Source code for qiskit_algorithms.optimizers.nelder_mead

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"""Nelder-Mead optimizer."""
from __future__ import annotations

from .scipy_optimizer import SciPyOptimizer

[docs]class NELDER_MEAD(SciPyOptimizer): # pylint: disable=invalid-name """ Nelder-Mead optimizer. 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 """ _OPTIONS = ["maxiter", "maxfev", "disp", "xatol", "adaptive"] # pylint: disable=unused-argument def __init__( self, maxiter: int | None = None, maxfev: int = 1000, disp: bool = False, xatol: float = 0.0001, tol: float | None = None, adaptive: bool = False, options: dict | None = None, **kwargs, ) -> None: """ Args: maxiter: Maximum allowed number of iterations. If both maxiter and maxfev are set, minimization will stop at the first reached. maxfev: Maximum allowed number of function evaluations. If both maxiter and maxfev are set, minimization will stop at the first reached. disp: Set to True to print convergence messages. xatol: Absolute error in xopt between iterations that is acceptable for convergence. tol: Tolerance for termination. adaptive: Adapt algorithm parameters to dimensionality of problem. options: A dictionary of solver options. kwargs: additional kwargs for scipy.optimize.minimize. """ if options is None: options = {} for k, v in list(locals().items()): if k in self._OPTIONS: options[k] = v super().__init__(method="Nelder-Mead", options=options, tol=tol, **kwargs)