def kernel(self):
matvecs = lambda xs: [self.matvec(x) for x in xs]
precond = self.get_precond()
guesses = list(self.get_guesses())
pick = self.get_picker(guesses=guesses)
conv, e, v = self.davidson(
matvecs,
guesses,
precond,
tol=self.conv_tol,
max_cycle=self.max_cycle,
max_space=self.max_space,
lindep=self.lindep_tol,
max_memory=1e9,
nroots=self.nroots,
pick=pick,
verbose=0,
)
mask = np.argsort(e)
conv = [conv[x] for x in mask]
e = e[mask]
v = np.array([v[x] for x in mask]).T
for i in range(self.nroots):
conv_str = "(*)" if not conv[i] else ""
self.log.info(" %s: %s %s", i, util.format_value(e[i]), conv_str)
if not all(conv):
self.log.info(" (*) = not converged!")
return e, v, conv
def kernel(self):
e, v = self.eig(self.matrix)
self.log.info("eigenvalues:")
for i in range(min(5, len(e))):
self.log.info(" %s: %s", i, util.format_value(e[i]))
self.log.info(" ...")
return e, v
def kernel(self):
α, β = self.get_blocks()
orth = self.mat_sqrt(self.moments[0])
h_tri = self.build_block_tridiagonal(α, β)
e, u = np.linalg.eigh(h_tri)
u = np.dot(orth, u[:self.norb])
for i in range(min(5, len(e))):
self.log.info(" %s: %s", i, util.format_value(e[i]))
self.log.info(" ...")
return e, u
def kernel(self):
α, β, γ = self.get_blocks()
orth = self.mat_sqrt(self.moments[0])
h_tri = self.build_block_tridiagonal(α, β, γ)
e, u = np.linalg.eig(h_tri)
ul = np.dot(orth, u[:self.norb])
ur = np.dot(np.linalg.inv(u)[:, :self.norb], orth).T.conj()
for i in range(min(5, len(e))):
self.log.info(" %s: %s", i, util.format_value(e[i]))
self.log.info(" ...")
return e, ul, ur
def kernel(self):
e, u = self.get_auxiliaries()
h_aux = np.block([
[self.h_phys, u],
[u.T.conj(), np.diag(e)],
])
e, v = np.linalg.eigh(h_aux)
v = v[:self.norb]
for i in range(min(5, len(e))):
self.log.info(" %s: %s", i, util.format_value(e[i]))
self.log.info(" ...")
return e, v