Struct OdeCallbacksOut 

pub struct OdeCallbacksOut<'a, M: Matrix>(/* private fields */);

The output function of an OdeCallbacks.

Trait Implementations

impl<M: Matrix> NonLinearOp for OdeCallbacksOut<'_, M>

fn call_inplace(&self, x: &M::V, t: M::T, y: &mut M::V)

Compute the operator F(x, t) at a given state and time.

fn call(&self, x: &Self::V, t: Self::T) -> Self::V

Compute the operator F(x, t) at a given state and time, and return the result. Use [Self::call_inplace] to for a non-allocating version.

impl<M: Matrix> NonLinearOpAdjoint for OdeCallbacksOut<'_, M>

fn jac_transpose_mul_inplace(&self, x: &M::V, t: M::T, v: &M::V, y: &mut M::V)

Compute the product of the transpose of the Jacobian with a given vector -J(x, t)^T * v. The default implementation fails with a panic, as this method is not implemented by default and should be implemented by the user if needed.

fn adjoint_inplace(&self, x: &M::V, t: M::T, y: &mut M)

Compute the Adjoint matrix -J^T(x, t) of the operator and store it in the matrix y. y should have been previously initialised using the output of Self::adjoint_sparsity. The default implementation of this method computes the Jacobian using Self::jac_transpose_mul_inplace, but it can be overriden for more efficient implementations.

fn adjoint_sparsity(&self) -> Option<M::Sparsity>

Return sparsity information (if available)

fn _default_adjoint_inplace(&self, x: &Self::V, t: Self::T, y: &mut Self::M)

Default implementation of the Adjoint computation (this is the default for Self::adjoint_inplace).

fn adjoint(&self, x: &Self::V, t: Self::T) -> Self::M

Compute the Adjoint matrix -J^T(x, t) of the operator and return it. See Self::adjoint_inplace for a non-allocating version.

impl<M: Matrix> NonLinearOpJacobian for OdeCallbacksOut<'_, M>

fn jac_mul_inplace(&self, x: &M::V, t: M::T, v: &M::V, y: &mut M::V)

Compute the product of the Jacobian with a given vector J(x, t) * v.

fn jacobian_inplace(&self, x: &M::V, t: M::T, y: &mut M)

Compute the Jacobian matrix J(x, t) of the operator and store it in the matrix y. y should have been previously initialised using the output of Self::jacobian_sparsity. The default implementation of this method computes the Jacobian using Self::jac_mul_inplace, but it can be overriden for more efficient implementations.

fn jacobian_sparsity(&self) -> Option<M::Sparsity>

Return sparsity information (if available)

fn jac_mul(&self, x: &Self::V, t: Self::T, v: &Self::V) -> Self::V

Compute the product of the Jacobian with a given vector J(x, t) * v, and return the result. Use [Self::jac_mul_inplace] to for a non-allocating version.

fn jacobian(&self, x: &Self::V, t: Self::T) -> Self::M

Compute the Jacobian matrix J(x, t) of the operator and return it. See Self::jacobian_inplace for a non-allocating version.

fn _default_jacobian_inplace(&self, x: &Self::V, t: Self::T, y: &mut Self::M)

Default implementation of the Jacobian computation (this is the default for Self::jacobian_inplace).

impl<M: Matrix> NonLinearOpSens for OdeCallbacksOut<'_, M>

fn sens_mul_inplace(&self, x: &M::V, t: M::T, v: &M::V, y: &mut M::V)

Compute the product of the gradient of F wrt a parameter vector p with a given vector J_p(x, t) * v. Note that the vector v is of size nparams() and the result is of size nstates().

fn sens_inplace(&self, x: &M::V, t: M::T, y: &mut M)

Compute the gradient of the operator wrt a parameter vector p and store it in the matrix y. y should have been previously initialised using the output of Self::sens_sparsity. The default implementation of this method computes the gradient using Self::sens_mul_inplace, but it can be overriden for more efficient implementations.

fn sens_sparsity(&self) -> Option<M::Sparsity>

fn sens_mul(&self, x: &Self::V, t: Self::T, v: &Self::V) -> Self::V

Compute the product of the partial gradient of F wrt a parameter vector p with a given vector \parial F/\partial p(x, t) * v, and return the result. Use [Self::sens_mul_inplace] to for a non-allocating version.

fn _default_sens_inplace(&self, x: &Self::V, t: Self::T, y: &mut Self::M)

Default implementation of the gradient computation (this is the default for Self::sens_inplace).

fn sens(&self, x: &Self::V, t: Self::T) -> Self::M

Compute the gradient of the operator wrt a parameter vector p and return it. See Self::sens_inplace for a non-allocating version.

impl<M: Matrix> NonLinearOpSensAdjoint for OdeCallbacksOut<'_, M>

fn sens_transpose_mul_inplace(&self, x: &M::V, t: M::T, v: &M::V, y: &mut M::V)

Compute the product of the negative tramspose of the gradient of F wrt a parameter vector p with a given vector -J_p(x, t)^T * v.

fn sens_adjoint_inplace(&self, x: &M::V, t: M::T, y: &mut M)

Compute the negative transpose of the gradient of the operator wrt a parameter vector p and store it in the matrix y. y should have been previously initialised using the output of Self::sens_adjoint_sparsity. The default implementation of this method computes the gradient using Self::sens_transpose_mul_inplace, but it can be overriden for more efficient implementations.

fn sens_adjoint_sparsity(&self) -> Option<M::Sparsity>

fn sens_adjoint(&self, x: &Self::V, t: Self::T) -> Self::M

Compute the negative transpose of the gradient of the operator wrt a parameter vector p and return it. See Self::sens_adjoint_inplace for a non-allocating version.

fn _default_sens_adjoint_inplace( &self, x: &Self::V, t: Self::T, y: &mut Self::M, )

Default implementation of the gradient computation (this is the default for Self::sens_adjoint_inplace).

impl<M: Matrix> Op for OdeCallbacksOut<'_, M>

type T = <M as MatrixCommon>::T

type V = <M as MatrixCommon>::V

type M = M

type C = <M as MatrixCommon>::C

fn context(&self) -> &Self::C

return the context of the operator

fn nstates(&self) -> usize

Return the number of input states of the operator.

fn nout(&self) -> usize

Return the number of outputs of the operator.

fn nparams(&self) -> usize

Return the number of parameters of the operator.

fn statistics(&self) -> OpStatistics

Return statistics about the operator (e.g. how many times it was called, how many times the jacobian was computed, etc.)