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SNOBFIT

class SNOBFIT(maxiter=1000, maxfail=10, maxmp=None, verbose=False)

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Bases: qiskit.algorithms.optimizers.optimizer.Optimizer

Stable Noisy Optimization by Branch and FIT algorithm.

SnobFit is used for the optimization of derivative-free, noisy objective functions providing robust and fast solutions of problems with continuous variables varying within bound.

Uses skquant.opt installed with pip install scikit-quant. For further detail, please refer to https://github.com/scikit-quant/scikit-quant and https://qat4chem.lbl.gov/software.

Parameters

  • maxiter (int) – Maximum number of function evaluations.
  • maxmp (Optional[int]) – Maximum number of model points requested for the local fit. Default = 2 * number of parameters + 6 set to this value when None.
  • maxfail (int) – Maximum number of failures to improve the solution. Stops the algorithm after maxfail is reached.
  • verbose (bool) – Provide verbose (debugging) output.

Raises

MissingOptionalLibraryError – scikit-quant or SQSnobFit not installed


Methods

get_support_level

SNOBFIT.get_support_level()

Returns support level dictionary.

gradient_num_diff

static SNOBFIT.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

SNOBFIT.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

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

Runs the optimization.

SNOBFIT.print_options()

Print algorithm-specific options.

set_max_evals_grouped

SNOBFIT.set_max_evals_grouped(limit)

Set max evals grouped

set_options

SNOBFIT.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 SNOBFIT.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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