def solve_static_iterative(self, maxiter=20, conv=1.e-9, use_diis=True):
# Init JK object
jk = psi4.core.JK.build(self.scf_wfn.basisset())
jk.initialize()
# Add blank matrices to the jk object and numpy hooks to C_right
npC_right = []
for xyz in range(3):
jk.C_left_add(self.Co)
mC = psi4.core.Matrix(self.nbf, self.nocc)
npC_right.append(np.asarray(mC))
jk.C_right_add(mC)
# Build initial guess, previous vectors, diis object, and C_left updates
self.x = []
x_old = []
diis = []
ia_denom = -self.epsilon[:self.nocc].reshape(
-1, 1) + self.epsilon[self.nocc:]
for xyz in range(3):
self.x.append(self.dipoles_xyz[xyz] / ia_denom)
x_old.append(np.zeros(ia_denom.shape))
diis.append(DIIS_helper())
# Convert Co and Cv to numpy arrays
Co = np.asarray(self.Co)
Cv = np.asarray(self.Cv)
print('\nStarting CPHF iterations:')
t = time.time()
for CPHF_ITER in range(1, maxiter + 1):
# Update jk's C_right
for xyz in range(3):
npC_right[xyz][:] = Cv.dot(self.x[xyz].T)
# Compute JK objects
jk.compute()
# Update amplitudes
for xyz in range(3):
# Build J and K objects
J = np.asarray(jk.J()[xyz])
K = np.asarray(jk.K()[xyz])
# Bulid new guess
X = self.dipoles_xyz[xyz].copy()
X -= (Co.T).dot(4 * J - K.T - K).dot(Cv)
X /= ia_denom
# DIIS for good measure
if use_diis:
diis[xyz].add(X, X - x_old[xyz])
X = diis[xyz].extrapolate()
self.x[xyz] = X.copy()
# Check for convergence
rms = []
for xyz in range(3):
rms.append(np.max((self.x[xyz] - x_old[xyz])**2))
x_old[xyz] = self.x[xyz]
avg_RMS = sum(rms) / 3
max_RMS = max(rms)
if max_RMS < conv:
print('CPHF converged in %d iterations and %.2f seconds.' %
(CPHF_ITER, time.time() - t))
self.rhsvecs = []
for numx in range(3):
rhsvec = self.dipoles_xyz[numx].reshape(-1)
self.rhsvecs.append(rhsvec)
self.x[numx] = self.x[numx].reshape(-1)
break
print(
'CPHF Iteration %3d: Average RMS = %3.8f Maximum RMS = %3.8f'
% (CPHF_ITER, avg_RMS, max_RMS))
def solve_dynamic_iterative(self,
omega=0.0,
maxiter=20,
conv=1.e-9,
use_diis=True):
# Init JK object
jk = psi4.core.JK.build(self.scf_wfn.basisset())
jk.initialize()
# Add blank matrices to the JK object and NumPy hooks to
# C_right; there are 6 sets of matrices to account for X and Y
# vectors separately.
npC_right = []
for xyz in range(6):
jk.C_left_add(self.Co)
mC = psi4.core.Matrix(self.nbf, self.nocc)
npC_right.append(np.asarray(mC))
jk.C_right_add(mC)
# Build initial guess, previous vectors, diis object, and C_left updates
x_l, x_r = [], []
x_l_old, x_r_old = [], []
diis_l, diis_r = [], []
ia_denom_l = self.epsilon[self.nocc:] - self.epsilon[:self.
nocc].reshape(
-1, 1) - omega
ia_denom_r = self.epsilon[self.nocc:] - self.epsilon[:self.
nocc].reshape(
-1, 1) + omega
for xyz in range(3):
x_l.append(self.dipoles_xyz[xyz] / ia_denom_l)
x_r.append(self.dipoles_xyz[xyz] / ia_denom_r)
x_l_old.append(np.zeros(ia_denom_l.shape))
x_r_old.append(np.zeros(ia_denom_r.shape))
diis_l.append(DIIS_helper())
diis_r.append(DIIS_helper())
# Convert Co and Cv to numpy arrays
Co = np.asarray(self.Co)
Cv = np.asarray(self.Cv)
print('\nStarting CPHF iterations:')
t = time.time()
for CPHF_ITER in range(1, maxiter + 1):
# Update jk's C_right; ordering is Xx, Xy, Xz, Yx, Yy, Yz
for xyz in range(3):
npC_right[xyz][:] = Cv.dot(x_l[xyz].T)
npC_right[xyz + 3][:] = Cv.dot(x_r[xyz].T)
# Perform generalized J/K build
jk.compute()
# Update amplitudes
for xyz in range(3):
# Build J and K objects
J_l = np.asarray(jk.J()[xyz])
K_l = np.asarray(jk.K()[xyz])
J_r = np.asarray(jk.J()[xyz + 3])
K_r = np.asarray(jk.K()[xyz + 3])
# Bulid new guess
X_l = self.dipoles_xyz[xyz].copy()
X_r = self.dipoles_xyz[xyz].copy()
X_l -= (Co.T).dot(2 * J_l - K_l).dot(Cv)
X_r -= (Co.T).dot(2 * J_r - K_r).dot(Cv)
X_l /= ia_denom_l
X_r /= ia_denom_r
# DIIS for good measure
if use_diis:
diis_l[xyz].add(X_l, X_l - x_l_old[xyz])
X_l = diis_l[xyz].extrapolate()
diis_r[xyz].add(X_r, X_r - x_r_old[xyz])
X_r = diis_r[xyz].extrapolate()
x_l[xyz] = X_l.copy()
x_r[xyz] = X_r.copy()
# Check for convergence
rms = []
for xyz in range(3):
rms_l = np.max((x_l[xyz] - x_l_old[xyz])**2)
rms_r = np.max((x_r[xyz] - x_r_old[xyz])**2)
rms.append(max(rms_l, rms_r))
x_l_old[xyz] = x_l[xyz]
x_r_old[xyz] = x_r[xyz]
avg_RMS = sum(rms) / 3
max_RMS = max(rms)
if max_RMS < conv:
print('CPHF converged in %d iterations and %.2f seconds.' %
(CPHF_ITER, time.time() - t))
self.rhsvecs = []
for numx in range(3):
rhsvec = self.dipoles_xyz[numx].reshape(-1)
self.rhsvecs.append(np.concatenate((rhsvec, -rhsvec)))
self.x.append(
np.concatenate(
(x_l[numx].reshape(-1), x_r[numx].reshape(-1))))
break
print(
'CPHF Iteration %3d: Average RMS = %3.8f Maximum RMS = %3.8f'
% (CPHF_ITER, avg_RMS, max_RMS))
npC_right = []
for xyz in range(3):
jk.C_left_add(Co)
mC = psi4.core.Matrix(nbf, nocc)
npC_right.append(np.asarray(mC))
jk.C_right_add(mC)
# Build initial guess, previous vectors, diis object, and C_left updates
x = []
x_old = []
diis = []
ia_denom = -epsilon[:nocc].reshape(-1, 1) + epsilon[nocc:]
for xyz in range(3):
x.append(dipoles_xyz[xyz] / ia_denom)
x_old.append(np.zeros(ia_denom.shape))
diis.append(DIIS_helper())
# Convert Co and Cv to numpy arrays
mCo = Co
Co = np.asarray(Co)
Cv = np.asarray(Cv)
print('\nStarting CPHF iterations:')
t = time.time()
for CPHF_ITER in range(1, maxiter + 1):
# Update jk's C_right
for xyz in range(3):
npC_right[xyz][:] = Cv.dot(x[xyz].T)
# Compute JK objects