# qiskit.algorithms.optimizers.SLSQP¶

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

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

Parâmetros
• 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.

__init__(maxiter=100, disp=False, ftol=1e-06, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)[código fonte]
Parâmetros
• 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

 __init__([maxiter, disp, ftol, tol, eps, …]) type maxiter int Return support level dictionary gradient_num_diff(x_center, f, epsilon[, …]) We compute the gradient with the numeric differentiation in the parallel way, around the point x_center. optimize(num_vars, objective_function[, …]) Perform optimization. Print algorithm-specific options. Set max evals grouped set_options(**kwargs) Sets or updates values in the options dictionary. wrap_function(function, args) 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 The optimizer settings in a dictionary format.
property bounds_support_level

Returns bounds support level

get_support_level()

Return support level dictionary

static 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.

Parâmetros
• 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

Retorna

Tipo de retorno

property gradient_support_level

Returns gradient support level

property initial_point_support_level

Returns initial point support level

property is_bounds_ignored

Returns is bounds ignored

property is_bounds_required

Returns is bounds required

property is_bounds_supported

Returns is bounds supported

property is_gradient_ignored

Returns is gradient ignored

property is_gradient_required

Returns is gradient required

property is_gradient_supported

Returns is gradient supported

property is_initial_point_ignored

Returns is initial point ignored

property is_initial_point_required

Returns is initial point required

property is_initial_point_supported

Returns is initial point supported

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

Perform optimization.

Parâmetros
• 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.

Retorna

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

Levanta

ValueError – invalid input

print_options()

Print algorithm-specific options.

set_max_evals_grouped(limit)

Set max evals grouped

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.

Parâmetros

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

property setting

Return setting

property settings

The optimizer settings in a dictionary format.

The settings can for instance be used for JSON-serialization (if all settings are serializable, which e.g. doesn’t hold per default for callables), such that the optimizer object can be reconstructed as

settings = optimizer.settings
# JSON serialize and send to another server
optimizer = OptimizerClass(**settings)

Tipo de retorno

Dict[str, Any]

static wrap_function(function, args)

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

Parâmetros
• function (func) – the target function

• args (tuple) – the args to be injected

Retorna

wrapper

Tipo de retorno

function_wrapper