def solve(self, dms_output, focks_output):
# interpolation only makes sense if there are two points
assert self.nused >= 2
# Fill in the missing commutators
self._complete_edots_matrix()
assert not np.isnan(self.edots[:self.nused,:self.nused]).any()
# Setup the equations
b, e = self._setup_equations()
# Check if solving these equations makes sense.
if b.max() - b.min() == 0 and e.max() - e.min() == 0:
raise NoSCFConvergence('Convergence criteria too tight for EDIIS')
# solve the quadratic programming problem
qps = QPSolver(b, e, np.ones((1,self.nused)), np.array([1.0]), eps=1e-6)
if self.nused < 10:
energy, coeffs = qps.find_brute()
guess = None
else:
guess = np.zeros(self.nused)
guess[e.argmax()] = 1.0
energy, coeffs = qps.find_local(guess, 1.0)
# for debugging purposes (negligible computational overhead)
try:
qps.check_solution(coeffs)
except:
qps.log(guess)
raise
cn = qps.compute_cn(coeffs != 0.0)
# assign extrapolated fock
error = self._build_combinations(coeffs, dms_output, focks_output)
return energy, coeffs, cn, 'E', error
def solve(self, dm_output, fock_output):
'''Extrapolate a new density and/or fock matrix that should have the smallest commutator norm.
**Arguments:**
dm_output
The output for the density matrix. If set to None, this is
argument is ignored.
fock_output
The output for the Fock matrix. If set to None, this is
argument is ignored.
'''
# interpolation only makes sense if there are two points
assert self.nused >= 2
# Fill in the missing commutators
self._complete_edots_matrix()
assert not np.isnan(self.edots[:self.nused, :self.nused]).any()
# Setup the equations
b, e = self._setup_equations()
# Check if solving these equations makes sense.
if b.max() - b.min() == 0 and e.max() - e.min() == 0:
raise NoSCFConvergence('Convergence criteria too tight for EDIIS')
# solve the quadratic programming problem
qps = QPSolver(b,
e,
np.ones((1, self.nused)),
np.array([1.0]),
eps=1e-6)
if self.nused < 10:
energy, coeffs = qps.find_brute()
guess = None
else:
guess = np.zeros(self.nused)
guess[e.argmax()] = 1.0
energy, coeffs = qps.find_local(guess, 1.0)
# for debugging purposes
try:
qps.check_solution(coeffs)
except:
qps.log(guess)
raise
cn = qps.compute_cn(coeffs != 0.0)
# assign extrapolated fock
self._build_combinations(coeffs, dm_output, fock_output)
return energy, coeffs, cn, 'E'
def solve(self, dm_output, fock_output):
'''Extrapolate a new density and/or fock matrix that should have the smallest commutator norm.
**Arguments:**
dm_output
The output for the density matrix. If set to None, this is
argument is ignored.
fock_output
The output for the Fock matrix. If set to None, this is
argument is ignored.
'''
# interpolation only makes sense if there are two points
assert self.nused >= 2
# Fill in the missing commutators
self._complete_edots_matrix()
assert not np.isnan(self.edots[:self.nused,:self.nused]).any()
# Setup the equations
b, e = self._setup_equations()
# Check if solving these equations makes sense.
if b.max() - b.min() == 0 and e.max() - e.min() == 0:
raise NoSCFConvergence('Convergence criteria too tight for EDIIS')
# solve the quadratic programming problem
qps = QPSolver(b, e, np.ones((1,self.nused)), np.array([1.0]), eps=1e-6)
if self.nused < 10:
energy, coeffs = qps.find_brute()
guess = None
else:
guess = np.zeros(self.nused)
guess[e.argmax()] = 1.0
energy, coeffs = qps.find_local(guess, 1.0)
# for debugging purposes
try:
qps.check_solution(coeffs)
except:
qps.log(guess)
raise
cn = qps.compute_cn(coeffs != 0.0)
# assign extrapolated fock
self._build_combinations(coeffs, dm_output, fock_output)
return energy, coeffs, cn, 'E'
