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