Struct Verner9 

pub struct Verner9 {
    pub abs_tol: Scalar,
    pub rel_tol: Scalar,
    pub dt_beta: Scalar,
    pub dt_expn: Scalar,
    pub dt_cut: Scalar,
    pub dt_min: Scalar,
}

Explicit, sixteen-stage, ninth-order, variable-step, Runge-Kutta method.¹

\frac{dy}{dt} = f(t, y)

t_{n+1} = t_n + h

k_1 = f(t_n, y_n)

\cdots

---

1. J.H. Verner, Numer. Algor. 53, 383 (2010). ↩

Fields

abs_tol: Scalar

Absolute error tolerance.

rel_tol: Scalar

Relative error tolerance.

dt_beta: Scalar

Multiplier for adaptive time steps.

dt_expn: Scalar

Exponent for adaptive time steps.

dt_cut: Scalar

Cut back factor for the time step.

dt_min: Scalar

Minimum value for the time step.

Trait Implementations

impl Debug for Verner9

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Verner9

fn default() -> Self

Returns the “default value” for a type.

impl<Y, U> Explicit<Y, U> for Verner9

where Self: OdeSolver<Y, U>, Y: Tensor, for<'a> &'a Y: Mul<Scalar, Output = Y> + Sub<&'a Y, Output = Y>, U: TensorVec<Item = Y>,

const SLOPES: usize = 16usize

fn dt_beta(&self) -> Scalar

Returns the multiplier for adaptive time steps.

fn dt_expn(&self) -> Scalar

Returns the exponent for adaptive time steps.

fn integrate( &self, function: impl FnMut(Scalar, &Y) -> Result<Y, String>, time: &[Scalar], initial_condition: Y, ) -> Result<(Vector, U, U), IntegrationError>

Solves an initial value problem by explicitly integrating a system of ordinary differential equations.

fn time_step(&self, error: Scalar, dt: &mut Scalar)

Provides the adaptive time step as a function of the error.

impl<Y, U> InterpolateSolution<Y, U> for Verner9

where Y: Tensor, for<'a> &'a Y: Mul<Scalar, Output = Y> + Sub<&'a Y, Output = Y>, U: TensorVec<Item = Y>,

fn interpolate( &self, time: &Vector, tp: &Vector, yp: &U, function: impl FnMut(Scalar, &Y) -> Result<Y, String>, ) -> Result<(U, U), IntegrationError>

Solution interpolation.

impl<Y, U> OdeSolver<Y, U> for Verner9

where Y: Tensor, U: TensorVec<Item = Y>,

fn abs_tol(&self) -> Scalar

Returns the absolute error tolerance.

fn dt_cut(&self) -> Scalar

Returns the cut back factor for function errors.

fn dt_min(&self) -> Scalar

Returns the minimum value for the time step.