def optimize_shift(u0s, driv, l_op, shift_z0, opt_index=None,
**keyargs):
ns = [u.n for u in u0s]
zs = [u.z for u in u0s]
bt = type(u0s[0])
if(opt_index == None):
opt_index = range(len(zs))
def basis(shift_z):
return [l2.d0_basis(bt, n, z + cond(i in opt_index, shift_z, 0))
for (n, z, i)
in zip(ns, zs, range(len(zs)))]
# numerical derivative
if('h' in keyargs):
def one(vs):
shift_z = vs[0]
us = basis(shift_z)
return val_only(us, driv, l_op)
res = nnewton.nnewton(one, [shift_z0], func='v', **keyargs)
(res_conv, shift_z_opt, val_opt, grad_opt) = res
# analytic derivative
else:
def one(vs):
shift_z = vs[0]
us = basis(shift_z)
(val, grad, hess, data) = val_grad_hess(us, driv, l_op)
g_val = sum([grad[i] for i in opt_index])
h_val = sum([hess[i, j]
for i in opt_index
for j in opt_index])
g = np.array([g_val])
h = np.array([[h_val]])
return (val, g, h)
res = nnewton.nnewton(one, [shift_z0], func="vgh", **keyargs)
(res_conv, shift_z_opt, val_opt, grad_opt) = res
basis_opt = basis(shift_z_opt[0])
return (res_conv, basis_opt, shift_z_opt[0], val_opt, grad_opt)
def test_1basis_1skp(self):
us = [ l2.STO(1.0, 2, 0.5-0.5j) ]
h_atom = l2.HAtom(1.0)
l_op = h_atom.h_minus_ene_op(1, 0.9)
driv = h_atom.length(1, 0, 1)
target = opt_cbf.newton_target(us, driv, l_op)
res = nnewton(target, [0.5-0.5j], func="vgh")
"""
res = opt_cbf.optimize_simple(us, driv, l_op, eps=0.0000001, show_lvl=0)
self.assertTrue(res[0])
"""
self.assertTrue(res[0])
self.assertAlmostEqual(1.1117640506-0.3673558953j, res[1][0])
