COBYLA
class COBYLA(maxiter=1000, disp=False, rhobeg=1.0, tol=None, options=None, **kwargs)
Bases: qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer
Constrained Optimization By Linear Approximation optimizer.
COBYLA is a numerical optimization method for constrained problems where the derivative of the objective function is not known.
Uses scipy.optimize.minimize COBYLA. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
Parameters
- maxiter (
int) – Maximum number of function evaluations. - disp (
bool) – Set to True to print convergence messages. - rhobeg (
float) – Reasonable initial changes to the variables. - tol (
Optional[float]) – Final accuracy in the optimization (not precisely guaranteed). This is a lower bound on the size of the trust region. - options (
Optional[dict]) – A dictionary of solver options. - kwargs – additional kwargs for scipy.optimize.minimize.
Methods
get_support_level
COBYLA.get_support_level()
Return support level dictionary
gradient_num_diff
static COBYLA.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
COBYLA.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
COBYLA.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
COBYLA.print_options()
Print algorithm-specific options.
set_max_evals_grouped
COBYLA.set_max_evals_grouped(limit)
Set max evals grouped
set_options
COBYLA.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 COBYLA.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]