ISRES
class qiskit.algorithms.optimizers.ISRES(max_evals=1000)
Bases: NLoptOptimizer
Improved Stochastic Ranking Evolution Strategy optimizer.
Improved Stochastic Ranking Evolution Strategy (ISRES) is an algorithm for non-linearly constrained global optimization. It has heuristics to escape local optima, even though convergence to a global optima is not guaranteed. The evolution strategy is based on a combination of a mutation rule and differential variation. The fitness ranking is simply via the objective function for problems without nonlinear constraints. When nonlinear constraints are included, the stochastic ranking proposed by Runarsson and Yao is employed. This method supports arbitrary nonlinear inequality and equality constraints, in addition to the bound constraints.
NLopt global optimizer, derivative-free. For further detail, please refer to http://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/#isres-improved-stochastic-ranking-evolution-strategy
Parameters
max_evals (int) – Maximum allowed number of function evaluations.
Raises
MissingOptionalLibraryError – NLopt library not installed.
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
Methods
get_nlopt_optimizer
get_nlopt_optimizer()
Return NLopt optimizer type
Return type
NLoptOptimizerType
get_support_level
get_support_level()
return support level dictionary
gradient_num_diff
static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=None)
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, defaults to 1 (i.e. no batching).
Returns
the gradient computed
Return type
grad
minimize
minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
Parameters
- fun (Callable[[POINT], float]) – The scalar function to minimize.
- x0 (POINT) – The initial point for the minimization.
- jac (Callable[[POINT], POINT] | None) – The gradient of the scalar function
fun. - bounds (list[tuple[float, float]] | None) – Bounds for the variables of
fun. This argument might be ignored if the optimizer does not support bounds.
Returns
The result of the optimization, containing e.g. the result as attribute x.
Return type
print_options
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.
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