def test_dimensions(self):
bm = BlockMatrix(2, 2)
self.assertTrue(bm.has_undefined_row_sizes())
self.assertTrue(bm.has_undefined_col_sizes())
with self.assertRaises(NotFullyDefinedBlockMatrixError):
shape = bm.shape
with self.assertRaises(NotFullyDefinedBlockMatrixError):
bm.set_block(0, 0, BlockMatrix(2, 2))
with self.assertRaises(NotFullyDefinedBlockMatrixError):
row_sizes = bm.row_block_sizes()
with self.assertRaises(NotFullyDefinedBlockMatrixError):
col_sizes = bm.col_block_sizes()
bm2 = BlockMatrix(2, 2)
bm2.set_block(0, 0, coo_matrix((2, 2)))
bm2.set_block(1, 1, coo_matrix((2, 2)))
bm3 = bm2.copy()
bm.set_block(0, 0, bm2)
bm.set_block(1, 1, bm3)
self.assertFalse(bm.has_undefined_row_sizes())
self.assertFalse(bm.has_undefined_col_sizes())
self.assertEqual(bm.shape, (8, 8))
bm.set_block(0, 0, None)
self.assertFalse(bm.has_undefined_row_sizes())
self.assertFalse(bm.has_undefined_col_sizes())
self.assertEqual(bm.shape, (8, 8))
self.assertTrue(np.all(bm.row_block_sizes() == np.ones(2) * 4))
self.assertTrue(np.all(bm.col_block_sizes() == np.ones(2) * 4))
self.assertTrue(
np.all(bm.row_block_sizes(copy=False) == np.ones(2) * 4))
self.assertTrue(
np.all(bm.col_block_sizes(copy=False) == np.ones(2) * 4))
def regularize_hessian(self,
kkt: BlockMatrix,
coef: float,
copy_kkt: bool = True) -> BlockMatrix:
if copy_kkt:
kkt = kkt.copy()
for ndx, nlp in self._nlps.items():
nlp.regularize_hessian(kkt=kkt.get_block(ndx, ndx).get_block(0, 0),
coef=coef,
copy_kkt=False)
return kkt
