def mat_herm_dense():
np.random.seed(1)
u = qu.rand_uni(4)
a = u @ qu.ldmul(np.array([-1, 2, 4, -3]), u.H)
# |--|--|--|--|--|--|--|
# -3 -1 2 4
return u, a
def test_eigh(self):
H = qu.ham_mbl(6, dh=2.5)
a_el, a_ev = qu.eigh(H, autoblock=False)
el, ev = qu.eigh(H, autoblock=True)
assert qu.norm(ev @ qu.ldmul(el, ev.H) - H, 'fro') < 1e-12
assert_allclose(a_el, el)
assert_allclose(ev.H @ ev, np.eye(H.shape[0]), atol=1e-12)
def test_evecs(self, ham1, which, k, sigma):
lk, vk = seigsys_numpy(ham1,
k=k,
which=which,
return_vecs=True,
sigma=sigma,
sort=False)
assert isinstance(vk, np.matrix)
assert_allclose(dot(vk, ldmul(lk, vk.H)), ham1)
def ham2():
u = qu.rand_uni(7)
el = np.array([-3.72, 0, 1, 1.1, 2.1, 2.2, 6.28])
return u @ qu.ldmul(el, u.H)
def ham1():
u = qu.rand_uni(7)
el = np.array([-3, 0, 1, 2, 3, 4, 7])
return u @ qu.ldmul(el, u.H)
def mat_nherm_sparse():
np.random.seed(1)
u, v = qu.rand_uni(5), qu.rand_uni(5)
a = u @ qu.ldmul(np.array([1, 2, 4, 3, 0.1]), v.H)
a = qu.sparse(a)
return u, v, a
def mat_herm_sparse():
np.random.seed(1)
u = qu.rand_uni(4)
a = u @ qu.ldmul(np.array([-1, 2, 4, -3]), u.H)
a = qu.sparse(a)
return u, a
def mat_nherm_sparse():
np.random.seed(1)
u, v = rand_uni(5), rand_uni(5)
a = u @ ldmul(np.array([1, 2, 4, 3, 0.1]), v.H)
a = qu(a, sparse=True)
return u, v, a
def mat_nherm_dense():
np.random.seed(1)
u, v = rand_uni(5), rand_uni(5)
a = u @ ldmul(np.array([1, 2, 4, 3, 0.1]), v.H)
return u, v, a
def mat_herm_sparse():
np.random.seed(1)
u = rand_uni(4)
a = u @ ldmul(np.array([-1, 2, 4, -3]), u.H)
a = qu(a, sparse=True)
return u, a
def prematsparse():
u = rand_uni(4)
a = u @ ldmul(np.array([-1, 2, 4, -3]), u.H)
a = qu(a, sparse=True)
return u, a
def ham1():
evecs = rand_uni(5)
evals = np.array([-5, -3, 0.1, 2, 4])
return dot(evecs, ldmul(evals, evecs.H))
def test_evecs(self, ham1, which, k, sigma):
lk, vk = eigs_numpy(ham1, k=k, which=which, return_vecs=True,
sigma=sigma, sort=False)
assert isinstance(vk, qu.qarray)
assert_allclose(qu.dot(vk, qu.ldmul(lk, vk.H)), ham1)
