mrpro.operators.EinsumOp

class mrpro.operators.EinsumOp[source]

Bases: LinearOperator

A Linear Operator that implements sum products in Einstein notation.

Implements \(A_{\mathrm{indices}_A}*x^{\mathrm{indices}_x} = y_{\mathrm{indices}_y}\) with Einstein summation rules over the \(indices\), see torch.einsum or einops.einsum for more information. Note, that the indices must be space separated (einops convention).

It can be used to implement tensor contractions, such as for example, different versions of matrix-vector or matrix-matrix products of the form A @ x, depending on the chosen einsum rules and shapes of A and x.

Examples are:

• matrix-vector multiplication of \(A\) and the batched vector \(x = [x_1, ..., x_N]\) consisting of \(N\) vectors \(x_1, x_2, ..., x_N\). Then, the operation defined by \(A @ x := \mathrm{diag}(A, A, ..., A) [x_1, x_2, ..., x_N]^T\) = \([A x_1, A x_2, ..., A x_N]^T\) can be implemented by the einsum rule 'i j, ... j -> ... i'.

• matrix-vector multiplication of a matrix \(A\) consisting of \(N\) different matrices \(A_1, A_2, ... A_N\) with one vector \(x\). Then, the operation defined by \(A @ x := \mathrm{diag}(A_1, A_2,..., A_N) [x, x, ..., x]^T\) can be implemented by the einsum rule '... i j, j -> ... i'.

• matrix-vector multiplication of a matrix \(A\) consisting of \(N\) different matrices \(A_1, A_2, ... A_N\) with a vector \(x = [x_1,...,x_N]\) consisting of \(N\) vectors \(x_1, x_2, ..., x_N\). Then, the operation defined by \(A @ x := \mathrm{diag}(A_1, A_2,..., A_N) [x_1, x_2, ..., x_N]^T\) can be implemented by the einsum rule '... i j, ... j -> ... i'. This is the default behavior of the operator.

__init__(matrix: Tensor, einsum_rule: str = '... i j, ... j -> ... i') → None[source]

Initialize Einsum Operator.

Parameters:

• matrix (Tensor) – Matrix \(A\) to be used as first factor in the sum product \(A*x\)

• einsum_rule (str, default: '... i j, ... j -> ... i') – Einstein summation rule describing the forward of the operator. Also see torch.einsum for more information.

property H: LinearOperator[source]

Adjoint operator.

Obtains the adjoint of an instance of this operator as an AdjointLinearOperator, which itself is a an LinearOperator that can be applied to tensors.

Note: linear_operator.H.H == linear_operator

property gram: LinearOperator[source]

Gram operator.

For a LinearOperator \(A\), the self-adjoint Gram operator is defined as \(A^H A\).

Note

This is the inherited default implementation.

__call__(x: Tensor) → tuple[Tensor][source]

Apply sum-product of input x with the operator’s matrix A.

\(A\) and the rule used to perform the sum-product is set at initialization.

Parameters:

x (Tensor) – Input tensor.

Returns:

Result of the sum-product operation.

adjoint(y: Tensor) → tuple[Tensor][source]

Multiplication of input with the adjoint of \(A\).

Parameters:

y (Tensor) – Tensor to be multiplied with hermitian/adjoint matrix \(A\)

Returns:

Result of the adjoint sum-product operation.

forward(x: Tensor) → tuple[Tensor][source]

Apply forward of EinsumOp.

Note

Prefer calling the instance of the EinsumOp operator as operator(x) over directly calling this method. See this PyTorch discussion.

operator_norm(initial_value: Tensor, dim: Sequence[int] | None, max_iterations: int = 20, relative_tolerance: float = 1e-4, absolute_tolerance: float = 1e-5, callback: Callable[[Tensor], None] | None = None) → Tensor[source]

Power iteration for computing the operator norm of the operator.

Parameters:

• initial_value (Tensor) – initial value to start the iteration; must be element of the domain. if the initial value contains a zero-vector for one of the considered problems, the function throws an ValueError.

• dim (Sequence[int] | None) –

  The dimensions of the tensors on which the operator operates. The choice of dim determines how the operator norm is inperpreted. For example, for a matrix-vector multiplication with a batched matrix tensor of shape (batch1, batch2, row, column) and a batched input tensor of shape (batch1, batch2, row):

  • If dim=None, the operator is considered as a block diagonal matrix with batch1*batch2 blocks and the result is a tensor containing a single norm value (shape (1, 1, 1)).

  • If dim=(-1), batch1*batch2 matrices are considered, and for each a separate operator norm is computed.

  • If dim=(-2,-1), batch1 matrices with batch2 blocks are considered, and for each matrix a separate operator norm is computed.

  Thus, the choice of dim determines implicitly determines the domain of the operator.

• max_iterations (int, default: 20) – maximum number of iterations

• relative_tolerance (float, default: 1e-4) – absolute tolerance for the change of the operator-norm at each iteration; if set to zero, the maximal number of iterations is the only stopping criterion used to stop the power iteration.

• absolute_tolerance (float, default: 1e-5) – absolute tolerance for the change of the operator-norm at each iteration; if set to zero, the maximal number of iterations is the only stopping criterion used to stop the power iteration.

• callback (Callable[[Tensor], None] | None, default: None) – user-provided function to be called at each iteration

Returns:

An estimaton of the operator norm. Shape corresponds to the shape of the input tensor initial_value with the dimensions specified in dim reduced to a single value. The pointwise multiplication of initial_value with the result of the operator norm will always be well-defined.

__add__(other: LinearOperator | Tensor | complex) → LinearOperator[source]

__add__(other: Operator[Tensor, tuple[Tensor]]) → Operator[Tensor, tuple[Tensor]]

Operator addition.

Returns lambda x: self(x) + other(x) if other is a operator, lambda x: self(x) + other if other is a tensor

__and__(other: LinearOperator) → LinearOperatorMatrix[source]

Vertical stacking of two LinearOperators.

A&B is a LinearOperatorMatrix with two rows, with (A&B)(x) == (A(x), B(x)). See mrpro.operators.LinearOperatorMatrix for more information.

__matmul__(other: LinearOperator) → LinearOperator[source]

__matmul__(other: Operator[Unpack[Tin2], tuple[Tensor]] | Operator[Unpack[Tin2], tuple[Tensor, ...]]) → Operator[Unpack[Tin2], tuple[Tensor]]

Operator composition.

Returns lambda x: self(other(x))

__mul__(other: Tensor | complex) → LinearOperator[source]

Operator elementwise left multiplication with tensor/scalar.

Returns lambda x: self(x*other)

__or__(other: LinearOperator) → LinearOperatorMatrix[source]

Horizontal stacking of two LinearOperators.

A|B is a LinearOperatorMatrix with two columns, with (A|B)(x1,x2) == A(x1) + B(x2). See mrpro.operators.LinearOperatorMatrix for more information.

__radd__(other: Tensor | complex) → LinearOperator[source]

Operator addition.

Returns lambda x: self(x) + other*x

__rmul__(other: Tensor | complex) → LinearOperator[source]

Operator elementwise right multiplication with tensor/scalar.

Returns lambda x: other*self(x)