Struct Milstein 

pub struct Milstein<T: Real, Y: State<T>> {
    pub h0: T,
    pub h_min: T,
    pub h_max: T,
    pub max_steps: usize,
    /* private fields */
}

Derivative-Free Milstein method for solving SDEs.

Provides strong order 1.0 convergence for commutative/diagonal noise, which is an improvement over the 0.5 strong order of Euler-Maruyama.

Fields

h0: T

h_min: T

h_max: T

max_steps: usize

Implementations

impl<T: Real, Y: State<T>> Milstein<T, Y>

pub fn new(h0: T) -> Self

Creates a new Milstein method solver

pub fn h_min(self, h_min: T) -> Self

Set minimum step size

pub fn h_max(self, h_max: T) -> Self

Set maximum step size

pub fn max_steps(self, max_steps: usize) -> Self

Set maximum number of steps

Trait Implementations

impl<T: Real, Y: State<T>> Interpolation<T, Y> for Milstein<T, Y>

fn interpolate(&mut self, t_interp: T) -> Result<Y, Error<T, Y>>

Evaluate the step-local interpolant at the given time.

impl<T: Real, Y: State<T>> StochasticNumericalMethod<T, Y> for Milstein<T, Y>

fn init<F>( &mut self, sde: &mut F, t0: T, tf: T, y0: &Y, ) -> Result<Evals, Error<T, Y>>

where F: SDE<T, Y> + ?Sized,

Initialize the solver before integration

fn step<F>(&mut self, sde: &mut F) -> Result<Evals, Error<T, Y>>

where F: SDE<T, Y> + ?Sized,

Advance the solution by one step

fn t(&self) -> T

Time of last accepted step

fn y(&self) -> &Y

State at last accepted step

fn t_prev(&self) -> T

Time of previous accepted step

fn y_prev(&self) -> &Y

State at previous accepted step

fn h(&self) -> T

Step size for next step

fn set_h(&mut self, h: T)

Set step size for next step

fn status(&self) -> &Status<T, Y>

Current solver status

fn set_status(&mut self, status: Status<T, Y>)

Set solver status