boundlab.linearop.ComposedOp#

class boundlab.linearop.ComposedOp[source]#

Bases: LinearOp

Composition of two LinearOps: (outer ∘ inner)(x) = outer(inner(x)).

Methods

`__init__`	Initialize a LinearOp wrapper.
`abs`	Return a LinearOp representing the element-wise absolute value of this LinearOp.
`backward`	Apply the transposed linear function to an input tensor.
`einsum_op`	Materialize this LinearOp as an explicit Jacobian tensor.
`force_jacobian`	Materialize Jacobian via batched forward/backward application.
`forward`	Apply the original linear function to an input tensor.
`jacobian`	Return an explicit Jacobian tensor when efficiently available.
`jacobian_scatter`	Add this operator's Jacobian contribution into an existing tensor.
`norm_input`	Return a LinearOp that computes the norm over the input dimensions, if supported.
`norm_output`	Return a LinearOp that computes the norm over the output dimensions, if supported.
`purify`
`purify_with`	Materialize as EinsumOp and delegate to EinsumOp.purify_with.
`vbackward`	Apply the transposed linear function to an input tensor, supporting additional leading dimensions for batching.
`vforward`	Apply the original linear function to an input tensor, supporting additional trailing dimensions for batching.

__init__(*ops)[source]#

Initialize a LinearOp wrapper.

Parameters:

backward(grad)[source]#: Apply the transposed linear function to an input tensor.

vforward(x)[source]#: Apply the original linear function to an input tensor, supporting additional trailing dimensions for batching.

vbackward(grad)[source]#: Apply the transposed linear function to an input tensor, supporting additional leading dimensions for batching.

jacobian()[source]#

Return an explicit Jacobian tensor when efficiently available.

Returns:: A tensor with shape [*output_shape, *input_shape] if the concrete Jacobian can be produced directly. Returns NotImplemented for operators that only support implicit application.

norm_input(p=1)[source]#: Return a LinearOp that computes the norm over the input dimensions, if supported.

norm_output(p=1)[source]#: Return a LinearOp that computes the norm over the output dimensions, if supported.

purify_with(other)[source]#: Materialize as EinsumOp and delegate to EinsumOp.purify_with.

__add__(other)#

Add this LinearOp to another.

__mul__(other)#

Scale this LinearOp by a scalar factor.

abs()#

Return a LinearOp representing the element-wise absolute value of this LinearOp.

einsum_op()#

Materialize this LinearOp as an explicit Jacobian tensor.

Returns:: A tensor with shape [*output_shape, *input_shape] representing the Jacobian of this LinearOp.
Return type:: EinsumOp

Notes

This may be expensive in time and memory and is mainly intended for debugging, validation, or rare paths that require explicit Jacobians.

force_jacobian()#

Materialize Jacobian via batched forward/backward application.

This fallback constructs an identity basis and applies either vforward() or vbackward() depending on whether the input or output side is smaller.

Returns:: A dense Jacobian tensor with shape [*output_shape, *input_shape].

Notes

This may be expensive in time and memory and is mainly intended for debugging, validation, or rare paths that require explicit Jacobians.

jacobian_scatter(src)#

Add this operator’s Jacobian contribution into an existing tensor.

Parameters:: src (torch.Tensor) – A tensor with Jacobian layout [*output_shape, *input_shape] that acts as the accumulation buffer.
Returns:: A tensor with the same shape as src containing src + jacobian(self).
Return type:: torch.Tensor

Notes

Subclasses may override this to implement structured/sparse updates without materializing the full Jacobian first.