Explicit Runge Kutta, defined via a butcher tableau. More...

#include <elementary/ode/AdaptiveRungeKutta.hpp>

Inheritance diagram for Traum::AdaptiveRungeKutta< DiffEquation >:

Classes
struct	EvolutionVars
	Set of variables that are updated at each integration step but keep their values from the previous step. More...

struct	OutputVars
	Set of variables that contain output information. More...

struct	StatisticVars

Public Member Functions
success_code	advance (bool backward=false) override
	Advance the solution by one step, producing dense output.

const real &	f (int i) const override
	Return the solution of the $i^{th}$ component.

long	step_nr () const override
	Return the current integration step number, which is increased with each call to advance().

const real &	x0 () const override
	Return the last integration parameter.

const real &	x1 () const override
	Return the current integration parameter.

Public Member Functions inherited from Traum::Integrator< DiffEquation::real >
bool	contains (const real &x) const
	Query whether the current integration interval [x0,x1) contains a given value.

Detailed Description

template<class DiffEquation>
class Traum::AdaptiveRungeKutta< DiffEquation >

Explicit Runge Kutta, defined via a butcher tableau.

Supports adaptive step size control.

Class Documentation

struct Traum::AdaptiveRungeKutta::EvolutionVars

template<class DiffEquation>
struct Traum::AdaptiveRungeKutta< DiffEquation >::EvolutionVars

Set of variables that are updated at each integration step but keep their values from the previous step.

struct Traum::AdaptiveRungeKutta::OutputVars

template<class DiffEquation>
struct Traum::AdaptiveRungeKutta< DiffEquation >::OutputVars

Set of variables that contain output information.

struct Traum::AdaptiveRungeKutta::StatisticVars

template<class DiffEquation>
struct Traum::AdaptiveRungeKutta< DiffEquation >::StatisticVars