def test_davidson_solver_numpy():
BIGDIM = 100
nroot = 3
guess = list(np.random.randn(BIGDIM, nroot).T)
test_engine = DSProblemSimulate(BIGDIM)
test_vals, test_vectors, _ = davidson_solver(
engine=test_engine,
guess=guess,
nroot=nroot,
#Don't let the ss grow to size of real thing
max_vecs_per_root=20,
# Don't assault stdout with logging
verbose=0,
maxiter=100)
assert test_vals is not None, "Solver Failed to converge"
ref_vals, ref_vectors = np.linalg.eigh(test_engine.A)
idx = ref_vals.argsort()[:nroot]
ref_vals = ref_vals[idx]
ref_vectors = ref_vectors[:, idx]
compare_arrays(ref_vals, test_vals, 6, "Davidson eigenvalues")
# NOTE: The returned eigenvectors are a list of (whatever the engine used is using for a vector)
# So in this case, we need to put the columns together into a matrix to compare directly to the np.LA.eigh result
compare_arrays(ref_vectors, np.column_stack(test_vectors), 8,
"Davidson eigenvectors")
def tda_solver(e, n, g, m):
return solvers.davidson_solver(engine=e,
nroot=n,
guess=g,
r_convergence=r_convergence,
max_ss_size=max_vecs_per_root * n,
verbose=verbose)
def test_davidson_solver_numpy():
BIGDIM = 100
nroot = 3
guess = list(np.random.randn(BIGDIM, nroot).T)
test_engine = DSProblemSimulate(BIGDIM)
test_vals, test_vectors, _ = davidson_solver(
engine=test_engine,
guess=guess,
nroot=nroot,
#Don't let the ss grow to size of real thing
max_vecs_per_root=20,
# Don't assault stdout with logging
verbose=0,
maxiter=100)
assert test_vals is not None, "Solver Failed to converge"
ref_vals, ref_vectors = np.linalg.eigh(test_engine.A)
idx = ref_vals.argsort()[:nroot]
ref_vals = ref_vals[idx]
ref_vectors = ref_vectors[:, idx]
compare_arrays(ref_vals, test_vals, 6, "Davidson eigenvalues")
# NOTE: The returned eigenvectors are a list of (whatever the engine used is using for a vector)
# So in this case, we need to put the columns together into a matrix to compare directly to the np.LA.eigh result
compare_arrays(ref_vectors, np.column_stack(test_vectors), 8, "Davidson eigenvectors")
wfn.form_D()
record = [(False, wfn.compute_E())]
for x in range(5):
prod = SCFProducts(wfn)
def func(vector):
return -1 * prod.H1_product(vector)
def precon(resid, i, A_w):
return resid
nvecs = 5
guess = np.ones((prod.narot, nvecs))
evals, evecs = davidson_solver(func, precon, guess, no_eigs=nvecs, e_conv=1.e-4)
# If x == 0 we take a "bad" step, require rotation next step to get back on track
if (x == 0) or (evals[0] < 0):
stab = True
evecs = -evecs[:, 0]
rot = psi4.core.Matrix.from_array(evecs.reshape(ndocc, nvir))
wfn.rotate_orbitals(wfn.Ca(), rot)
else:
stab = False
wfn.form_C()
wfn.form_D()
wfn.form_G()
wfn.form_F()
print(wfn.compute_E())
record = [(False, wfn.compute_E())]
for x in range(5):
prod = SCFProducts(wfn)
def func(vector):
return -1 * prod.H1_product(vector)
def precon(resid, i, A_w):
return resid
nvecs = 5
guess = np.ones((prod.narot, nvecs))
evals, evecs = davidson_solver(func,
precon,
guess,
no_eigs=nvecs,
e_conv=1.e-4)
# If x == 0 we take a "bad" step, require rotation next step to get back on track
if (x == 0) or (evals[0] < 0):
stab = True
evecs = -evecs[:, 0]
rot = psi4.core.Matrix.from_array(evecs.reshape(ndocc, nvir))
wfn.rotate_orbitals(wfn.Ca(), rot)
else:
stab = False
wfn.form_C()
wfn.form_D()
wfn.form_G()