Struct DormandPrinceFixedStep 

pub struct DormandPrinceFixedStep { /* private fields */ }

Explicit, six-stage, fifth-order, fixed-step, Runge-Kutta method.¹

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

t_{n+1} = t_n + h

k_1 = f(t_n, y_n)

k_2 = f(t_n + \tfrac{1}{5} h, y_n + \tfrac{1}{5} h k_1)

k_3 = f(t_n + \tfrac{3}{10} h, y_n + \tfrac{3}{40} h k_1 + \tfrac{9}{40} h k_2)

k_4 = f(t_n + \tfrac{4}{5} h, y_n + \tfrac{44}{45} h k_1 - \tfrac{56}{15} h k_2 + \tfrac{32}{9} h k_3)

k_5 = f(t_n + \tfrac{8}{9} h, y_n + \tfrac{19372}{6561} h k_1 - \tfrac{25360}{2187} h k_2 + \tfrac{64448}{6561} h k_3 - \tfrac{212}{729} h k_4)

k_6 = f(t_n + h, y_n + \tfrac{9017}{3168} h k_1 - \tfrac{355}{33} h k_2 - \tfrac{46732}{5247} h k_3 + \tfrac{49}{176} h k_4 - \tfrac{5103}{18656} h k_5)

y_{n+1} = y_n + h\left(\frac{35}{384}\,k_1 + \frac{500}{1113}\,k_3 + \frac{125}{192}\,k_4 - \frac{2187}{6784}\,k_5 + \frac{11}{84}\,k_6\right)

---

1. Fixed-step variant of the Dormand-Prince method. ↩

Trait Implementations

impl Debug for DormandPrince

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

Formats the value using the given formatter.

impl Default for DormandPrince

fn default() -> DormandPrince

Returns the “default value” for a type.

impl<Y, U, V, T> Explicit<Y, U, V, T> for DormandPrince

where Y: Differentiable<T> + Tensor, Derivative<Y, T>: Mul<Quantity<T>, Output = Y>, for<'a> &'a Y: Mul<Scalar, Output = Y> + Sub<&'a Y, Output = Y>, for<'a> &'a Derivative<Y, T>: Mul<Scalar, Output = Derivative<Y, T>> + Mul<Quantity<T>, Output = Y>, U: TensorVec<Item = Y>, V: TensorVec<Item = Derivative<Y, T>>,

const SLOPES: usize = 6

fn integrate( &self, function: impl FnMut(Quantity<T>, &Y) -> Result<Derivative<Y, T>, String>, time: &[Quantity<T>], initial_condition: Y, ) -> Result<(Times<T>, U, V), IntegrationError>

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

impl<T> FixedStep<T> for DormandPrince

fn dt(&self) -> Quantity<T>

Returns the time step.

impl<Y, U, V, T> FixedStepExplicit<Y, U, V, T> for DormandPrince

where Y: Differentiable<T> + Tensor, Derivative<Y, T>: Mul<Quantity<T>, Output = Y>, for<'a> &'a Y: Mul<Scalar, Output = Y> + Sub<&'a Y, Output = Y>, for<'a> &'a Derivative<Y, T>: Mul<Scalar, Output = Derivative<Y, T>> + Mul<Quantity<T>, Output = Y>, U: TensorVec<Item = Y>, V: TensorVec<Item = Derivative<Y, T>>,

type Tableau = Tableau

Butcher tableau of this method.

fn integrate_fixed_step( &self, function: impl FnMut(Quantity<T>, &Y) -> Result<Derivative<Y, T>, String>, time: &[Quantity<T>], initial_condition: Y, ) -> Result<(Times<T>, U, V), IntegrationError>

fn step( &self, function: impl FnMut(Quantity<T>, &Y) -> Result<Derivative<Y, T>, String>, y: &Y, t: Quantity<T>, dt: Quantity<T>, k: &mut [Derivative<Y, T>], y_trial: &mut Y, ) -> Result<(), String>

impl<Y, U> OdeIntegrator<Y, U> for DormandPrince

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