def matrix_pair(self, rng, size, size_inner):
left_factor_matrix = rng.standard_normal((size, size_inner))
right_factor_matrix = rng.standard_normal((size_inner, size))
square_matrix = rng.standard_normal((size, size))
return (
matrices.SquareLowRankUpdateMatrix(
matrices.DenseRectangularMatrix(left_factor_matrix),
matrices.DenseRectangularMatrix(right_factor_matrix),
matrices.DenseSquareMatrix(square_matrix),
),
square_matrix + left_factor_matrix @ right_factor_matrix,
)
def __init__(self):
matrix_pairs = {}
rng = np.random.RandomState(SEED)
for outer_dim in SIZES:
inner_dim = max(1, outer_dim // 2)
left_factor_matrix = rng.standard_normal((outer_dim, inner_dim))
right_factor_matrix = rng.standard_normal((inner_dim, outer_dim))
square_matrix = rng.standard_normal((outer_dim, outer_dim))
matrix_pairs[(inner_dim, outer_dim)] = (
matrices.SquareLowRankUpdateMatrix(
matrices.DenseRectangularMatrix(left_factor_matrix),
matrices.DenseRectangularMatrix(right_factor_matrix),
matrices.DenseSquareMatrix(square_matrix)),
square_matrix + left_factor_matrix @ right_factor_matrix)
super().__init__(matrix_pairs, rng)