SLSQP

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

GitHub

Bases: qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer

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.
options (Optional[dict]) – A dictionary of solver options.
max_evals_grouped (int) – Max number of default gradient evaluations performed simultaneously.
kwargs – additional kwargs for scipy.optimize.minimize.

Methods

get_support_level

SLSQP.get_support_level()

Return support level dictionary

gradient_num_diff

static SLSQP.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters

x_center (ndarray) – point around which we compute the gradient
f (func) – the function of which the gradient is to be computed.
epsilon (float) – the epsilon used in the numeric differentiation.
max_evals_grouped (int) – max evals grouped

Returns

the gradient computed

Return type

grad

minimize

SLSQP.minimize(fun, x0, jac=None, bounds=None)

Minimize the scalar function.

Parameters

fun (Callable[[Union[float, ndarray]], float]) – The scalar function to minimize.
x0 (Union[float, ndarray]) – The initial point for the minimization.
jac (Optional[Callable[[Union[float, ndarray]], Union[float, ndarray]]]) – The gradient of the scalar function fun.
bounds (Optional[List[Tuple[float, float]]]) – Bounds for the variables of fun. This argument might be ignored if the optimizer does not support bounds.

Return type

OptimizerResult

Returns

The result of the optimization, containing e.g. the result as attribute x.

optimize

SLSQP.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)

Perform optimization.

Parameters

num_vars (int) – Number of parameters to be optimized.
objective_function (callable) – A function that computes the objective function.
gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
initial_point (numpy.ndarray[float]) – Initial point.

Returns

point, value, nfev

point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Raises

ValueError – invalid input

print_options

SLSQP.print_options()

Print algorithm-specific options.

set_max_evals_grouped

SLSQP.set_max_evals_grouped(limit)

Set max evals grouped

set_options

SLSQP.set_options(**kwargs)

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters

kwargs (dict) – options, given as name=value.

wrap_function

static SLSQP.wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parameters

function (func) – the target function
args (tuple) – the args to be injected

Returns

wrapper

Return type

function_wrapper

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

Return type

Dict[str, Any]

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