Struct rgsl::types::ordinary_differential_equations::ODEiv2StepType [−][src]
pub struct ODEiv2StepType { /* fields omitted */ }
Implementations
Explicit embedded Runge-Kutta (2, 3) method.
Explicit 4th order (classical) Runge-Kutta. Error estimation is carried out by the step doubling method. For more efficient estimate of the error, use the embedded methods described below.
Explicit embedded Runge-Kutta-Fehlberg (4, 5) method. This method is a good general-purpose integrator.
Explicit embedded Runge-Kutta Cash-Karp (4, 5) method.
Explicit embedded Runge-Kutta Prince-Dormand (8, 9) method.
Implicit Gaussian first order Runge-Kutta. Also known as implicit Euler or backward Euler method. Error estimation is carried out by the step doubling method. This algorithm requires the Jacobian and access to the driver object via gsl_odeiv2_step_set_driver.
Implicit Gaussian second order Runge-Kutta. Also known as implicit mid-point rule. Error estimation is carried out by the step doubling method. This stepper requires the Jacobian and access to the driver object via gsl_odeiv2_step_set_driver.
Implicit Gaussian 4th order Runge-Kutta. Error estimation is carried out by the step doubling method. This algorithm requires the Jacobian and access to the driver object via gsl_odeiv2_step_set_driver.
Implicit Bulirsch-Stoer method of Bader and Deuflhard. The method is generally suitable for stiff problems. This stepper requires the Jacobian.
A variable-coefficient linear multistep Adams method in Nordsieck form. This stepper uses explicit Adams-Bashforth (predictor) and implicit Adams-Moulton (corrector) methods in P(EC)^m functional iteration mode. Method order varies dynamically between 1 and 12. This stepper requires the access to the driver object via gsl_odeiv2_step_set_driver.
A variable-coefficient linear multistep backward differentiation formula (BDF) method in Nordsieck form. This stepper uses the explicit BDF formula as predictor and implicit BDF formula as corrector. A modified Newton iteration method is used to solve the system of non-linear equations. Method order varies dynamically between 1 and 5. The method is generally suitable for stiff problems. This stepper requires the Jacobian and the access to the driver object via gsl_odeiv2_step_set_driver.
Trait Implementations
Auto Trait Implementations
impl RefUnwindSafe for ODEiv2StepType
impl !Send for ODEiv2StepType
impl !Sync for ODEiv2StepType
impl Unpin for ODEiv2StepType
impl UnwindSafe for ODEiv2StepType
Blanket Implementations
Mutably borrows from an owned value. Read more