A first specialization are the explicit steppers. Explicit means that the new state of the ode can be computed explicitly from the current state without solving implicit equations. Such steppers have in common that they evaluate the system at time t such that the result of f(x,t) can be passed to the stepper. In odeint, the explicit stepper have two additional methods

do_step( sys , inout , dxdtin , t , dt )

do_step( sys , in , dxdtin , t , out , dt )

Here, the additional parameter is the value of the function f at state x and time t. An example is the Runge-Kutta stepper of fourth order:

runge_kutta4< state_type > rk;
rk.do_step( sys1 , inout , t , dt );               // In-place transformation of inout
rk.do_step( sys2 , inout , t , dt );               // call with different system: Ok
rk.do_step( sys1 , in , t , out , dt );            // Out-of-place transformation
rk.do_step( sys1 , inout , dxdtin , t , dt );      // In-place tranformation of inout
rk.do_step( sys1 , in , dxdtin , t , out , dt );   // Out-of-place transformation

In fact, you do not need to call these two methods. You can always use the simpler do_step( sys , inout , t , dt ), but sometimes the derivative of the state is needed externally to do some external computations or to perform some statistical analysis.

A special class of the explicit steppers are the FSAL (first-same-as-last) steppers, where the last evaluation of the system function is also the first evaluation of the following step. For such steppers the do_step method are slightly different:

do_step( sys , inout , dxdtinout , t , dt )

do_step( sys , in , dxdtin , out , dxdtout , t , dt )

This method takes the derivative at time t and also stores the derivative at time t+dt. Calling these functions subsequently iterating along the solution one saves one function call by passing the result for dxdt into the next function call. However, when using FSAL steppers without supplying derivatives:

do_step( sys , inout , t , dt )

the stepper internally satisfies the FSAL property which means it remembers the last dxdt and uses it for the next step. An example for a FSAL stepper is the Runge-Kutta-Dopri5 stepper. The FSAL trick is sometimes also referred as the Fehlberg trick. An example how the FSAL steppers can be used is

runge_kutta_dopri5< state_type > rk;
rk.do_step( sys1 , in , t , out , dt );
rk.do_step( sys2 , in , t , out , dt );         // DONT do this, sys1 is assumed

rk.do_step( sys2 , in2 , t , out , dt );
rk.do_step( sys2 , in3 , t , out , dt );        // DONT do this, in2 is assumed

rk.do_step( sys1 , inout , dxdtinout , t , dt );
rk.do_step( sys2 , inout , dxdtinout , t , dt );           // Ok, internal derivative is not used, dxdtinout is updated

rk.do_step( sys1 , in , dxdtin , t , out , dxdtout , dt );
rk.do_step( sys2 , in , dxdtin , t , out , dxdtout , dt ); // Ok, internal derivative is not used

Caution

The FSAL-steppers save the derivative at time t+dt internally if they are called via do_step( sys , in , out , t , dt ). The first call of do_step will initialize dxdt and for all following calls it is assumed that the same system and the same state are used. If you use the FSAL stepper within the integrate functions this is taken care of automatically. See the Using steppers section for more details or look into the table below to see which stepper have an internal state.

As mentioned above symplectic solvers are used for Hamiltonian systems. Symplectic solvers conserve the phase space volume exactly and if the Hamiltonian system is energy conservative they also conserve the energy approximately. A special class of symplectic systems are separable systems which can be written in the form dqdt/dt = f1(p), dpdt/dt = f2(q), where (q,p) are the state of system. The space of (q,p) is sometimes referred as the phase space and q and p are said the be the phase space variables. Symplectic systems in this special form occur widely in nature. For example the complete classical mechanics as written down by Newton, Lagrange and Hamilton can be formulated in this framework. The separability of the system depends on the specific choice of coordinates.

Symplectic systems can be solved by odeint by means of the symplectic_euler stepper and a symplectic Runge-Kutta-Nystrom method of fourth order. These steppers assume that the system is autonomous, hence the time will not explicitly occur. Further they fulfill in principle the default Stepper concept, but they expect the system to be a pair of callable objects. The first entry of this pair calculates f1(p) while the second calculates f2(q). The syntax is sys.first(p,dqdt) and sys.second(q,dpdt), where the first and second part can be again simple C-functions of functors. An example is the harmonic oscillator:

typedef boost::array< double , 1 > vector_type;


struct harm_osc_f1
{
    void operator()( const vector_type &p , vector_type &dqdt )
    {
        dqdt[0] = p[0];
    }
};

struct harm_osc_f2
{
    void operator()( const vector_type &q , vector_type &dpdt )
    {
        dpdt[0] = -q[0];
    }
};

The state of such an ODE consist now also of two parts, the part for q (also called the coordinates) and the part for p (the momenta). The full example for the harmonic oscillator is now:

pair< vector_type , vector_type > x;
x.first[0] = 1.0; x.second[0] = 0.0;
symplectic_rkn_sb3a_mclachlan< vector_type > rkn;
rkn.do_step( make_pair( harm_osc_f1() , harm_osc_f2() ) , x , t , dt );

If you like to represent the system with one class you can easily bind two public method:

struct harm_osc
{
    void f1( const vector_type &p , vector_type &dqdt ) const
    {
        dqdt[0] = p[0];
    }

    void f2( const vector_type &q , vector_type &dpdt ) const
    {
        dpdt[0] = -q[0];
    }
};

harm_osc h;
rkn.do_step( make_pair( boost::bind( &harm_osc::f1 , h , _1 , _2 ) , boost::bind( &harm_osc::f2 , h , _1 , _2 ) ) ,
        x , t , dt );

Many Hamiltonian system can be written as dq/dt=p, dp/dt=f(q) which is computationally much easier than the full separable system. Very often, it is also possible to transform the original equations of motion to bring the system in this simplified form. This kind of system can be used in the symplectic solvers, by simply passing f(p) to the do_step method, again f(p) will be represented by a simple C-function or a functor. Here, the above example of the harmonic oscillator can be written as

pair< vector_type , vector_type > x;
x.first[0] = 1.0; x.second[0] = 0.0;
symplectic_rkn_sb3a_mclachlan< vector_type > rkn;
rkn.do_step( harm_osc_f1() , x , t , dt );

In this example the function harm_osc_f1 is exactly the same function as in the above examples.

Note, that the state of the ODE must not be constructed explicitly via pair< vector_type , vector_type > x. One can also use a combination of make_pair and ref. Furthermore, a convenience version of do_step exists which takes q and p without combining them into a pair:

rkn.do_step( harm_osc_f1() , make_pair( boost::ref( q ) , boost::ref( p ) ) , t , dt );
rkn.do_step( harm_osc_f1() , q , p , t , dt );
rkn.do_step( make_pair( harm_osc_f1() , harm_osc_f2() ) , q , p , t , dt );

	Caution
	This section is not up-to-date.

For some kind of systems the stability properties of the classical Runge-Kutta are not sufficient, especially if the system is said to be stiff. A stiff system possesses two or more time scales of very different order. Solvers for stiff systems are usually implicit, meaning that they solve equations like x(t+dt) = x(t) + dt * f(x(t+1)). This particular scheme is the implicit Euler method. Implicit methods usually solve the system of equations by a root finding algorithm like the Newton method and therefore need to know the Jacobian of the system J_​ij = df_​i / dx_​j.

For implicit solvers the system is again a pair, where the first component computes f(x,t) and the second the Jacobian. The syntax is sys.first( x , dxdt , t ) and sys.second( x , J , t ). For the implicit solver the state_type is ublas::vector and the Jacobian is represented by ublas::matrix.

	Important
	Implicit solvers only work with ublas::vector as state type. At the moment, no other state types are supported.

Another large class of solvers are multi-step method. They save a small part of the history of the solution and compute the next step with the help of this history. Since multi-step methods know a part of their history they do not need to compute the system function very often, usually it is only computed once. This makes multi-step methods preferable if a call of the system function is expensive. Examples are ODEs defined on networks, where the computation of the interaction is usually where expensive (and might be of order O(N^2)).

Multi-step methods differ from the normal steppers. They save a part of their history and this part has to be explicitly calculated and initialized. In the following example an Adams-Bashforth-stepper with a history of 5 steps is instantiated and initialized;

adams_bashforth_moulton< 5 , state_type > abm;
abm.initialize( sys , inout , t , dt );
abm.do_step( sys , inout , t , dt );

The initialization uses a fourth-order Runge-Kutta stepper and after the call of initialize the state of inout has changed to the current state, such that it can be immediately used by passing it to following calls of do_step. You can also use you own steppers to initialize the internal state of the Adams-Bashforth-Stepper:

abm.initialize( runge_kutta_fehlberg78< state_type >() , sys , inout , t , dt );

Many multi-step methods are also explicit steppers, hence the parameter of do_step method do not differ from the explicit steppers.

	Caution
	The multi-step methods have some internal variables which depend on the explicit solution. Hence after any external changes of your state (e.g. size) or system the initialize function has to be called again to adjust the internal state of the stepper. If you use the integrate functions this will be taken into account. See the Using steppers section for more details.

Many of the above introduced steppers possess the possibility to use adaptive step-size control. Adaptive step size integration works in principle as follows:

The class of controlled steppers has their own concept in odeint - the Controlled Stepper concept. They are usually constructed from the underlying error steppers. An example is the controller for the explicit Runge-Kutta steppers. The Runge-Kutta steppers enter the controller as a template argument. Additionally one can pass the Runge-Kutta stepper to the constructor, but this step is not necessary; the stepper is default-constructed if possible.

Different step size controlling mechanism exist. They all have in common that they somehow compare predefined error tolerance against the error and that they might reject or accept a step. If a step is rejected the step size is usually decreased and the step is made again with the reduced step size. This procedure is repeated until the step is accepted. This algorithm is implemented in the integration functions.

A classical way to decide whether a step is rejected or accepted is to calculate

val = || | err_​i | / ( ε_​abs + ε_​rel * ( a_​x | x_​i | + a_​dxdt | | dxdt_​i | )||

ε_​abs and ε_​rel are the absolute and the relative error tolerances, and || x || is a norm, typically ||x||=(Σ_​i x_​i²)^1/2 or the maximum norm. The step is rejected if val is greater then 1, otherwise it is accepted. For details of the used norms and error tolerance see the table below.

For the controlled_runge_kutta stepper the new step size is then calculated via

val > 1 : dt_​new = dt_​current max( 0.9 pow( val , -1 / ( O_​E - 1 ) ) , 0.2 )

val < 0.5 : dt_​new = dt_​current min( 0.9 pow( val , -1 / O_​S ) , 5 )

else : dt_​new = dt_​current

Here, O_​S and O_​E are the order of the stepper and the error stepper. These formulas also contain some safety factors, avoiding that the step size is reduced or increased to much. For details of the implementations of the controlled steppers in odeint see the table below.

To ease to generation of the controlled stepper, generation functions exist which take the absolute and relative error tolerances and a predefined error stepper and construct from this knowledge an appropriate controlled stepper. The generation functions are explained in detail in Generation functions.

A fourth class of stepper exists which are the so called dense output steppers. Dense-output steppers might take larger steps and interpolate the solution between two consecutive points. This interpolated points have usually the same order as the order of the stepper. Dense-output steppers are often composite stepper which take the underlying method as a template parameter. An example is the dense_output_runge_kutta stepper which takes a Runge-Kutta stepper with dense-output facilities as argument. Not all Runge-Kutta steppers provide dense-output calculation; at the moment only the Dormand-Prince 5 stepper provides dense output. An example is

dense_output_runge_kutta< controlled_runge_kutta< runge_kutta_dopri5< state_type > > > dense;
dense.initialize( in , t , dt );
pair< double , double > times = dense.do_step( sys );
(void)times;

Dense output stepper have their own concept. The main difference to usual steppers is that they manage the state and time internally. If you call do_step, only the ODE is passed as argument. Furthermore do_step return the last time interval: t and t+dt, hence you can interpolate the solution between these two times points. Another difference is that they must be initialized with initialize, otherwise the internal state of the stepper is default constructed which might produce funny errors or bugs.

The construction of the dense output stepper looks a little bit nasty, since in the case of the dense_output_runge_kutta stepper a controlled stepper and an error stepper have to be nested. To simplify the generation of the dense output stepper generation functions exist:

typedef boost::numeric::odeint::result_of::make_dense_output<
    runge_kutta_dopri5< state_type > >::type dense_stepper_type;
dense_stepper_type dense2 = make_dense_output( 1.0e-6 , 1.0e-6 , runge_kutta_dopri5< state_type >() );
(void)dense2;

This statement is also lengthy; it demonstrates how make_dense_output can be used with the result_of protocol. The parameters to make_dense_output are the absolute error tolerance, the relative error tolerance and the stepper. This explicitly assumes that the underlying stepper is a controlled stepper and that this stepper has an absolute and a relative error tolerance. For details about the generation functions see Generation functions. The generation functions have been designed for easy use with the integrate functions:

integrate_const( make_dense_output( 1.0e-6 , 1.0e-6 , runge_kutta_dopri5< state_type >() ) , sys , inout , t_start , t_end , dt );

This section contains some general information about the usage of the steppers in odeint.

Steppers are copied by value

The stepper in odeint are always copied by values. They are copied for the creation of the controlled steppers or the dense output steppers as well as in the integrate functions.

Steppers might have a internal state

	Caution
	Some of the features described in this section are not yet implemented

Some steppers require to store some information about the state of the ODE between two steps. Examples are the multi-step methods which store a part of the solution during the evolution of the ODE, or the FSAL steppers which store the last derivative at time t+dt, to be used in the next step. In both cases the steppers expect that consecutive calls of do_step are from the same solution and the same ODE. In this case it is absolutely necessary that you call do_step with the same system function and the same state, see also the examples for the FSAL steppers above.

Stepper with an internal state support two additional methods: reset which resets the state and initialize which initializes the internal state. The parameters of initialize depend on the specific stepper. For example the Adams-Bashforth-Moulton stepper provides two initialize methods: initialize( system , inout , t , dt ) which initializes the internal states with the help of the Runge-Kutta 4 stepper, and initialize( stepper , system , inout , t , dt ) which initializes with the help of stepper. For the case of the FSAL steppers, initialize is initialize( sys , in , t ) which simply calculates the r.h.s. of the ODE and assigns its value to the internal derivative.

All these steppers have in common, that they initially fill their internal state by themselves. Hence you are not required to call initialize. See how this works for the Adams-Bashforth-Moulton stepper: in the example we instantiate a fourth order Adams-Bashforth-Moulton stepper, meaning that it will store 4 internal derivatives of the solution at times (t-dt,t-2*dt,t-3*dt,t-4*dt).

adams_bashforth_moulton< 4 , state_type > stepper;
stepper.do_step( sys , x , t , dt );   // make one step with the classical Runge-Kutta stepper and initialize the first internal state
                                       // the internal array is now [x(t-dt)]

stepper.do_step( sys , x , t , dt );   // make one step with the classical Runge-Kutta stepper and initialize the second internal state
                                       // the internal state array is now [x(t-dt), x(t-2*dt)]

stepper.do_step( sys , x , t , dt );   // make one step with the classical Runge-Kutta stepper and initialize the third internal state
                                       // the internal state array is now [x(t-dt), x(t-2*dt), x(t-3*dt)]

stepper.do_step( sys , x , t , dt );   // make one step with the classical Runge-Kutta stepper and initialize the fourth internal state
                                       // the internal state array is now [x(t-dt), x(t-2*dt), x(t-3*dt), x(t-4*dt)]

stepper.do_step( sys , x , t , dt );   // make one step with Adam-Bashforth-Moulton, the internal array of states is now rotated

In the stepper table at the bottom of this page one can see which stepper have an internal state and hence provide the reset and initialize methods.

Stepper might be resizable

Nearly all steppers in odeint need to store some intermediate results of the type state_type or deriv_type. To do so odeint need some memory management for the internal temporaries. As this memory management is typically related to adjusting the size of vector-like types, it is called resizing in odeint. So, most steppers in odeint provide an additional template parameter which controls the size adjustment of the internal variables - the resizer. In detail odeint provides three policy classes (resizers) always_resizer, initially_resizer, and never_resizer. Furthermore, all stepper have a method adjust_size which takes a parameter representing a state type and which manually adjusts the size of the internal variables matching the size of the given instance. Before performing the actual resizing odeint always checks if the sizes of the state and the internal variable differ and only resizes if they are different.

	Note
	You only have to worry about memory allocation when using dynamically sized vector types. If your state type is heap allocated, like boost::array, no memory allocation is required whatsoever.

By default the resizing parameter is initially_resizer, meaning that the first call to do_step performs the resizing, hence memory allocation. If you have changed the size of your system and your state you have to call adjust_size by hand in this case. The second resizer is the always_resizer which tries to resize the internal variables at every call of do_step. Typical use cases for this kind of resizer are self expanding lattices like shown in the tutorial ( Self expanding lattices) or partial differential equations with an adaptive grid. Here, no calls of adjust_size are required, the steppers manage everything themselves. The third class of resizer is the never_resizer which means that the internal variables are never adjusted automatically and always have to be adjusted by hand .

There is a second mechanism which influences the resizing and which controls if a state type is at least resizeable - a meta-function is_resizeable. This meta-function returns a static Boolean value if any type is resizable. For example it will return true for std::vector< T > but false for boost::array< T >. By default and for unknown types is_resizeable returns false, so if you have your own type you need to specialize this meta-function. For more details on the resizing mechanism see the section Adapt your own state types.

Which steppers should be used in which situation

odeint provides a quite large number of different steppers such that the user is left with the question of which stepper fits his needs. Our personal recommendations are:

Finally, one can also write new steppers which are fully compatible with odeint. They only have to fulfill one or several of the stepper Concepts of odeint.

We will illustrate how to write your own stepper with the example of the stochastic Euler method. This method is suited to solve stochastic differential equations (SDEs). A SDE has the form

dx/dt = f(x) + g(x) ξ(t)

where ξ is Gaussian white noise with zero mean and a standard deviation σ(t). f(x) is said to be the deterministic part while g(x) ξ is the noisy part. In case g(x) is independent of x the SDE is said to have additive noise. It is not possible to solve SDE with the classical solvers for ODEs since the noisy part of the SDE has to be scaled differently then the deterministic part with respect to the time step. But there exist many solvers for SDEs. A classical and easy method is the stochastic Euler solver. It works by iterating

x(t+Δ t) = x(t) + Δ t f(x(t)) + Δ t^1/2 g(x) ξ(t)

where ξ(t) is an independent normal distributed random variable.

Now we will implement this method. We will call the stepper stochastic_euler. It models the Stepper concept. For simplicity, we fix the state type to be an array< double , N > The class definition looks like

template< size_t N > class stochastic_euler
{
public:

    typedef boost::array< double , N > state_type;
    typedef boost::array< double , N > deriv_type;
    typedef double value_type;
    typedef double time_type;
    typedef unsigned short order_type;
    typedef boost::numeric::odeint::stepper_tag stepper_category;

    static order_type order( void ) { return 1; }

    // ...
};

The types are needed in order to fulfill the stepper concept. As internal state and deriv type we use simple arrays in the stochastic Euler, they are needed for the temporaries. The stepper has the order one which is returned from the order() function.

The system functions needs to calculate the deterministic and the stochastic part of our stochastic differential equation. So it might be suitable that the system function is a pair of functions. The first element of the pair computes the deterministic part and the second the stochastic one. Then, the second part also needs to calculate the random numbers in order to simulate the stochastic process. We can now implement the do_step method

template< size_t N > class stochastic_euler
{
public:

    // ...

    template< class System >
    void do_step( System system , state_type &x , time_type t , time_type dt ) const
    {
        deriv_type det , stoch ;
        system.first( x , det );
        system.second( x , stoch );
        for( size_t i=0 ; i<x.size() ; ++i )
            x[i] += dt * det[i] + sqrt( dt ) * stoch[i];
    }
};

This is all. It is quite simple and the stochastic Euler stepper implement here is quite general. Of course it can be enhanced, for example

Now, lets look how we use the new stepper. A nice example is the Ornstein-Uhlenbeck process. It consists of a simple Brownian motion overlapped with an relaxation process. Its SDE reads

dx/dt = - x + ξ

where ξ is Gaussian white noise with standard deviation σ. Implementing the Ornstein-Uhlenbeck process is quite simple. We need two functions or functors - one for the deterministic and one for the stochastic part:

const static size_t N = 1;
typedef boost::array< double , N > state_type;

struct ornstein_det
{
    void operator()( const state_type &x , state_type &dxdt ) const
    {
        dxdt[0] = -x[0];
    }
};

struct ornstein_stoch
{
    boost::mt19937 &m_rng;
    boost::normal_distribution<> m_dist;

  ornstein_stoch( boost::mt19937 &rng , double sigma ) : m_rng( rng ) , m_dist( 0.0 , sigma ) { }

    void operator()( const state_type &x , state_type &dxdt )
    {
        dxdt[0] = m_dist( m_rng );
    }
};

In the stochastic part we have used the Mersenne twister for the random number generation and a Gaussian white noise generator normal_distribution with standard deviation σ. Now, we can use the stochastic Euler stepper with the integrate functions:

boost::mt19937 rng;
double dt = 0.1;
state_type x = {{ 1.0 }};
integrate_const( stochastic_euler< N >() , make_pair( ornstein_det() , ornstein_stoch( rng , 1.0 ) ),
        x , 0.0 , 10.0 , dt , streaming_observer() );

Note, how we have used the make_pair function for the generation of the system function.

odeint provides a C++ template meta-algorithm for constructing arbitrary Runge-Kutta schemes ^[1]. Some schemes are predefined in odeint, for example the classical Runge-Kutta of fourth order, or the Runge-Kutta-Cash-Karp 54 and the Runge-Kutta-Fehlberg 78 method. You can use this meta algorithm to construct you own solvers. This has the advantage that you can make full use of odeint's algebra and operation system.

Consider for example the method of Heun, defined by the following Butcher tableau:

c1 = 0

c2 = 1/3, a21 = 1/3

c3 = 2/3, a31 =  0 , a32 = 2/3

          b1  = 1/4, b2  = 0  , b3 = 3/4

Implementing this method is very easy. First you have to define the constants:

template< class Value = double >
struct heun_a1 : boost::array< Value , 1 > {
    heun_a1( void )
    {
        (*this)[0] = static_cast< Value >( 1 ) / static_cast< Value >( 3 );
    }
};

template< class Value = double >
struct heun_a2 : boost::array< Value , 2 >
{
    heun_a2( void )
    {
        (*this)[0] = static_cast< Value >( 0 );
        (*this)[1] = static_cast< Value >( 2 ) / static_cast< Value >( 3 );
    }
};


template< class Value = double >
struct heun_b : boost::array< Value , 3 >
{
    heun_b( void )
    {
        (*this)[0] = static_cast<Value>( 1 ) / static_cast<Value>( 4 );
        (*this)[1] = static_cast<Value>( 0 );
        (*this)[2] = static_cast<Value>( 3 ) / static_cast<Value>( 4 );
    }
};

template< class Value = double >
struct heun_c : boost::array< Value , 3 >
{
    heun_c( void )
    {
        (*this)[0] = static_cast< Value >( 0 );
        (*this)[1] = static_cast< Value >( 1 ) / static_cast< Value >( 3 );
        (*this)[2] = static_cast< Value >( 2 ) / static_cast< Value >( 3 );
    }
};

While this might look cumbersome, packing all parameters into a templatized class which is not immediately evaluated has the advantage that you can change the value_type of your stepper to any type you like - presumably arbitrary precision types. One could also instantiate the coefficients directly

const boost::array< double , 1 > heun_a1 = {{ 1.0 / 3.0 }};
const boost::array< double , 2 > heun_a2 = {{ 0.0 , 2.0 / 3.0 }};
const boost::array< double , 3 > heun_b = {{ 1.0 / 4.0 , 0.0 , 3.0 / 4.0 }};
const boost::array< double , 3 > heun_c = {{ 0.0 , 1.0 / 3.0 , 2.0 / 3.0 }};

But then you are nailed down to use doubles.

Next, you need to define your stepper, note that the Heun method has 3 stages and produces approximations of order 3:

template<
    class State ,
    class Value = double ,
    class Deriv = State ,
    class Time = Value ,
    class Algebra = boost::numeric::odeint::range_algebra ,
    class Operations = boost::numeric::odeint::default_operations ,
    class Resizer = boost::numeric::odeint::initially_resizer
>
class heun : public
boost::numeric::odeint::explicit_generic_rk< 3 , 3 , State , Value , Deriv , Time ,
                                             Algebra , Operations , Resizer >
{

public:

    typedef boost::numeric::odeint::explicit_generic_rk< 3 , 3 , State , Value , Deriv , Time ,
                                                         Algebra , Operations , Resizer > stepper_base_type;

    typedef typename stepper_base_type::state_type state_type;
    typedef typename stepper_base_type::wrapped_state_type wrapped_state_type;
    typedef typename stepper_base_type::value_type value_type;
    typedef typename stepper_base_type::deriv_type deriv_type;
    typedef typename stepper_base_type::wrapped_deriv_type wrapped_deriv_type;
    typedef typename stepper_base_type::time_type time_type;
    typedef typename stepper_base_type::algebra_type algebra_type;
    typedef typename stepper_base_type::operations_type operations_type;
    typedef typename stepper_base_type::resizer_type resizer_type;
    typedef typename stepper_base_type::stepper_type stepper_type;

    heun( const algebra_type &algebra = algebra_type() )
    : stepper_base_type(
            fusion::make_vector(
                heun_a1<Value>() ,
                heun_a2<Value>() ) ,
            heun_b<Value>() , heun_c<Value>() , algebra )
    { }
};

That's it. Now, we have a new stepper method and we can use it, for example with the Lorenz system:

typedef boost::array< double , 3 > state_type;
heun< state_type > h;
state_type x = {{ 10.0 , 10.0 , 10.0 }};

integrate_const( h , lorenz() , x , 0.0 , 100.0 , 0.01 ,
                 streaming_observer( std::cout ) );