pymor.operators package¶

Submodules¶

block module¶

class pymor.operators.block.BlockColumnOperator(blocks)[source]

A column vector of arbitrary Operators.

Attributes

adjoint_type

alias of BlockRowOperator

class pymor.operators.block.BlockDiagonalOperator(blocks)[source]

Block diagonal Operator of arbitrary Operators.

This is a specialization of BlockOperator for the block diagonal case.

Attributes

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

class pymor.operators.block.BlockEmbeddingOperator(block_space, component)[source]

Attributes

class pymor.operators.block.BlockOperator(blocks)[source]

A matrix of arbitrary Operators.

This operator can be applied to a compatible BlockVectorArrays.

Parameters

blocks

Two-dimensional array-like where each entry is an Operator or None.

Attributes

adjoint_type

alias of BlockOperator

class pymor.operators.block.BlockOperatorBase(blocks)[source]
apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

class pymor.operators.block.BlockProjectionOperator(block_space, component)[source]

Attributes

class pymor.operators.block.BlockRowOperator(blocks)[source]

A row vector of arbitrary Operators.

Attributes

adjoint_type

alias of BlockColumnOperator

class pymor.operators.block.SecondOrderModelOperator(E, K)[source]

BlockOperator appearing in SecondOrderModel.to_lti().

This represents a block operator

$\begin{split}\mathcal{A} = \begin{bmatrix} 0 & I \\ -K & -E \end{bmatrix},\end{split}$

which satisfies

$\begin{split}\mathcal{A}^H &= \begin{bmatrix} 0 & -K^H \\ I & -E^H \end{bmatrix}, \\ \mathcal{A}^{-1} &= \begin{bmatrix} -K^{-1} E & -K^{-1} \\ I & 0 \end{bmatrix}, \\ \mathcal{A}^{-H} &= \begin{bmatrix} -E^H K^{-H} & I \\ -K^{-H} & 0 \end{bmatrix}.\end{split}$

Parameters

E
K

Attributes

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

class pymor.operators.block.ShiftedSecondOrderModelOperator(M, E, K, a, b)[source]

BlockOperator appearing in second-order systems.

This represents a block operator

$\begin{split}a \mathcal{E} + b \mathcal{A} = \begin{bmatrix} a I & b I \\ -b K & a M - b E \end{bmatrix},\end{split}$

which satisfies

$\begin{split}(a \mathcal{E} + b \mathcal{A})^H &= \begin{bmatrix} \overline{a} I & -\overline{b} K^H \\ \overline{b} I & \overline{a} M^H - \overline{b} E^H \end{bmatrix}, \\ (a \mathcal{E} + b \mathcal{A})^{-1} &= \begin{bmatrix} (a^2 M - a b E + b^2 K)^{-1} (a M - b E) & -b (a^2 M - a b E + b^2 K)^{-1} \\ b (a^2 M - a b E + b^2 K)^{-1} K & a (a^2 M - a b E + b^2 K)^{-1} \end{bmatrix}, \\ (a \mathcal{E} + b \mathcal{A})^{-H} &= \begin{bmatrix} (a M - b E)^H (a^2 M - a b E + b^2 K)^{-H} & \overline{b} K^H (a^2 M - a b E + b^2 K)^{-H} \\ -\overline{b} (a^2 M - a b E + b^2 K)^{-H} & \overline{a} (a^2 M - a b E + b^2 K)^{-H} \end{bmatrix}.\end{split}$

Parameters

M
E
K
a, b

Complex numbers.

Attributes

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

constructions module¶

Module containing some constructions to obtain new operators from old ones.

class pymor.operators.constructions.AdjointOperator(operator, source_product=None, range_product=None, name=None, with_apply_inverse=True, solver_options=None)[source]

Represents the adjoint of a given linear Operator.

For a linear Operator op the adjoint op^* of op is given by:

(op^*(v), u)_s = (v, op(u))_r,


where ( , )_s and ( , )_r denote the inner products on the source and range space of op. If two products are given by P_s and P_r, then:

op^*(v) = P_s^(-1) o op.H o P_r,


Thus, if ( , )_s and ( , )_r are the Euclidean inner products, op^*v is simply given by application of the :attr:adjoint <pymor.operators.interface.Operator.H> Operator.

Parameters

operator

The Operator of which the adjoint is formed.

source_product

If not None, inner product Operator for the source VectorSpace w.r.t. which to take the adjoint.

range_product

If not None, inner product Operator for the range VectorSpace w.r.t. which to take the adjoint.

name

If not None, name of the operator.

with_apply_inverse

If True, provide own apply_inverse and apply_inverse_adjoint implementations by calling these methods on the given operator. (Is set to False in the default implementation of and apply_inverse_adjoint.)

solver_options

When with_apply_inverse is False, the solver_options to use for the apply_inverse default implementation.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

class pymor.operators.constructions.AffineOperator(operator, name=None)[source]

Decompose an affine Operator into affine_shift and linear_part.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

class pymor.operators.constructions.ComponentProjection(components, source, name=None)[source]

Operator representing the projection of a VectorArray onto some of its components.

Parameters

components

List or 1D NumPy array of the indices of the vector components that are to be extracted by the operator.

source

Source VectorSpace of the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.Concatenation(operators, solver_options=None, name=None)[source]

Operator representing the concatenation of two Operators.

Parameters

operators

Tuple of Operators to concatenate. operators[-1] is the first applied operator, operators[0] is the last applied operator.

solver_options

The solver_options for the operator.

name

Name of the operator.

__matmul__(other)[source]

Concatenation of two operators.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.ConstantOperator(value, source, name=None)[source]

A constant Operator always returning the same vector.

Parameters

value

A VectorArray of length 1 containing the vector which is returned by the operator.

source

Source VectorSpace of the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.FixedParameterOperator(operator, mu=None, name=None)[source]

Makes an Operator Parameter-independent by setting a fixed Parameter.

Parameters

operator

The Operator to wrap.

mu

The fixed Parameter that will be fed to the apply method (and related methods) of operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

class pymor.operators.constructions.IdentityOperator(space, name=None)[source]

The identity Operator.

In other words:

op.apply(U) == U


Parameters

space

The VectorSpace the operator acts on.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.InducedNorm(product, raise_negative, tol, name)[source]

Instantiated by induced_norm. Do not use directly.

__call__(U, mu=None)[source]

Call self as a function.

class pymor.operators.constructions.InverseAdjointOperator(operator, name=None)[source]

Represents the inverse adjoint of a given Operator.

Parameters

operator

The Operator of which the inverse adjoint is formed.

name

If not None, name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

class pymor.operators.constructions.InverseOperator(operator, name=None)[source]

Represents the inverse of a given Operator.

Parameters

operator

The Operator of which the inverse is formed.

name

If not None, name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

class pymor.operators.constructions.LincombOperator(operators, coefficients, solver_options=None, name=None)[source]

Linear combination of arbitrary Operators.

This Operator represents a (possibly Parameter dependent) linear combination of a given list of Operators.

Parameters

operators

List of Operators whose linear combination is formed.

coefficients

A list of linear coefficients. A linear coefficient can either be a fixed number or a ParameterFunctional.

solver_options

The solver_options for the operator.

name

Name of the operator.

__add__(other)[source]

Sum of two operators.

__mul__(other)[source]

Product of operator by a scalar.

__radd__(other)[source]

Sum of two operators.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

If the operator is a linear operator given by multiplication with a matrix M, then apply2 is given as:

op.apply2(V, U) = V^T*M*U.


In the case of complex numbers, note that apply2 is anti-linear in the first variable by definition of dot.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V), len(U)) containing the 2-form evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

d_mu(component, index=())[source]

Return the operator’s derivative with respect to an index of a parameter component.

Parameters

component

Parameter component

index

index in the parameter component

Returns

New Operator representing the partial derivative.

evaluate_coefficients(mu)[source]

Compute the linear coefficients for a given Parameter.

Parameters

mu

Parameter for which to compute the linear coefficients.

Returns

List of linear coefficients.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

pairwise_apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

Same as Operator.apply2, except that vectors from V and U are applied in pairs.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V),) == (len(U),) containing the 2-form evaluations.

class pymor.operators.constructions.LinearOperator(operator, name=None)[source]

Mark the wrapped Operator to be linear.

class pymor.operators.constructions.LowRankOperator(left, core, right, inverted=False, solver_options=None, name=None)[source]

Non-parametric low-rank operator.

Represents an operator of the form $$L C R^H$$ or $$L C^{-1} R^H$$ where $$L$$ and $$R$$ are VectorArrays of column vectors and $$C$$ a 2D NumPy array.

Parameters

left

VectorArray representing $$L$$.

core

NumPy array representing $$C$$.

right

VectorArray representing $$R$$.

inverted

Whether $$C$$ is inverted.

solver_options

The solver_options for the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

class pymor.operators.constructions.LowRankUpdatedOperator(operator, lr_operator, coeff, lr_coeff, solver_options=None, name=None)[source]

Represents a linear combination of an Operator and LowRankOperator. Uses the Sherman-Morrison-Woodbury formula in apply_inverse and apply_inverse_adjoint:

$\begin{split}\left(\alpha A + \beta L C R^H \right)^{-1} & = \alpha^{-1} A^{-1} - \alpha^{-1} \beta A^{-1} L C \left(\alpha C + \beta C R^H A^{-1} L C \right)^{-1} C R^H A^{-1}, \\ \left(\alpha A + \beta L C^{-1} R^H \right)^{-1} & = \alpha^{-1} A^{-1} - \alpha^{-1} \beta A^{-1} L \left(\alpha C + \beta R^H A^{-1} L \right)^{-1} R^H A^{-1}.\end{split}$

Parameters

operator
lr_operator
coeff

A linear coefficient for operator. Can either be a fixed number or a ParameterFunctional.

lr_coeff

A linear coefficient for lr_operator. Can either be a fixed number or a ParameterFunctional.

solver_options

The solver_options for the operator.

name

Name of the operator.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

class pymor.operators.constructions.ProjectedOperator(operator, range_basis, source_basis, product=None, solver_options=None)[source]

Generic Operator representing the projection of an Operator to a subspace.

This operator is implemented as the concatenation of the linear combination with source_basis, application of the original operator and projection onto range_basis. As such, this operator can be used to obtain a reduced basis projection of any given Operator. However, no offline/online decomposition is performed, so this operator is mainly useful for testing before implementing offline/online decomposition for a specific application.

This operator is instantiated in pymor.algorithms.projection.project as a default implementation for parametric or nonlinear operators.

Parameters

operator

The Operator to project.

range_basis
source_basis
product
solver_options

The solver_options for the projected operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

class pymor.operators.constructions.ProxyOperator(operator, name=None)[source]

Forwards all interface calls to given Operator.

Mainly useful as base class for other Operator implementations.

Parameters

operator

The Operator to wrap.

name

Name of the wrapping operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.SelectionOperator(operators, parameter_functional, boundaries, name=None)[source]

An Operator selected from a list of Operators.

operators[i] is used if parameter_functional(mu) is less or equal than boundaries[i] and greater than boundaries[i-1]:

-infty ------- boundaries[i] ---------- boundaries[i+1] ------- infty
|                        |
--- operators[i] ---|---- operators[i+1] ----|---- operators[i+2]
|                        |


Parameters

operators

List of Operators from which one Operator is selected based on the given Parameter.

parameter_functional

The ParameterFunctional used for the selection of one Operator.

boundaries

The interval boundaries as defined above.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

class pymor.operators.constructions.VectorArrayOperator(array, adjoint=False, space_id=None, name=None)[source]

Wraps a VectorArray as an Operator.

If adjoint is False, the operator maps from NumpyVectorSpace(len(array)) to array.space by forming linear combinations of the vectors in the array with given coefficient arrays.

If adjoint == True, the operator maps from array.space to NumpyVectorSpace(len(array)) by forming the inner products of the argument with the vectors in the given array.

Parameters

array

The VectorArray which is to be treated as an operator.

See description above.

space_id

Id of the source (range) VectorSpace in case adjoint is False (True).

name

The name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

class pymor.operators.constructions.VectorFunctional(vector, product=None, name=None)[source]

Wrap a vector as a linear Functional.

Given a vector v of dimension d, this class represents the functional

f: R^d ----> R^1
u  |---> (u, v)


where ( , ) denotes the inner product given by product.

In particular, if product is None

VectorFunctional(vector).as_source_array() == vector.


If product is not none, we obtain

VectorFunctional(vector).as_source_array() == product.apply(vector).


Parameters

vector

VectorArray of length 1 containing the vector v.

product

Operator representing the scalar product to use.

name

Name of the operator.

class pymor.operators.constructions.VectorOperator(vector, name=None)[source]

Wrap a vector as a vector-like Operator.

Given a vector v of dimension d, this class represents the operator

op: R^1 ----> R^d
x  |---> x⋅v


In particular:

VectorOperator(vector).as_range_array() == vector


Parameters

vector

VectorArray of length 1 containing the vector v.

name

Name of the operator.

class pymor.operators.constructions.ZeroOperator(range, source, name=None)[source]

The Operator which maps every vector to zero.

Parameters

range

Range VectorSpace of the operator.

source

Source VectorSpace of the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

pymor.operators.constructions.induced_norm(product, raise_negative=True, tol=1e-10, name=None)[source]

Obtain induced norm of an inner product.

The norm of the vectors in a VectorArray U is calculated by calling

product.pairwise_apply2(U, U, mu=mu).


In addition, negative norm squares of absolute value smaller than tol are clipped to 0. If raise_negative is True, a ValueError exception is raised if there are negative norm squares of absolute value larger than tol.

Parameters

product

The inner product Operator for which the norm is to be calculated.

raise_negative

If True, raise an exception if calculated norm is negative.

tol

See above.

name

optional, if None product’s name is used

Returns

norm

A function norm(U, mu=None) taking a VectorArray U as input together with the Parameter mu which is passed to the product.

Defaults

raise_negative, tol (see pymor.core.defaults)

ei module¶

class pymor.operators.ei.EmpiricalInterpolatedOperator(operator, interpolation_dofs, collateral_basis, triangular, solver_options=None, name=None)[source]

Interpolate an Operator using Empirical Operator Interpolation.

Let L be an Operator, 0 <= c_1, ..., c_M < L.range.dim indices of interpolation DOFs and let b_1, ..., b_M in R^(L.range.dim) be collateral basis vectors. If moreover ψ_j(U) denotes the j-th component of U, the empirical interpolation L_EI of L w.r.t. the given data is given by

|                M
|   L_EI(U, μ) = ∑ b_i⋅λ_i     such that
|               i=1
|
|   ψ_(c_i)(L_EI(U, μ)) = ψ_(c_i)(L(U, μ))   for i=0,...,M


Since the original operator only has to be evaluated at the given interpolation DOFs, EmpiricalInterpolatedOperator calls restricted to obtain a restricted version of the operator which is used to quickly obtain the required evaluations. If the restricted method, is not implemented, the full operator will be evaluated (which will lead to the same result, but without any speedup).

The interpolation DOFs and the collateral basis can be generated using the algorithms provided in the pymor.algorithms.ei module.

Parameters

operator

The Operator to interpolate.

interpolation_dofs

List or 1D NumPy array of the interpolation DOFs c_1, ..., c_M.

collateral_basis

VectorArray containing the collateral basis b_1, ..., b_M.

triangular

If True, assume that ψ_(c_i)(b_j) = 0 for i < j, which means that the interpolation matrix is triangular.

solver_options

The solver_options for the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

class pymor.operators.ei.ProjectedEmpiciralInterpolatedOperator(restricted_operator, interpolation_matrix, source_basis_dofs, projected_collateral_basis, triangular, solver_options=None, name=None)[source]

A projected EmpiricalInterpolatedOperator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

interface module¶

class pymor.operators.interface.Operator[source]

Interface for Parameter dependent discrete operators.

An operator in pyMOR is simply a mapping which for any given Parameter maps vectors from its source VectorSpace to vectors in its range VectorSpace.

Note that there is no special distinction between functionals and operators in pyMOR. A functional is simply an operator with NumpyVectorSpace (1) as its range VectorSpace.

solver_options

If not None, a dict which can contain the following keys:

‘inverse’

solver options used for apply_inverse

solver options used for apply_inverse_adjoint

‘jacobian’

solver options for the operators returned by jacobian (has no effect for linear operators)

If solver_options is None or a dict entry is missing or None, default options are used. The interpretation of the given solver options is up to the operator at hand. In general, values in solver_options should either be strings (indicating a solver type) or dicts of options, usually with an entry 'type' which specifies the solver type to use and further items which configure this solver.

linear

True if the operator is linear.

source

The source VectorSpace.

range

The range VectorSpace.

H

self.H.apply(V, mu) == self.apply_adjoint(V, mu)


for all V, mu.

__add__(other)[source]

Sum of two operators.

__matmul__(other)[source]

Concatenation of two operators.

__mul__(other)[source]

Product of operator by a scalar.

__radd__(other)

Sum of two operators.

__str__()[source]

Return str(self).

abstract apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

If the operator is a linear operator given by multiplication with a matrix M, then apply2 is given as:

op.apply2(V, U) = V^T*M*U.


In the case of complex numbers, note that apply2 is anti-linear in the first variable by definition of dot.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V), len(U)) containing the 2-form evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_vector(mu=None)[source]

Return a vector representation of a linear functional or vector operator.

Depending on the operator’s source and range, this method is equivalent to calling as_range_array or as_source_array respectively. The resulting VectorArray is required to have length 1.

Parameters

mu

The Parameter for which to return the vector representation.

Returns

V

VectorArray of length 1 containing the vector representation.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

d_mu(component, index=())[source]

Return the operator’s derivative with respect to an index of a parameter component.

Parameters

component

Parameter component

index

index in the parameter component

Returns

New Operator representing the partial derivative.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

pairwise_apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

Same as Operator.apply2, except that vectors from V and U are applied in pairs.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V),) == (len(U),) containing the 2-form evaluations.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

list module¶

class pymor.operators.list.LinearComplexifiedListVectorArrayOperatorBase[source]

class pymor.operators.list.ListVectorArrayOperatorBase[source]
apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

mpi module¶

class pymor.operators.mpi.MPIOperator(obj_id, mpi_range, mpi_source, with_apply2=False, pickle_local_spaces=True, space_type=<class 'pymor.vectorarrays.mpi.MPIVectorSpace'>)[source]

MPI distributed Operator.

Given a single-rank implementation of an Operator, this wrapper class uses the event loop from pymor.tools.mpi to allow an MPI distributed usage of the Operator.

Instances of MPIOperator can be used on rank 0 like any other (non-distributed) Operator.

Note, however, that the underlying Operator implementation needs to be MPI aware. For instance, the operator’s apply method has to perform the necessary MPI communication to obtain all DOFs hosted on other MPI ranks which are required for the local operator evaluation.

Instead of instantiating MPIOperator directly, it is usually preferable to use mpi_wrap_operator instead.

Parameters

obj_id

ObjectId of the local Operators on each rank.

mpi_range

Set to True if the range of the Operator is MPI distributed.

mpi_source

Set to True if the source of the Operator is MPI distributed.

with_apply2

Set to True if the operator implementation has its own MPI aware implementation of apply2 and pairwise_apply2. Otherwise, the default implementations using apply and dot will be used.

pickle_local_spaces

If pickle_local_spaces is False, a unique identifier is computed for each local source/range VectorSpace, which is then transferred to rank 0 instead of the true VectorSpace. This allows the useage of MPIVectorArray even when the local VectorSpaces are not picklable.

space_type

This class will be used to wrap the local VectorArrays returned by the local operators into an MPI distributed VectorArray managed from rank 0. By default, MPIVectorSpace will be used, other options are MPIVectorSpaceAutoComm and MPIVectorSpaceNoComm.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

If the operator is a linear operator given by multiplication with a matrix M, then apply2 is given as:

op.apply2(V, U) = V^T*M*U.


In the case of complex numbers, note that apply2 is anti-linear in the first variable by definition of dot.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V), len(U)) containing the 2-form evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

apply_inverse_adjoint(U, mu=None, least_squares=False)[source]

Parameters

U

VectorArray of vectors to which the inverse adjoint operator is applied.

mu

The Parameter for which to evaluate the inverse adjoint operator.

least_squares

If True, solve the least squares problem:

v = argmin ||op^*(v) - u||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most operator implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse adjoint operator evaluations.

Raises

InversionError

The operator could not be inverted.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

jacobian(U, mu=None)[source]

Return the operator’s Jacobian as a new Operator.

Parameters

U

Length 1 VectorArray containing the vector for which to compute the Jacobian.

mu

The Parameter for which to compute the Jacobian.

Returns

Linear Operator representing the Jacobian.

pairwise_apply2(V, U, mu=None)[source]

Treat the operator as a 2-form by computing V.dot(self.apply(U)).

Same as Operator.apply2, except that vectors from V and U are applied in pairs.

Parameters

V

VectorArray of the left arguments V.

U

VectorArray of the right right arguments U.

mu

The Parameter for which to evaluate the operator.

Returns

A NumPy array with shape (len(V),) == (len(U),) containing the 2-form evaluations.

restricted(dofs)[source]

Restrict the operator range to a given set of degrees of freedom.

This method returns a restricted version restricted_op of the operator along with an array source_dofs such that for any VectorArray U in self.source the following is true:

self.apply(U, mu).dofs(dofs)
== restricted_op.apply(NumpyVectorArray(U.dofs(source_dofs)), mu))


Such an operator is mainly useful for empirical interpolation where the evaluation of the original operator only needs to be known for few selected degrees of freedom. If the operator has a small stencil, only few source_dofs will be needed to evaluate the restricted operator which can make its evaluation very fast compared to evaluating the original operator.

Parameters

dofs

One-dimensional NumPy array of degrees of freedom in the operator range to which to restrict.

Returns

restricted_op

The restricted operator as defined above. The operator will have NumpyVectorSpace (len(source_dofs)) as source and NumpyVectorSpace (len(dofs)) as range.

source_dofs

One-dimensional NumPy array of source degrees of freedom as defined above.

pymor.operators.mpi.mpi_wrap_operator(obj_id, mpi_range, mpi_source, with_apply2=False, pickle_local_spaces=True, space_type=<class 'pymor.vectorarrays.mpi.MPIVectorSpace'>)[source]

Wrap MPI distributed local Operators to a global Operator on rank 0.

Given MPI distributed local Operators referred to by the ObjectId obj_id, return a new Operator which manages these distributed operators from rank 0. This is done by instantiating MPIOperator. Additionally, the structure of the wrapped operators is preserved. E.g. LincombOperators will be wrapped as a LincombOperator of MPIOperators.

Parameters

See : class:MPIOperator.

Returns

The wrapped Operator.

numpy module¶

This module provides the following NumPy based Operators:

class pymor.operators.numpy.NumpyGenericOperator(mapping, adjoint_mapping=None, dim_source=1, dim_range=1, linear=False, parameter_type=None, source_id=None, range_id=None, solver_options=None, name=None)[source]

Wraps an arbitrary Python function between NumPy arrays as an Operator.

Parameters

mapping

The function to wrap. If parameter_type is None, the function is of the form mapping(U) and is expected to be vectorized. In particular:

mapping(U).shape == U.shape[:-1] + (dim_range,).


If parameter_type is not None, the function has to have the signature mapping(U, mu).

The adjoint function to wrap. If parameter_type is None, the function is of the form adjoint_mapping(U) and is expected to be vectorized. In particular:

adjoint_mapping(U).shape == U.shape[:-1] + (dim_source,).


If parameter_type is not None, the function has to have the signature adjoint_mapping(U, mu).

dim_source

Dimension of the operator’s source.

dim_range

Dimension of the operator’s range.

linear

Set to True if the provided mapping and adjoint_mapping are linear.

parameter_type

The ParameterType of the Parameters the mapping accepts.

solver_options

The solver_options for the operator.

name

Name of the operator.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

class pymor.operators.numpy.NumpyMatrixBasedOperator[source]

Base class for operators which assemble into a NumpyMatrixOperator.

sparse

True if the operator assembles into a sparse matrix, False if the operator assembles into a dense matrix, None if unknown.

apply(U, mu=None)[source]

Apply the operator to a VectorArray.

Parameters

U

VectorArray of vectors to which the operator is applied.

mu

The Parameter for which to evaluate the operator.

Returns

VectorArray of the operator evaluations.

apply_adjoint(V, mu=None)[source]

For any given linear Operator op, Parameter mu and VectorArrays U, V in the source resp. range we have:

op.apply_adjoint(V, mu).dot(U) == V.dot(op.apply(U, mu))


Thus, when op is represented by a matrix M, apply_adjoint is given by left-multplication of (the complex conjugate of) M with V.

Parameters

V

VectorArray of vectors to which the adjoint operator is applied.

mu

The Parameter for which to apply the adjoint operator.

Returns

VectorArray of the adjoint operator evaluations.

apply_inverse(V, mu=None, least_squares=False)[source]

Apply the inverse operator.

Parameters

V

VectorArray of vectors to which the inverse operator is applied.

mu

The Parameter for which to evaluate the inverse operator.

least_squares

If True, solve the least squares problem:

u = argmin ||op(u) - v||_2.


Since for an invertible operator the least squares solution agrees with the result of the application of the inverse operator, setting this option should, in general, have no effect on the result for those operators. However, note that when no appropriate solver_options are set for the operator, most implementations will choose a least squares solver by default which may be undesirable.

Returns

VectorArray of the inverse operator evaluations.

Raises

InversionError

The operator could not be inverted.

as_range_array(mu=None)[source]

Return a VectorArray representation of the operator in its range space.

In the case of a linear operator with NumpyVectorSpace as source, this method returns for every Parameter mu a VectorArray V in the operator’s range, such that

V.lincomb(U.to_numpy()) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

as_source_array(mu=None)[source]

Return a VectorArray representation of the operator in its source space.

In the case of a linear operator with NumpyVectorSpace as range, this method returns for every Parameter mu a VectorArray V in the operator’s source, such that

self.range.make_array(V.dot(U).T) == self.apply(U, mu)


for all VectorArrays U.

Parameters

mu

The Parameter for which to return the VectorArray representation.

Returns

V

The VectorArray defined above.

assemble(mu=None)[source]

Assemble the operator for a given parameter.

The result of the method strongly depends on the given operator. For instance, a matrix-based operator will assemble its matrix, a LincombOperator will try to form the linear combination of its operators, whereas an arbitrary operator might simply return a FixedParameterOperator. The only assured property of the assembled operator is that it no longer depends on a Parameter.

Parameters

mu

The Parameter for which to assemble the operator.

Returns

Parameter-independent, assembled Operator.

export_matrix(filename, matrix_name=None, output_format='matlab', mu=None)[source]

Save the matrix of the operator to a file.

Parameters

filename

Name of output file.

matrix_name

The name, the output matrix is given. (Comment field is used in case of Matrix Market output_format.) If None, the Operator’s name is used.

output_format

Output file format. Either matlab or matrixmarket.

mu

The Parameter to assemble the to be exported matrix for.

class pymor.operators.numpy.NumpyMatrixOperator(matrix, source_id=None, range_id=None, solver_options=None, name=None)[source]

Wraps a 2D NumPy array as an Operator.

Parameters

matrix

The NumPy array which is to be wrapped.

source_id

The id of the operator’s source VectorSpace.

range_id

The id of the operator’s range VectorSpace.

solver_options

The solver_options` for the operator.

name

Name of the operator.