Struct Sdirk

pub struct Sdirk<'a, Eqn, LS, M = <<Eqn as Op>::V as DefaultDenseMatrix>::M, AugmentedEqn = NoAug<Eqn>>
where
    M: DenseMatrix<T = Eqn::T, V = Eqn::V, C = Eqn::C>,
    LS: LinearSolver<Eqn::M>,
    Eqn: OdeEquationsImplicit,
    Eqn::V: DefaultDenseMatrix<T = Eqn::T, C = Eqn::C>,
    AugmentedEqn: AugmentedOdeEquations<Eqn>,
{ /* private fields */ }

A singly diagonally implicit Runge-Kutta method. Can optionally have an explicit first stage for ESDIRK methods.

The particular method is defined by the Tableau used to create the solver. If the beta matrix of the Tableau is present this is used for interpolation, otherwise hermite interpolation is used.

Restrictions:

• The upper triangular part of the a matrix must be zero (i.e. not fully implicit).
• The diagonal of the a matrix must be the same non-zero value for all rows (i.e. an SDIRK method), except for the first row which can be zero for ESDIRK methods.
• The last row of the a matrix must be the same as the b vector, and the last element of the c vector must be 1 (i.e. a stiffly accurate method)

Implementations

impl<'a, M, Eqn, LS, AugmentedEqn> Sdirk<'a, Eqn, LS, M, AugmentedEqn>

where LS: LinearSolver<Eqn::M>, M: DenseMatrix<T = Eqn::T, V = Eqn::V, C = Eqn::C>, Eqn: OdeEquationsImplicit, Eqn::V: DefaultDenseMatrix<T = Eqn::T, C = Eqn::C>, AugmentedEqn: AugmentedOdeEquationsImplicit<Eqn>,

pub fn new( problem: &'a OdeSolverProblem<Eqn>, state: RkState<Eqn::V>, tableau: Tableau<M>, linear_solver: LS, ) -> Result<Self, DiffsolError>

pub fn new_augmented( problem: &'a OdeSolverProblem<Eqn>, state: RkState<Eqn::V>, tableau: Tableau<M>, linear_solver: LS, augmented_eqn: AugmentedEqn, ) -> Result<Self, DiffsolError>

pub fn get_statistics(&self) -> &BdfStatistics

Trait Implementations

impl<'a, M, Eqn, LS, AugEqn> AugmentedOdeSolverMethod<'a, Eqn, AugEqn> for Sdirk<'a, Eqn, LS, M, AugEqn>

where Eqn: OdeEquationsImplicit, AugEqn: AugmentedOdeEquationsImplicit<Eqn>, M: DenseMatrix<T = Eqn::T, V = Eqn::V, C = Eqn::C>, LS: LinearSolver<Eqn::M>, Eqn::V: DefaultDenseMatrix<T = Eqn::T, C = Eqn::C>, for<'b> &'b Eqn::V: VectorRef<Eqn::V>, for<'b> &'b Eqn::M: MatrixRef<Eqn::M>,

fn into_state_and_eqn(self) -> (Self::State, Option<AugEqn>)

fn augmented_eqn(&self) -> Option<&AugEqn>

impl<M, Eqn, LS, AugmentedEqn> Clone for Sdirk<'_, Eqn, LS, M, AugmentedEqn>

where M: DenseMatrix<T = Eqn::T, V = Eqn::V, C = Eqn::C>, LS: LinearSolver<Eqn::M>, Eqn: OdeEquationsImplicit, Eqn::V: DefaultDenseMatrix<T = Eqn::T, C = Eqn::C>, AugmentedEqn: AugmentedOdeEquationsImplicit<Eqn>,

fn clone(&self) -> Self

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a, M, Eqn, AugmentedEqn, LS> OdeSolverMethod<'a, Eqn> for Sdirk<'a, Eqn, LS, M, AugmentedEqn>

where LS: LinearSolver<Eqn::M>, M: DenseMatrix<T = Eqn::T, V = Eqn::V, C = Eqn::C>, Eqn: OdeEquationsImplicit, Eqn::V: DefaultDenseMatrix<T = Eqn::T, C = Eqn::C>, AugmentedEqn: AugmentedOdeEquationsImplicit<Eqn>,

type State = RkState<<Eqn as Op>::V>

fn problem(&self) -> &'a OdeSolverProblem<Eqn>

Get the current problem

fn jacobian(&self) -> Option<Ref<'_, Eqn::M>>

Returns the current jacobian matrix of the solver, if it has one Note that this will force a full recalculation of the Jacobian.

fn mass(&self) -> Option<Ref<'_, Eqn::M>>

Returns the current mass matrix of the solver, if it has one Note that this will force a full recalculation of the mass matrix.

fn order(&self) -> usize

Get the current order of accuracy of the solver (e.g. explict euler method is first-order)

fn set_state(&mut self, state: Self::State)

Replace the current state of the solver with a new state.

fn into_state(self) -> RkState<Eqn::V>

Take the current state of the solver, if it exists, returning it to the user. This is useful if you want to use this state in another solver or problem. Note that this will unset the current problem and solver state, so you will need to call set_problem again before calling step or solve.

fn checkpoint(&mut self) -> Self::State

Take a checkpoint of the current state of the solver, returning it to the user. This is useful if you want to use this state in another solver or problem but want to keep this solver active. If you don’t need to use this solver again, you can use take_state instead. Note that this will force a reinitialisation of the internal Jacobian for the solver, if it has one.

fn step(&mut self) -> Result<OdeSolverStopReason<Eqn::T>, DiffsolError>

Step the solution forward by one step, altering the internal state of the solver. The return value is a Result containing the reason for stopping the solver, possible reasons are:

fn set_stop_time(&mut self, tstop: <Eqn as Op>::T) -> Result<(), DiffsolError>

Set a stop time for the solver. The solver will stop when the internal time reaches this time. Once it stops, the stop time is unset. If tstop is at or before the current internal time, an error is returned.

fn interpolate_sens( &self, t: <Eqn as Op>::T, ) -> Result<Vec<<Eqn as Op>::V>, DiffsolError>

Interpolate the sensitivity vectors at a given time. This time should be between the current time and the last solver time step

fn interpolate(&self, t: Eqn::T) -> Result<Eqn::V, DiffsolError>

Interpolate the solution at a given time. This time should be between the current time and the last solver time step

fn interpolate_out(&self, t: Eqn::T) -> Result<Eqn::V, DiffsolError>

Interpolate the integral of the output function at a given time. This time should be between the current time and the last solver time step

fn state(&self) -> StateRef<'_, Eqn::V>

Get the current state of the solver

fn state_mut(&mut self) -> StateRefMut<'_, Eqn::V>

Get a mutable reference to the current state of the solver Note that calling this will cause the next call to step to perform some reinitialisation to take into account the mutated state, this could be expensive for multi-step methods.

fn solve( &mut self, final_time: Eqn::T, ) -> Result<(<Eqn::V as DefaultDenseMatrix>::M, Vec<Eqn::T>), DiffsolError>

where Eqn::V: DefaultDenseMatrix, Self: Sized,

Using the provided state, solve the problem up to time final_time Returns a Vec of solution values at timepoints chosen by the solver. After the solver has finished, the internal state of the solver is at time final_time.

fn solve_dense( &mut self, t_eval: &[Eqn::T], ) -> Result<<Eqn::V as DefaultDenseMatrix>::M, DiffsolError>

where Eqn::V: DefaultDenseMatrix, Self: Sized,

Using the provided state, solve the problem up to time t_eval[t_eval.len()-1] Returns a Vec of solution values at timepoints given by t_eval. After the solver has finished, the internal state of the solver is at time t_eval[t_eval.len()-1].

fn solve_with_checkpointing( &mut self, final_time: Eqn::T, max_steps_between_checkpoints: Option<usize>, ) -> Result<(Checkpointing<'a, Eqn, Self>, <Eqn::V as DefaultDenseMatrix>::M, Vec<Eqn::T>), DiffsolError>

where Eqn::V: DefaultDenseMatrix, Self: Sized,

fn solve_dense_with_checkpointing( &mut self, t_eval: &[Eqn::T], max_steps_between_checkpoints: Option<usize>, ) -> Result<(Checkpointing<'a, Eqn, Self>, <Eqn::V as DefaultDenseMatrix>::M), DiffsolError>

where Eqn::V: DefaultDenseMatrix, Self: Sized,

Solve the problem and write out the solution at the given timepoints, using checkpointing so that the solution can be interpolated at any timepoint. See Self::solve_dense for a similar method that does not use checkpointing.