DIRECT_L_RAND
class DIRECT_L_RAND(max_evals=1000)
Bases: qiskit.algorithms.optimizers.nlopts.nloptimizer.NLoptOptimizer
DIviding RECTangles Locally-biased Randomized optimizer.
DIRECT-L RAND is the “locally biased” variant with some randomization in near-tie decisions. See also DIRECT_L
NLopt global optimizer, derivative-free. For further detail, please refer to http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#direct-and-direct-l
Parameters
max_evals (int) – Maximum allowed number of function evaluations.
Raises
MissingOptionalLibraryError – NLopt library not installed.
Methods
get_nlopt_optimizer
DIRECT_L_RAND.get_nlopt_optimizer()
Return NLopt optimizer type
Return type
NLoptOptimizerType
get_support_level
DIRECT_L_RAND.get_support_level()
return support level dictionary
gradient_num_diff
static DIRECT_L_RAND.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
DIRECT_L_RAND.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 functionfun. - bounds (
Optional[List[Tuple[float,float]]]) – Bounds for the variables offun. 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
DIRECT_L_RAND.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
DIRECT_L_RAND.print_options()
Print algorithm-specific options.
set_max_evals_grouped
DIRECT_L_RAND.set_max_evals_grouped(limit)
Set max evals grouped
set_options
DIRECT_L_RAND.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 DIRECT_L_RAND.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