qiskit.algorithms.optimizers.CG(maxiter=20, disp=False, gtol=1e-05, tol=None, eps=1.4901161193847656e-08, options=None, max_evals_grouped=1, **kwargs)
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 (opens in a new tab)
- maxiter (int (opens in a new tab)) – Maximum number of iterations to perform.
- disp (bool (opens in a new tab)) – Set to True to print convergence messages.
- gtol (float (opens in a new tab)) – Gradient norm must be less than gtol before successful termination.
- tol (float (opens in a new tab) | None) – Tolerance for termination.
- eps (float (opens in a new tab)) – If jac is approximated, use this value for the step size.
- options (dict (opens in a new tab) | None) – A dictionary of solver options.
- max_evals_grouped (int (opens in a new tab)) – Max number of default gradient evaluations performed simultaneously.
- kwargs – additional kwargs for scipy.optimize.minimize.
Returns bounds support level
Returns gradient support level
Returns initial point support level
Returns is bounds ignored
Returns is bounds required
Returns is bounds supported
Returns is gradient ignored
Returns is gradient required
Returns is gradient supported
Returns is initial point ignored
Returns is initial point required
Returns is initial point supported
Return support level dictionary
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.
- x_center (ndarray) – point around which we compute the gradient
- f (func) – the function of which the gradient is to be computed.
- epsilon (float (opens in a new tab)) – the epsilon used in the numeric differentiation.
- max_evals_grouped (int (opens in a new tab)) – max evals grouped, defaults to 1 (i.e. no batching).
the gradient computed
minimize(fun, x0, jac=None, bounds=None)
Minimize the scalar function.
- fun (Callable[[POINT], float (opens in a new tab)]) – The scalar function to minimize.
- x0 (POINT) – The initial point for the minimization.
- jac (Callable[[POINT], POINT] | None) – The gradient of the scalar function
- bounds (list (opens in a new tab)[tuple (opens in a new tab)[float (opens in a new tab), float (opens in a new tab)]] | None) – Bounds for the variables of
fun. This argument might be ignored if the optimizer does not support bounds.
The result of the optimization, containing e.g. the result as attribute
Print algorithm-specific options.
Set max evals grouped
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.
kwargs (dict (opens in a new tab)) – options, given as name=value.
static wrap_function(function, args)
Wrap the function to implicitly inject the args at the call of the function.
- function (func) – the target function
- args (tuple (opens in a new tab)) – the args to be injected