# 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
            # ϕq(t) = t²(ϕ(t₀) - ϕ(0) - t₀ϕ'(0))/t₀² + ϕ'(0)t + ϕ(0)
            self._last_step = self.step
            self._last_trial_value = self.trial_value
            step = -self.slope * self._last_step**2
            step /= 2 * (self.trial_value -
                         self.value - self.slope * self._last_step)
            self._step = step
        else:
            # cubic interpolation
            a0 = self._last_step
            a1 = self.step
            phi0 = self.value
            phi_a0 = self._last_trial_value
            phi_a1 = self.trial_value
            c = 1. / (a0**2 * a1**2 * (a1 - a0))
            M = np.array([[a0**2, -a1**2], [-a0**3, a1**3]])
            coeffs = c * np.dot(M, np.array([phi_a1 - phi0 - self.slope * a1,
                                             phi_a0 - phi0 - self.slope * a0]))
            step = -coeffs[1]
            step += sqrt(coeffs[1]**2 - 3 * coeffs[0] * self.slope)
            step /= 3 * coeffs[0]

            self._last_step = self.step
            self._last_trial_value = self.trial_value
            if abs(step - self._step) < self.__eps1 or abs(step) < self.__eps2:
                self._step = self.step / 2
            else:
                self._step = step

        if self.step < self.stepmin:

# 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

            # ϕq(t) = t²(ϕ(t₀) - ϕ(0) - t₀ϕ'(0))/t₀² + ϕ'(0)t + ϕ(0)
            self._last_step = self.step
            self._last_trial_value = self.trial_value
            step = -self.slope * self._last_step**2
            step /= 2 * (self.trial_value -
                         self.value - self.slope * self._last_step)
            self._step = step
        else:
            # cubic interpolation
            a0 = self._last_step
            a1 = self.step
            phi0 = self.value
            phi_a0 = self._last_trial_value
            phi_a1 = self.trial_value
            c = 1. / (a0**2 * a1**2 * (a1 - a0))
            M = np.array([[a0**2, -a1**2], [-a0**3, a1**3]])
            coeffs = c * np.dot(M, np.array([phi_a1 - phi0 - self.slope * a1,
                                             phi_a0 - phi0 - self.slope * a0]))
            step = -coeffs[1]
            step += sqrt(coeffs[1]**2 - 3 * coeffs[0] * self.slope)
            step /= 3 * coeffs[0]

            self._last_step = self.step
            self._last_trial_value = self.trial_value
            if abs(step - self._step) < self.__eps1 or abs(step) < self.__eps2:
                self._step = self.step / 2
            else:
                self._step = step

        if self.step < self.stepmin:

            raise LineSearchFailure("linesearch step too small")