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qiskit.aqua.components.optimizers.TNC

class TNC(maxiter=100, disp=False, accuracy=0, ftol=- 1, xtol=- 1, gtol=- 1, tol=None, eps=1e-08)

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Truncated Newton (TNC) optimizer.

TNC uses a truncated Newton algorithm to minimize a function with variables subject to bounds. This algorithm uses gradient information; it is also called Newton Conjugate-Gradient. It differs from the CG method as it wraps a C implementation and allows each variable to be given upper and lower bounds.

Uses scipy.optimize.minimize TNC 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 function evaluation.
  • disp (bool) – Set to True to print convergence messages.
  • accuracy (float) – Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
  • ftol (float) – Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.
  • xtol (float) – Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1.
  • gtol (float) – Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1.
  • tol (Optional[float]) – Tolerance for termination.
  • eps (float) – Step size used for numerical approximation of the Jacobian.

__init__

__init__(maxiter=100, disp=False, accuracy=0, ftol=- 1, xtol=- 1, gtol=- 1, tol=None, eps=1e-08)

Parameters

  • maxiter (int) – Maximum number of function evaluation.
  • disp (bool) – Set to True to print convergence messages.
  • accuracy (float) – Relative precision for finite difference calculations. If <= machine_precision, set to sqrt(machine_precision). Defaults to 0.
  • ftol (float) – Precision goal for the value of f in the stopping criterion. If ftol < 0.0, ftol is set to 0.0 defaults to -1.
  • xtol (float) – Precision goal for the value of x in the stopping criterion (after applying x scaling factors). If xtol < 0.0, xtol is set to sqrt(machine_precision). Defaults to -1.
  • gtol (float) – Precision goal for the value of the projected gradient in the stopping criterion (after applying x scaling factors). If gtol < 0.0, gtol is set to 1e-2 * sqrt(accuracy). Setting it to 0.0 is not recommended. Defaults to -1.
  • tol (Optional[float]) – Tolerance for termination.
  • eps (float) – Step size used for numerical approximation of the Jacobian.

Methods

__init__([maxiter, disp, accuracy, ftol, …])type maxiterint
get_support_level()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_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.
wrap_function(function, args)Wrap the function to implicitly inject the args at the call of the function.

Attributes

bounds_support_levelReturns bounds support level
gradient_support_levelReturns gradient support level
initial_point_support_levelReturns initial point support level
is_bounds_ignoredReturns is bounds ignored
is_bounds_requiredReturns is bounds required
is_bounds_supportedReturns is bounds supported
is_gradient_ignoredReturns is gradient ignored
is_gradient_requiredReturns is gradient required
is_gradient_supportedReturns is gradient supported
is_initial_point_ignoredReturns is initial point ignored
is_initial_point_requiredReturns is initial point required
is_initial_point_supportedReturns is initial point supported
settingReturn setting

bounds_support_level

Returns bounds support level

get_support_level

get_support_level()

return support level dictionary

gradient_num_diff

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.

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

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

optimize

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()

Print algorithm-specific options.

set_max_evals_grouped

set_max_evals_grouped(limit)

Set max evals grouped

set_options

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.

setting

Return setting

wrap_function

static 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

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