tf.linalg.LinearOperatorLowRankUpdate

Class LinearOperatorLowRankUpdate

Inherits From: LinearOperator

Aliases:

• Class tf.contrib.linalg.LinearOperatorLowRankUpdate
• Class tf.linalg.LinearOperatorLowRankUpdate

See the guide: Linear Algebra (contrib) > LinearOperator

Perturb a LinearOperator with a rank K update.

This operator acts like a [batch] matrix A with shape [B1,...,Bb, M, N] for some b >= 0. The first b indices index a batch member. For every batch index (i1,...,ib), A[i1,...,ib, : :] is an M x N matrix.

LinearOperatorLowRankUpdate represents A = L + U D V^H, where

L, is a LinearOperator representing [batch] M x N matrices
U, is a [batch] M x K matrix.  Typically K << M.
D, is a [batch] K x K matrix.
V, is a [batch] N x K matrix.  Typically K << N.
V^H is the Hermitian transpose (adjoint) of V.


If M = N, determinants and solves are done using the matrix determinant lemma and Woodbury identities, and thus require L and D to be non-singular.

Solves and determinants will be attempted unless the "is_non_singular" property of L and D is False.

In the event that L and D are positive-definite, and U = V, solves and determinants can be done using a Cholesky factorization.

# Create a 3 x 3 diagonal linear operator.
diag_operator = LinearOperatorDiag(
is_positive_definite=True)

# Perturb with a rank 2 perturbation
operator = LinearOperatorLowRankUpdate(
operator=diag_operator,
u=[[1., 2.], [-1., 3.], [0., 0.]],
diag_update=[11., 12.],
v=[[1., 2.], [-1., 3.], [10., 10.]])

operator.shape
==> [3, 3]

operator.log_abs_determinant()
==> scalar Tensor

x = ... Shape [3, 4] Tensor
operator.matmul(x)
==> Shape [3, 4] Tensor


Shape compatibility

This operator acts on [batch] matrix with compatible shape. x is a batch matrix with compatible shape for matmul and solve if

operator.shape = [B1,...,Bb] + [M, N],  with b >= 0
x.shape =        [B1,...,Bb] + [N, R],  with R >= 0.


Performance

Suppose operator is a LinearOperatorLowRankUpdate of shape [M, N], made from a rank K update of base_operator which performs .matmul(x) on x having x.shape = [N, R] with O(L_matmul*N*R) complexity (and similarly for solve, determinant. Then, if x.shape = [N, R],

• operator.matmul(x) is O(L_matmul*N*R + K*N*R)

and if M = N,

• operator.solve(x) is O(L_matmul*N*R + N*K*R + K^2*R + K^3)
• operator.determinant() is O(L_determinant + L_solve*N*K + K^2*N + K^3)

If instead operator and x have shape [B1,...,Bb, M, N] and [B1,...,Bb, N, R], every operation increases in complexity by B1*...*Bb.

Matrix property hints

This LinearOperator is initialized with boolean flags of the form is_X, for X = non_singular, self_adjoint, positive_definite, diag_update_positive and square. These have the following meaning:

• If is_X == True, callers should expect the operator to have the property X. This is a promise that should be fulfilled, but is not a runtime assert. For example, finite floating point precision may result in these promises being violated.
• If is_X == False, callers should expect the operator to not have X.
• If is_X == None (the default), callers should have no expectation either way.

Properties

base_operator

If this operator is A = L + U D V^H, this is the L.

batch_shape

TensorShape of batch dimensions of this LinearOperator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns TensorShape([B1,...,Bb]), equivalent to A.get_shape()[:-2]

Returns:

TensorShape, statically determined, may be undefined.

diag_operator

If this operator is A = L + U D V^H, this is D.

diag_update

If this operator is A = L + U D V^H, this is the diagonal of D.

domain_dimension

Dimension (in the sense of vector spaces) of the domain of this operator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns N.

Returns:

Dimension object.

dtype

The DType of Tensors handled by this LinearOperator.

graph_parents

List of graph dependencies of this LinearOperator.

is_diag_update_positive

If this operator is A = L + U D V^H, this hints D > 0 elementwise.

is_square

Return True/False depending on if this operator is square.

name

Name prepended to all ops created by this LinearOperator.

range_dimension

Dimension (in the sense of vector spaces) of the range of this operator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns M.

Returns:

Dimension object.

shape

TensorShape of this LinearOperator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns TensorShape([B1,...,Bb, M, N]), equivalent to A.get_shape().

Returns:

TensorShape, statically determined, may be undefined.

tensor_rank

Rank (in the sense of tensors) of matrix corresponding to this operator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns b + 2.

Args:

• name: A name for this Op.

Returns:

Python integer, or None if the tensor rank is undefined.

u

If this operator is A = L + U D V^H, this is the U.

v

If this operator is A = L + U D V^H, this is the V.

Methods

__init__

__init__(
base_operator,
u,
diag_update=None,
v=None,
is_diag_update_positive=None,
is_non_singular=None,
is_positive_definite=None,
is_square=None,
name='LinearOperatorLowRankUpdate'
)


Initialize a LinearOperatorLowRankUpdate.

This creates a LinearOperator of the form A = L + U D V^H, with L a LinearOperator, U, V both [batch] matrices, and D a [batch] diagonal matrix.

If L is non-singular, solves and determinants are available. Solves/determinants both involve a solve/determinant of a K x K system. In the event that L and D are self-adjoint positive-definite, and U = V, this can be done using a Cholesky factorization. The user should set the is_X matrix property hints, which will trigger the appropriate code path.

Args:

• base_operator: Shape [B1,...,Bb, M, N] real float16, float32 or float64 LinearOperator. This is L above.
• u: Shape [B1,...,Bb, M, K] Tensor of same dtype as base_operator. This is U above.
• diag_update: Optional shape [B1,...,Bb, K] Tensor with same dtype as base_operator. This is the diagonal of D above. Defaults to D being the identity operator.
• v: Optional Tensor of same dtype as u and shape [B1,...,Bb, N, K] Defaults to v = u, in which case the perturbation is symmetric. If M != N, then v must be set since the perturbation is not square.
• is_diag_update_positive: Python bool. If True, expect diag_update > 0.
• is_non_singular: Expect that this operator is non-singular. Default is None, unless is_positive_definite is auto-set to be True (see below).
• is_self_adjoint: Expect that this operator is equal to its hermitian transpose. Default is None, unless base_operator is self-adjoint and v = None (meaning u=v), in which case this defaults to True.
• is_positive_definite: Expect that this operator is positive definite. Default is None, unless base_operator is positive-definite v = None (meaning u=v), and is_diag_update_positive, in which case this defaults to True. Note that we say an operator is positive definite when the quadratic form x^H A x has positive real part for all nonzero x.
• is_square: Expect that this operator acts like square [batch] matrices.
• name: A name for this LinearOperator.

Raises:

• ValueError: If is_X flags are set in an inconsistent way.

add_to_tensor

add_to_tensor(
x,
)


Add matrix represented by this operator to x. Equivalent to A + x.

Args:

• x: Tensor with same dtype and shape broadcastable to self.shape.
• name: A name to give this Op.

Returns:

A Tensor with broadcast shape and same dtype as self.

assert_non_singular

assert_non_singular(name='assert_non_singular')


Returns an Op that asserts this operator is non singular.

This operator is considered non-singular if

ConditionNumber < max{100, range_dimension, domain_dimension} * eps,
eps := np.finfo(self.dtype.as_numpy_dtype).eps


Args:

• name: A string name to prepend to created ops.

Returns:

An Assert Op, that, when run, will raise an InvalidArgumentError if the operator is singular.

assert_positive_definite

assert_positive_definite(name='assert_positive_definite')


Returns an Op that asserts this operator is positive definite.

Here, positive definite means that the quadratic form x^H A x has positive real part for all nonzero x. Note that we do not require the operator to be self-adjoint to be positive definite.

Args:

• name: A name to give this Op.

Returns:

An Assert Op, that, when run, will raise an InvalidArgumentError if the operator is not positive definite.

assert_self_adjoint

assert_self_adjoint(name='assert_self_adjoint')


Returns an Op that asserts this operator is self-adjoint.

Here we check that this operator is exactly equal to its hermitian transpose.

Args:

• name: A string name to prepend to created ops.

Returns:

An Assert Op, that, when run, will raise an InvalidArgumentError if the operator is not self-adjoint.

batch_shape_tensor

batch_shape_tensor(name='batch_shape_tensor')


Shape of batch dimensions of this operator, determined at runtime.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns a Tensor holding [B1,...,Bb].

Args:

• name: A name for this Op.

Returns:

int32 Tensor

determinant

determinant(name='det')


Determinant for every batch member.

Args:

• name: A name for this Op.

Returns:

Tensor with shape self.batch_shape and same dtype as self.

Raises:

• NotImplementedError: If self.is_square is False.

diag_part

diag_part(name='diag_part')


Efficiently get the [batch] diagonal part of this operator.

If this operator has shape [B1,...,Bb, M, N], this returns a Tensor diagonal, of shape [B1,...,Bb, min(M, N)], where diagonal[b1,...,bb, i] = self.to_dense()[b1,...,bb, i, i].

my_operator = LinearOperatorDiag([1., 2.])

# Efficiently get the diagonal
my_operator.diag_part()
==> [1., 2.]

# Equivalent, but inefficient method
tf.matrix_diag_part(my_operator.to_dense())
==> [1., 2.]


Args:

• name: A name for this Op.

Returns:

• diag_part: A Tensor of same dtype as self.

domain_dimension_tensor

domain_dimension_tensor(name='domain_dimension_tensor')


Dimension (in the sense of vector spaces) of the domain of this operator.

Determined at runtime.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns N.

Args:

• name: A name for this Op.

Returns:

int32 Tensor

log_abs_determinant

log_abs_determinant(name='log_abs_det')


Log absolute value of determinant for every batch member.

Args:

• name: A name for this Op.

Returns:

Tensor with shape self.batch_shape and same dtype as self.

Raises:

• NotImplementedError: If self.is_square is False.

matmul

matmul(
x,
name='matmul'
)


Transform [batch] matrix x with left multiplication: x --> Ax.

# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

X = ... # shape [..., N, R], batch matrix, R > 0.

Y = operator.matmul(X)
Y.shape
==> [..., M, R]

Y[..., :, r] = sum_j A[..., :, j] X[j, r]


Args:

• x: Tensor with compatible shape and same dtype as self. See class docstring for definition of compatibility.
• adjoint: Python bool. If True, left multiply by the adjoint: A^H x.
• adjoint_arg: Python bool. If True, compute A x^H where x^H is the hermitian transpose (transposition and complex conjugation).
• name: A name for this Op.

Returns:

A Tensor with shape [..., M, R] and same dtype as self.

matvec

matvec(
x,
name='matvec'
)


Transform [batch] vector x with left multiplication: x --> Ax.

# Make an operator acting like batch matric A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)

X = ... # shape [..., N], batch vector

Y = operator.matvec(X)
Y.shape
==> [..., M]

Y[..., :] = sum_j A[..., :, j] X[..., j]


Args:

• x: Tensor with compatible shape and same dtype as self. x is treated as a [batch] vector meaning for every set of leading dimensions, the last dimension defines a vector. See class docstring for definition of compatibility.
• adjoint: Python bool. If True, left multiply by the adjoint: A^H x.
• name: A name for this Op.

Returns:

A Tensor with shape [..., M] and same dtype as self.

range_dimension_tensor

range_dimension_tensor(name='range_dimension_tensor')


Dimension (in the sense of vector spaces) of the range of this operator.

Determined at runtime.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns M.

Args:

• name: A name for this Op.

Returns:

int32 Tensor

shape_tensor

shape_tensor(name='shape_tensor')


Shape of this LinearOperator, determined at runtime.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns a Tensor holding [B1,...,Bb, M, N], equivalent to tf.shape(A).

Args:

• name: A name for this Op.

Returns:

int32 Tensor

solve

solve(
rhs,
name='solve'
)


Solve (exact or approx) R (batch) systems of equations: A X = rhs.

The returned Tensor will be close to an exact solution if A is well conditioned. Otherwise closeness will vary. See class docstring for details.

Examples:

# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

# Solve R > 0 linear systems for every member of the batch.
RHS = ... # shape [..., M, R]

X = operator.solve(RHS)
# X[..., :, r] is the solution to the r'th linear system
# sum_j A[..., :, j] X[..., j, r] = RHS[..., :, r]

operator.matmul(X)
==> RHS


Args:

• rhs: Tensor with same dtype as this operator and compatible shape. rhs is treated like a [batch] matrix meaning for every set of leading dimensions, the last two dimensions defines a matrix. See class docstring for definition of compatibility.
• adjoint: Python bool. If True, solve the system involving the adjoint of this LinearOperator: A^H X = rhs.
• adjoint_arg: Python bool. If True, solve A X = rhs^H where rhs^H is the hermitian transpose (transposition and complex conjugation).
• name: A name scope to use for ops added by this method.

Returns:

Tensor with shape [...,N, R] and same dtype as rhs.

Raises:

• NotImplementedError: If self.is_non_singular or is_square is False.

solvevec

solvevec(
rhs,
name='solve'
)


Solve single equation with best effort: A X = rhs.

The returned Tensor will be close to an exact solution if A is well conditioned. Otherwise closeness will vary. See class docstring for details.

Examples:

# Make an operator acting like batch matrix A.  Assume A.shape = [..., M, N]
operator = LinearOperator(...)
operator.shape = [..., M, N]

# Solve one linear system for every member of the batch.
RHS = ... # shape [..., M]

X = operator.solvevec(RHS)
# X is the solution to the linear system
# sum_j A[..., :, j] X[..., j] = RHS[..., :]

operator.matvec(X)
==> RHS


Args:

• rhs: Tensor with same dtype as this operator. rhs is treated like a [batch] vector meaning for every set of leading dimensions, the last dimension defines a vector. See class docstring for definition of compatibility regarding batch dimensions.
• adjoint: Python bool. If True, solve the system involving the adjoint of this LinearOperator: A^H X = rhs.
• name: A name scope to use for ops added by this method.

Returns:

Tensor with shape [...,N] and same dtype as rhs.

Raises:

• NotImplementedError: If self.is_non_singular or is_square is False.

tensor_rank_tensor

tensor_rank_tensor(name='tensor_rank_tensor')


Rank (in the sense of tensors) of matrix corresponding to this operator.

If this operator acts like the batch matrix A with A.shape = [B1,...,Bb, M, N], then this returns b + 2.

Args:

• name: A name for this Op.

Returns:

int32 Tensor, determined at runtime.

to_dense

to_dense(name='to_dense')


Return a dense (batch) matrix representing this operator.

trace

trace(name='trace')


Trace of the linear operator, equal to sum of self.diag_part().

If the operator is square, this is also the sum of the eigenvalues.

Args:

• name: A name for this Op.

Returns:

Shape [B1,...,Bb] Tensor of same dtype as self.