CG
class CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)
Bases: qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer
Conjugate Gradient optimizer.
CG is an algorithm for the numerical solution of systems of linear equations whose matrices are symmetric and positive-definite. It is an iterative algorithm in that it uses an initial guess to generate a sequence of improving approximate solutions for a problem, in which each approximation is derived from the previous ones. It is often used to solve unconstrained optimization problems, such as energy minimization.
Uses scipy.optimize.minimize CG. For further detail, please refer to https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
Parameters
- maxiter (
int) – Maximum number of iterations to perform. - disp (
bool) – Set to True to print convergence messages. - gtol (
float) – Gradient norm must be less than gtol before successful termination. - tol (
Optional[float]) – Tolerance for termination. - eps (
float) – If jac is approximated, use this value for the step size. - options (
Optional[dict]) – A dictionary of solver options. - max_evals_grouped (
int) – Max number of default gradient evaluations performed simultaneously. - kwargs – additional kwargs for scipy.optimize.minimize.
Methods
get_support_level
CG.get_support_level()
Return support level dictionary
gradient_num_diff
static CG.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
CG.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
CG.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
CG.print_options()
Print algorithm-specific options.
set_max_evals_grouped
CG.set_max_evals_grouped(limit)
Set max evals grouped
set_options
CG.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 CG.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]