# Path: nlp/ls/linesearch.py
# class LineSearch(object):
# class ArmijoLineSearch(LineSearch):
# class ArmijoWolfeLineSearch(ArmijoLineSearch):
#     def __init__(self, linemodel, name="Generic Linesearch", **kwargs):
#     def linemodel(self):
#     def name(self):
#     def value(self):
#     def step(self):
#     def stepmin(self):
#     def trial_value(self):
#     def iterate(self):
#     def slope(self):
#     def check_slope(self, slope):
#     def __iter__(self):
#     def __next__(self):
#     def next(self):
#     def __init__(self, *args, **kwargs):
#     def ftol(self):
#     def bkmax(self):
#     def decr(self):
#     def bk(self):
#     def next(self):
#     def __init__(self, *args, **kwargs):
#     def gtol(self):
#     def wmax(self):
#     def incr(self):
#     def trial_slope(self):
#     def next(self):


from math import sqrt
from nlp.ls.linesearch import LineSearch, LineSearchFailure
import numpy as np
        """
        name = kwargs.pop("name", "Armijo linesearch")
        super(QuadraticCubicLineSearch, self).__init__(*args, name=name,
                                                       **kwargs)
        self.__ftol = max(min(kwargs.get("ftol", 1.0e-4), 1 - sqeps), sqeps)
        self.__bkmax = max(kwargs.get("bkmax", 20), 0)
        self.__eps1 = self.__eps2 = sqrt(eps) / 100
        self._bk = 0
        self._last_step = None
        self._last_trial_value = None
        return

    @property
    def ftol(self):
        return self.__ftol

    @property
    def bkmax(self):
        return self.__bkmax

    @property
    def bk(self):
        return self._bk

    def next(self):
        if self.trial_value <= self.value + self.step * self.ftol * self.slope:
            raise StopIteration()

        self._bk += 1
        if self._bk > self.__bkmax:

# Path: nlp/ls/linesearch.py
# class LineSearch(object):
# class ArmijoLineSearch(LineSearch):
# class ArmijoWolfeLineSearch(ArmijoLineSearch):
#     def __init__(self, linemodel, name="Generic Linesearch", **kwargs):
#     def linemodel(self):
#     def name(self):
#     def value(self):
#     def step(self):
#     def stepmin(self):
#     def trial_value(self):
#     def iterate(self):
#     def slope(self):
#     def check_slope(self, slope):
#     def __iter__(self):
#     def __next__(self):
#     def next(self):
#     def __init__(self, *args, **kwargs):
#     def ftol(self):
#     def bkmax(self):
#     def decr(self):
#     def bk(self):
#     def next(self):
#     def __init__(self, *args, **kwargs):
#     def gtol(self):
#     def wmax(self):
#     def incr(self):
#     def trial_slope(self):
#     def next(self):


# Path: nlp/ls/quad_cub.py
from math import sqrt
from nlp.ls.linesearch import LineSearch, LineSearchFailure
import numpy as np

        """
        name = kwargs.pop("name", "Armijo linesearch")
        super(QuadraticCubicLineSearch, self).__init__(*args, name=name,
                                                       **kwargs)
        self.__ftol = max(min(kwargs.get("ftol", 1.0e-4), 1 - sqeps), sqeps)
        self.__bkmax = max(kwargs.get("bkmax", 20), 0)
        self.__eps1 = self.__eps2 = sqrt(eps) / 100
        self._bk = 0
        self._last_step = None
        self._last_trial_value = None
        return

    @property
    def ftol(self):
        return self.__ftol

    @property
    def bkmax(self):
        return self.__bkmax

    @property
    def bk(self):
        return self._bk

    def next(self):
        if self.trial_value <= self.value + self.step * self.ftol * self.slope:
            raise StopIteration()

        self._bk += 1
        if self._bk > self.__bkmax:

            raise LineSearchFailure("backtracking limit exceeded")