Source code for pycif.plugins.minimizers.m1qn3.minimize

import numpy as np


from logging import info




def minimize(self, finit, gradinit, chi0, **kwargs):
    """Run the M1QN3 L-BFGS minimisation and return the optimal iterate.

    Args:
        self (Plugin): M1QN3 minimizer plugin instance.
        finit (float): initial cost function value :math:`J(\chi_0)`.
        gradinit (np.ndarray): initial gradient :math:`\\nabla J(\chi_0)`,
            shape ``(n,)``.
        chi0 (np.ndarray): initial iterate :math:`\chi_0`, shape ``(n,)``.
        **kwargs: forwarded to the simulator.

    Returns:
        np.ndarray: optimal iterate :math:`\chi_\mathrm{opt}`, shape ``(n,)``.
    """

    # Initializing options (and filling missing values with default)
    self = self.check_options(chi0, finit, **kwargs)

    # Running M1QN3
    xopt, fopt, gradopt, niter, nsim, epsg, mode = self.m1qn3(
        finit, gradinit, chi0, **kwargs
    )

    # Final verbose and output
    towrite = """
        M1QN3:
            output mode is {}
            number of iterations: {}
            number of simulations: {}
            realized relative precision on g: {}
        """.format(
        mode, niter, nsim, epsg
    )

    info(towrite)

    r1 = np.sqrt(np.dot(xopt, xopt))
    r2 = np.sqrt(np.dot(gradopt, gradopt))

    info("norm of x = " + str(r1))
    info("f         = " + str(fopt))
    info("norm of g = " + str(r2))

    return xopt