- class qiskit.algorithms.optimizers.NELDER_MEAD(maxiter=None, maxfev=1000, disp=False, xatol=0.0001, tol=None, adaptive=False, options=None, **kwargs)#
The Nelder-Mead algorithm performs unconstrained optimization; it ignores bounds or constraints. It is used to find the minimum or maximum of an objective function in a multidimensional space. It is based on the Simplex algorithm. Nelder-Mead is robust in many applications, especially when the first and second derivatives of the objective function are not known.
However, if the numerical computation of the derivatives can be trusted to be accurate, other algorithms using the first and/or second derivatives information might be preferred to Nelder-Mead for their better performance in the general case, especially in consideration of the fact that the Nelder–Mead technique is a heuristic search method that can converge to non-stationary points.
Uses scipy.optimize.minimize Nelder-Mead. For further detail, please refer to See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
maxiter (int | None) -- Maximum allowed number of iterations. If both maxiter and maxfev are set, minimization will stop at the first reached.
maxfev (int) -- Maximum allowed number of function evaluations. If both maxiter and maxfev are set, minimization will stop at the first reached.
disp (bool) -- Set to True to print convergence messages.
xatol (float) -- Absolute error in xopt between iterations that is acceptable for convergence.
tol (float | None) -- Tolerance for termination.
adaptive (bool) -- Adapt algorithm parameters to dimensionality of problem.
options (dict | None) -- A dictionary of solver options.
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.
the gradient computed
- Return type:
- minimize(fun, x0, jac=None, bounds=None)#
Minimize the scalar function.
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
The result of the optimization, containing e.g. the result as attribute
- Return type:
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) -- options, given as name=value.