def test_syevj(self):
a = cupy.array(self.a, order=self.order)
w, v = cusolver.syevj(a, UPLO=self.UPLO, with_eigen_vector=True)
# check eignvalue equation
testing.assert_allclose(self.a.dot(
v.get()), w * v, rtol=1e-3, atol=1e-4)
def test_syevjBatched(self):
lda, m = self.a.shape
na = numpy.stack([self.a, self.a + numpy.diag(numpy.ones(m))])
a = cupy.array(na, order=self.order)
w, v = cusolver.syevj(a, UPLO=self.UPLO, with_eigen_vector=True)
# check eignvalue equation
batch_size = a.shape[0]
for i in range(batch_size):
testing.assert_allclose(
na[i].dot(v[i].get()), w[i] * v[i], rtol=1e-3, atol=1e-4)
# check arbitrary batch dimension shape
na = numpy.stack([na, na, na])
a = cupy.array(na, order=self.order)
w, v = cusolver.syevj(a, UPLO=self.UPLO, with_eigen_vector=True)
assert v.shape == a.shape
assert w.shape == a.shape[:-1]