# 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
from nlp.ls._strong_wolfe_linesearch import dcsrch
import numpy as np

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

    @property
    def gtol(self):
        return self.__gtol

    @property
    def xtol(self):
        return self.__xtol

    @property
    def lb(self):
        return self._lb

    @property
    def ub(self):
        return self._ub

    @property
    def trial_slope(self):
        return self._trial_slope

    def next(self):
        if self.__task[:4] == "CONV":  # strong Wolfe conditions satisfied
            raise StopIteration()

        if self.__task[:2] != "FG":

# 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/wolfe.py
from math import sqrt
from nlp.ls.linesearch import LineSearch, LineSearchFailure
from nlp.ls._strong_wolfe_linesearch import dcsrch
import numpy as np


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

    @property
    def gtol(self):
        return self.__gtol

    @property
    def xtol(self):
        return self.__xtol

    @property
    def lb(self):
        return self._lb

    @property
    def ub(self):
        return self._ub

    @property
    def trial_slope(self):
        return self._trial_slope

    def next(self):
        if self.__task[:4] == "CONV":  # strong Wolfe conditions satisfied
            raise StopIteration()

        if self.__task[:2] != "FG":

            raise LineSearchFailure(self.__task)