Struct OdeCallbacksInit 

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

The initial condition of an OdeCallbacks.

Trait Implementations

impl<M: Matrix> ConstantOp for OdeCallbacksInit<'_, M>

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

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

impl<M: Matrix> ConstantOpSens for OdeCallbacksInit<'_, M>

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

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

fn sens(&self, 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> ConstantOpSensAdjoint for OdeCallbacksInit<'_, M>

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

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

fn sens_adjoint(&self, 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 sens_adjoint_inplace(&self, t: Self::T, y: &mut Self::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 _default_sens_adjoint_inplace(&self, t: Self::T, y: &mut Self::M)

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

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

impl<M: Matrix> Op for OdeCallbacksInit<'_, 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.)