def calc_residuals(self, h, a):
"""
Updates right hand side of the CC equations, commonly referred as G
"""
t2names = ['xlam', 'x1', 'x2', 'x3', 'x4']
t2x = [a.t2[key] for key in t2names]
t3names = ['xlam', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']
t3x = [a.t3[key] for key in t3names]
# symmetrize t2 before feeding into res
t2x_sym = ncpd_symmetrize(t2x, {(1, 0, 3, 2): ('ident',)})
# symmetrize t3 before feeding into res
t3x_sym = ncpd_symmetrize(t3x, {(1, 2, 0, 4, 5, 3): ('ident',),
(2, 0, 1, 5, 3, 4): ('ident',),
(0, 2, 1, 3, 5, 4): ('ident',),
(2, 1, 0, 5, 4, 3): ('ident',),
(1, 0, 2, 4, 3, 5): ('ident',)})
# return _rccsdt_ncpd_ls_t_calc_residuals(h, a)
return _rccsdt_mul_ri_calc_residuals(
h,
Tensors(t1=a.t1,
t2=ncpd_rebuild(t2x_sym),
t3=ncpd_rebuild(t3x_sym))
)
def calculate_update(self, h, a):
"""
Calculate new amplitudes
"""
names_abij = ['xlam', 'x1', 'x2', 'x3', 'x4']
xs = [a.t2[key] for key in names_abij]
# symmetrize t2 before feeding into res
xs_sym = ncpd_symmetrize(xs, {(1, 0, 3, 2): ('ident', )})
# Calculate residuals
r = self.calc_residuals(
h, Tensors(t1=a.t1, t2=Tensors(zip(names_abij, xs_sym))))
# Calculate T1
t1 = a.t1 - r.t1 * (cc_denom(h.f, 2, 'dir', 'full'))
# Symmetrize T2 residuals
r2 = 1 / 2 * (r.t2 + r.t2.transpose([1, 0, 3, 2]))
# Solve
r2_d = -r2 * cc_denom(h.f, 4, 'dir', 'full')
t2 = [f for f in xs]
for idx in range(len(t2)):
g = (als_contract_dense(t2, r2_d, idx, tensor_format='ncpd') +
als_contract_cpd(t2, xs_sym, idx, tensor_format='ncpd'))
s = als_pseudo_inverse(t2, t2, idx)
f = np.dot(g, s)
t2[idx] = f
return Tensors(t1=t1, t2=Tensors(zip(names_abij, t2)))
def calc_residuals(self, h, a):
"""
Calculates CC residuals for CC equations
"""
# Symmetrize T2 before feeding into res
xs_sym = ncpd_symmetrize(
[a.t2.xlam, a.t2.x1, a.t2.x2, a.t2.x3, a.t2.x4],
{(1, 0, 3, 2): ('ident', )})
amps = Tensors(t1=a.t1, t2=ncpd_rebuild(xs_sym))
# residuals are calculated with full amps for speed
return _rccsd_ri_calc_residuals(h, amps)
def calculate_update(self, h, a):
"""
Calculate new amplitudes
"""
t3names = ['xlam', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']
t3x = [a.t3[key] for key in t3names]
# Running residuals with symmetrized amplitudes.
# symmetrizetion is done inside calc_residuals
r = self.calc_residuals(
h,
Tensors(t1=a.t1, t2=a.t2,
t3=Tensors(zip(t3names, t3x)))
)
t1 = a.t1 - r.t1 * (cc_denom(h.f, 2, 'dir', 'full'))
# Solve T2
r2_d = - r.t2 * cc_denom(h.f, 4, 'dir', 'full')
t2 = a.t2 + r2_d
# Symmetrize
t2 = 1 / 2 * (t2 + t2.transpose([1, 0, 3, 2]))
# Build full unit residual
r3s = (+ r.t3
- r.t3.transpose([0, 1, 2, 3, 5, 4])
+ r.t3.transpose([0, 1, 2, 4, 5, 3]))
r3u = ((+ r.t3
+ r.t3.transpose([1, 2, 0, 4, 5, 3])
+ r.t3.transpose([2, 0, 1, 5, 3, 4])
+ r.t3.transpose([0, 2, 1, 3, 5, 4])
+ r.t3.transpose([2, 1, 0, 5, 4, 3])
+ r.t3.transpose([1, 0, 2, 4, 3, 5])
+ 2 * r3s) / 12)
# Solve
r3_d = - r3u * cc_denom(h.f, 6, 'dir', 'full')
# symmetrize inital T3
t3x_sym = ncpd_symmetrize(t3x, {(1, 2, 0, 4, 5, 3): ('ident',),
(2, 0, 1, 5, 3, 4): ('ident',),
(0, 2, 1, 3, 5, 4): ('ident',),
(2, 1, 0, 5, 4, 3): ('ident',),
(1, 0, 2, 4, 3, 5): ('ident',)})
# 0 1 2 4 3 5 permutation of t3
t3x_sym_a = [t3x_sym[0], t3x_sym[1], t3x_sym[2], t3x_sym[3],
t3x_sym[5], t3x_sym[4], t3x_sym[6]]
# Build combinations of T for unitary residual
t3x_u = ncpd_symmetrize(
t3x_sym,
{(0, 1, 2, 4, 3, 5): ('neg',),
(0, 1, 2, 5, 4, 3): ('neg',),
(0, 1, 2, 3, 5, 4): ('neg',),
(0, 1, 2, 5, 3, 4): ('ident',),
(0, 1, 2, 4, 5, 3): ('ident',), },
weights=[2 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 6, 1 / 6])
t3x_au = ncpd_symmetrize(
t3x_sym_a,
{(0, 1, 2, 4, 3, 5): ('neg',),
(0, 1, 2, 5, 4, 3): ('neg',),
(0, 1, 2, 3, 5, 4): ('neg',),
(0, 1, 2, 5, 3, 4): ('ident',),
(0, 1, 2, 4, 5, 3): ('ident',), },
weights=[2 / 3, 1 / 3, 1 / 3, 1 / 3, 1 / 6, 1 / 6])
t3 = [f for f in t3x]
for idx in range(len(t3)):
g = (als_contract_dense(t3, r3_d, idx,
tensor_format='ncpd')
+ als_contract_cpd(t3, t3x_u, idx,
tensor_format='ncpd')
- als_contract_cpd(t3, t3x_au, idx,
tensor_format='ncpd')
)
s = als_pseudo_inverse(t3, t3, idx)
f = np.dot(g, s)
t3[idx] = f
return Tensors(t1=t1, t2=t2,
t3=Tensors(zip(t3names, t3)))
def calculate_update(self, h, a):
"""
Calculate new amplitudes
"""
t2names = ['xlam', 'x1', 'x2', 'x3', 'x4']
t2x = [a.t2[key] for key in t2names]
t3names = ['xlam', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6']
t3x = [a.t3[key] for key in t3names]
# Running residuals with symmetrized amplitudes.
# Symmetrization done inside calc_residuals
r = self.calc_residuals(
h,
Tensors(t1=a.t1,
t2=Tensors(zip(t2names, t2x)),
t3=Tensors(zip(t3names, t3x))))
t1 = a.t1 - r.t1 * (cc_denom(h.f, 2, 'dir', 'full'))
# Build symmetric residual
r2 = 1 / 2 * (r.t2 + r.t2.transpose([1, 0, 3, 2]))
# Solve
r2_d = -r2 * cc_denom(h.f, 4, 'dir', 'full')
# symmetrize initial T2
t2x_sym = ncpd_symmetrize(t2x, {(1, 0, 3, 2): ('ident', )})
t2 = [f for f in t2x]
for idx in range(len(t2)):
g = (als_contract_dense(t2, r2_d, idx, tensor_format='ncpd') +
als_contract_cpd(t2, t2x_sym, idx, tensor_format='ncpd'))
s = als_pseudo_inverse(t2, t2, idx)
f = np.dot(g, s)
t2[idx] = f
# Build symmetric residual
r3 = ((+r.t3 + r.t3.transpose([1, 2, 0, 4, 5, 3]) + r.t3.transpose(
[2, 0, 1, 5, 3, 4]) + r.t3.transpose([0, 2, 1, 3, 5, 4]) +
r.t3.transpose([2, 1, 0, 5, 4, 3]) +
r.t3.transpose([1, 0, 2, 4, 3, 5])) / 6)
# Solve
r3_d = -r3 * cc_denom(h.f, 6, 'dir', 'full')
# symmetrize initial t3
t3x_sym = ncpd_symmetrize(
t3x, {
(1, 2, 0, 4, 5, 3): ('ident', ),
(2, 0, 1, 5, 3, 4): ('ident', ),
(0, 2, 1, 3, 5, 4): ('ident', ),
(2, 1, 0, 5, 4, 3): ('ident', ),
(1, 0, 2, 4, 3, 5): ('ident', )
})
# 0 1 2 4 3 5 permutation of t3
t3x_sym_a = [
t3x_sym[0], t3x_sym[1], t3x_sym[2], t3x_sym[3], t3x_sym[5],
t3x_sym[4], t3x_sym[6]
]
t3 = [f for f in t3x]
for idx in range(len(t3)):
g = (als_contract_dense(t3, r3_d, idx, tensor_format='ncpd') +
als_contract_cpd(t3, t3x_sym, idx, tensor_format='ncpd') -
als_contract_cpd(t3, t3x_sym_a, idx, tensor_format='ncpd'))
s = als_pseudo_inverse(t3, t3, idx)
f = np.dot(g, s)
t3[idx] = f
return Tensors(t1=t1,
t2=Tensors(zip(t2names, t2)),
t3=Tensors(zip(t3names, t3)))