) – Maximum number of iterations.

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

• ftol (float) – Precision goal for the value of f in the stopping criterion.

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

• eps (float) – Step size used for numerical approximation of the Jacobian.

Attributes

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

Methods

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