LostTech.TensorFlow : API Documentation

Type LinearOperatorFullMatrix

Namespace tensorflow.linalg

Parent LinearOperator

Interfaces ILinearOperatorFullMatrix

`LinearOperator` that wraps a [batch] matrix.

This operator wraps a [batch] matrix `A` (which is a `Tensor`) 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. #### 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

`LinearOperatorFullMatrix` has exactly the same performance as would be achieved by using standard `TensorFlow` matrix ops. Intelligent choices are made based on the following initialization hints.

* If `dtype` is real, and `is_self_adjoint` and `is_positive_definite`, a Cholesky factorization is used for the determinant and solve.

In all cases, suppose `operator` is a `LinearOperatorFullMatrix` of shape `[M, N]`, and `x.shape = [N, R]`. Then

* `operator.matmul(x)` is `O(M * N * R)`. * If `M=N`, `operator.solve(x)` is `O(N^3 * R)`. * If `M=N`, `operator.determinant()` is `O(N^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, 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.
Show Example
# Create a 2 x 2 linear operator.
            matrix = [[1., 2.], [3., 4.]]
            operator = LinearOperatorFullMatrix(matrix) 

operator.to_dense() ==> [[1., 2.] [3., 4.]]

operator.shape ==> [2, 2]

operator.log_abs_determinant() ==> scalar Tensor

x =... Shape [2, 4] Tensor operator.matmul(x) ==> Shape [2, 4] Tensor

# Create a [2, 3] batch of 4 x 4 linear operators. matrix = tf.random.normal(shape=[2, 3, 4, 4]) operator = LinearOperatorFullMatrix(matrix)


Public properties

object batch_shape get;

object batch_shape_dyn get;

Dimension domain_dimension get;

object domain_dimension_dyn get;

object dtype get;

object dtype_dyn get;

IList<object> graph_parents get;

object graph_parents_dyn get;

Nullable<bool> is_non_singular get;

object is_non_singular_dyn get;

object is_positive_definite get;

object is_positive_definite_dyn get;

object is_self_adjoint get;

object is_self_adjoint_dyn get;

Nullable<bool> is_square get;

object is_square_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

object PythonObject get;

Dimension range_dimension get;

object range_dimension_dyn get;

TensorShape shape get;

object shape_dyn get;

ValueTuple<object> submodules get;

object submodules_dyn get;

Nullable<int> tensor_rank get;

object tensor_rank_dyn get;

object trainable_variables get;

object trainable_variables_dyn get;

object variables get;

object variables_dyn get;