def test_mat_op_inverse(fn):
# Sparse case
sparse_mat = _sparse_matrix(fn)
op_sparse = MatrixOperator(sparse_mat, fn, fn)
op_sparse_inv = op_sparse.inverse
assert op_sparse_inv.domain == op_sparse.range
assert op_sparse_inv.range == op_sparse.domain
assert all_almost_equal(op_sparse_inv.matrix,
np.linalg.inv(op_sparse.matrix.todense()))
assert all_almost_equal(op_sparse_inv.inverse.matrix,
op_sparse.matrix.todense())
# Test application
x = noise_element(fn)
assert all_almost_equal(x, op_sparse.inverse(op_sparse(x)))
# Dense case
dense_mat = _dense_matrix(fn)
op_dense = MatrixOperator(dense_mat, fn, fn)
op_dense_inv = op_dense.inverse
assert op_dense_inv.domain == op_dense.range
assert op_dense_inv.range == op_dense.domain
assert all_almost_equal(op_dense_inv.matrix,
np.linalg.inv(op_dense.matrix))
assert all_almost_equal(op_dense_inv.inverse.matrix, op_dense.matrix)
# Test application
x = noise_element(fn)
assert all_almost_equal(x, op_dense.inverse(op_dense(x)))
def test_matrix_op_inverse():
"""Test if the inverse of matrix operators is correct."""
dense_matrix = np.ones((3, 3)) + 4 * np.eye(3) # invertible
sparse_matrix = scipy.sparse.coo_matrix(dense_matrix)
# Default 1d case
dmat_op = MatrixOperator(dense_matrix)
smat_op = MatrixOperator(sparse_matrix)
x = noise_element(dmat_op.domain)
md_x = dmat_op(x)
mdinv_md_x = dmat_op.inverse(md_x)
assert all_almost_equal(x, mdinv_md_x)
ms_x = smat_op(x)
msinv_ms_x = smat_op.inverse(ms_x)
assert all_almost_equal(x, msinv_ms_x)
# Multi-dimensional case
domain = odl.tensor_space((2, 2, 3), dense_matrix.dtype)
mat_op = MatrixOperator(dense_matrix, domain, axis=2)
x = noise_element(mat_op.domain)
m_x = mat_op(x)
minv_m_x = mat_op.inverse(m_x)
assert all_almost_equal(x, minv_m_x)