# Path: pybobyqa/util.py
# def sumsq(x):
#     # There are several ways to calculate sum of squares of a vector:
#     #   np.dot(x,x)
#     #   np.sum(x**2)
#     #   np.sum(np.square(x))
#     #   etc.
#     # Using the timeit routine, it seems like dot(x,x) is ~3-4x faster than other methods
#     return np.dot(x, x)
# 
# def model_value(g, H, s):
#     # Calculate model value (s^T * g + 0.5* s^T * H * s) = s^T * (gopt + 0.5 * H*s)
#     assert g.shape == s.shape, "g and s have incompatible sizes"
#     Hs = H.dot(s)
#     return np.dot(s, g + 0.5*Hs)


from math import sqrt
from .util import sumsq, model_value
import numpy as np
import warnings
    import trustregion
    # that will reduce Q from its value at XOPT. Thus XBDI(I) shows whether
    # or not to fix the I-th variable at one of its bounds initially, with
    # NACT being set to the number of fixed variables. D and GNEW are also
    # set for the first iteration. DELSQ is the upper bound on the sum of
    # squares of the free variables. QRED is the reduction in Q so far.

    iterc = 0
    nact = 0  # number of fixed variables

    xbdi = np.zeros((n,), dtype=int)  # fix x_i at bounds? [values -1, 0, 1]
    xbdi[(xopt <= sl) & (g >= 0.0)] = -1
    xbdi[(xopt >= su) & (g <= 0.0)] = 1

    d = np.zeros((n,))
    s = np.zeros((n,))
    gnew = g.copy()
    qred = 0.0
    delsq = delta ** 2
    crvmin = -1.0
    beta = 0.0  # label 20

    need_alt_trust_step = False  # will either quit main CG loop to finish, or do alternative step
    MAX_LOOP_ITERS = 100 * n ** 2  # avoid infinite loops
    # while True:  # main CG loop [label 30]
    for ii in range(MAX_LOOP_ITERS):
        s[xbdi != 0] = 0.0
        if beta == 0.0:
            s[xbdi == 0] = -gnew[xbdi == 0]
        else:
            s[xbdi == 0] = beta * s[xbdi == 0] - gnew[xbdi == 0]

# Path: pybobyqa/util.py
# def sumsq(x):
#     # There are several ways to calculate sum of squares of a vector:
#     #   np.dot(x,x)
#     #   np.sum(x**2)
#     #   np.sum(np.square(x))
#     #   etc.
#     # Using the timeit routine, it seems like dot(x,x) is ~3-4x faster than other methods
#     return np.dot(x, x)
# 
# def model_value(g, H, s):
#     # Calculate model value (s^T * g + 0.5* s^T * H * s) = s^T * (gopt + 0.5 * H*s)
#     assert g.shape == s.shape, "g and s have incompatible sizes"
#     Hs = H.dot(s)
#     return np.dot(s, g + 0.5*Hs)


# Path: pybobyqa/trust_region.py
from math import sqrt
from .util import sumsq, model_value
import numpy as np
import warnings
    import trustregion

    # that will reduce Q from its value at XOPT. Thus XBDI(I) shows whether
    # or not to fix the I-th variable at one of its bounds initially, with
    # NACT being set to the number of fixed variables. D and GNEW are also
    # set for the first iteration. DELSQ is the upper bound on the sum of
    # squares of the free variables. QRED is the reduction in Q so far.

    iterc = 0
    nact = 0  # number of fixed variables

    xbdi = np.zeros((n,), dtype=int)  # fix x_i at bounds? [values -1, 0, 1]
    xbdi[(xopt <= sl) & (g >= 0.0)] = -1
    xbdi[(xopt >= su) & (g <= 0.0)] = 1

    d = np.zeros((n,))
    s = np.zeros((n,))
    gnew = g.copy()
    qred = 0.0
    delsq = delta ** 2
    crvmin = -1.0
    beta = 0.0  # label 20

    need_alt_trust_step = False  # will either quit main CG loop to finish, or do alternative step
    MAX_LOOP_ITERS = 100 * n ** 2  # avoid infinite loops
    # while True:  # main CG loop [label 30]
    for ii in range(MAX_LOOP_ITERS):
        s[xbdi != 0] = 0.0
        if beta == 0.0:
            s[xbdi == 0] = -gnew[xbdi == 0]
        else:
            s[xbdi == 0] = beta * s[xbdi == 0] - gnew[xbdi == 0]

        stepsq = sumsq(s)