def matrix_pair(self, rng, size, size_inner):
factor_matrix = rng.standard_normal((size, size_inner))
inner_symmetric_matrix = rng.standard_normal((size_inner, size_inner))
inner_symmetric_matrix = inner_symmetric_matrix + inner_symmetric_matrix.T
symmetric_matrix = rng.standard_normal((size, size))
symmetric_matrix = symmetric_matrix + symmetric_matrix.T
return (
matrices.SymmetricLowRankUpdateMatrix(
matrices.DenseRectangularMatrix(factor_matrix),
matrices.DenseSymmetricMatrix(symmetric_matrix),
matrices.DenseSymmetricMatrix(inner_symmetric_matrix),
),
symmetric_matrix
+ factor_matrix @ (inner_symmetric_matrix @ factor_matrix.T),
)
def __init__(self):
matrix_pairs = {}
rng = np.random.RandomState(SEED)
for sz in SIZES:
array = rng.standard_normal((sz, sz))
array = array + array.T
matrix_pairs[sz] = (matrices.DenseSymmetricMatrix(array), array)
super().__init__(matrix_pairs, rng)
def matrix_pair(self, rng, size, n_block):
arrays = [rng.standard_normal((size, size)) for _ in range(n_block)]
arrays = [arr + arr.T for arr in arrays]
return (
matrices.SymmetricBlockDiagonalMatrix(
matrices.DenseSymmetricMatrix(arr) for arr in arrays
),
sla.block_diag(*arrays),
)
def __init__(self):
matrix_pairs = {}
rng = np.random.RandomState(SEED)
for outer_dim in SIZES:
for inner_dim in [max(1, outer_dim // 2), max(1, outer_dim - 1)]:
factor_matrix = rng.standard_normal((outer_dim, inner_dim))
inner_symmetric_matrix = rng.standard_normal(
(inner_dim, inner_dim))
inner_symmetric_matrix = (inner_symmetric_matrix +
inner_symmetric_matrix.T)
symmetric_matrix = rng.standard_normal((outer_dim, outer_dim))
symmetric_matrix = symmetric_matrix + symmetric_matrix.T
matrix_pairs[(inner_dim, outer_dim)] = (
matrices.SymmetricLowRankUpdateMatrix(
matrices.DenseRectangularMatrix(factor_matrix),
matrices.DenseSymmetricMatrix(symmetric_matrix),
matrices.DenseSymmetricMatrix(inner_symmetric_matrix)),
symmetric_matrix +
factor_matrix @ (inner_symmetric_matrix @ factor_matrix.T))
super().__init__(matrix_pairs, rng)
def __init__(self):
matrix_pairs = {}
rng = np.random.RandomState(SEED)
for s in SIZES:
for n_block in [1, 2, 5]:
arrays = [rng.standard_normal((s, s)) for _ in range(n_block)]
arrays = [arr + arr.T for arr in arrays]
matrix_pairs[(s, n_block)] = (
matrices.SymmetricBlockDiagonalMatrix(
matrices.DenseSymmetricMatrix(arr)
for arr in arrays), sla.block_diag(*arrays))
super().__init__(matrix_pairs, rng)
def matrix_pair(self, rng, size):
array = rng.standard_normal((size, size))
array = array + array.T
return matrices.DenseSymmetricMatrix(array), array