 ### State types, algebras and operations

Construction/Resizing
Algebras and Operations

In odeint the stepper algorithms are implemented independently of the underlying fundamental mathematical operations. This is realized by giving the user full control over the state type and the mathematical operations for this state type. Technically, this is done by introducing three concepts: StateType, Algebra, Operations. Most of the steppers in odeint expect three class types fulfilling these concepts as template parameters. Note that these concepts are not fully independent of each other but rather a valid combination must be provided in order to make the steppers work. In the following we will give some examples on reasonable state_type-algebra-operations combinations. For the most common state types, like `vector<double>` or `array<double,N>` the default values range_algebra and default_operations are perfectly fine and odeint can be used as is without worrying about algebra/operations at all.

Important state_type, algebra and operations are not independent, a valid combination must be provided to make odeint work properly

Moreover, as odeint handles the memory required for intermediate temporary objects itself, it also needs knowledge about how to create state_type objects and maybe how to allocate memory (resizing). All in all, the following things have to be taken care of when odeint is used with non-standard state types:

• construction/destruction
• resizing (if possible/required)
• algebraic operations

Again, odeint already provides basic interfaces for most of the usual state types. So if you use a `std::vector`, or a `boost::array` as state type no additional work is required, they just work out of the box.

#### Construction/Resizing

We distinguish between two basic state types: fixed sized and dynamically sized. For fixed size state types the default constructor `state_type()` already allocates the required memory, prominent example is `boost::array<T,N>`. Dynamically sized types have to be resized to make sure enough memory is allocated, the standard constructor does not take care of the resizing. Examples for this are the STL containers like `vector<double>`.

The most easy way of getting your own state type to work with odeint is to use a fixed size state, base calculations on the range_algebra and provide the following functionality:

Name

Expression

Type

Semantics

Construct State

`State x()`

`void`

Creates an instance of `State` and allocates memory.

Begin of the sequence

boost::begin(x)

Iterator

Returns an iterator pointing to the begin of the sequence

End of the sequence

boost::end(x)

Iterator

Returns an iterator pointing to the end of the sequence

Warning If your state type does not allocate memory by default construction, you must define it as resizeable and provide resize functionality (see below). Otherwise segmentation faults will occur.

So fixed sized arrays supported by Boost.Range immediately work with odeint. For dynamically sized arrays one has to additionally supply the resize functionality. First, the state has to be tagged as resizeable by specializing the struct `is_resizeable` which consists of one typedef and one bool value:

Name

Expression

Type

Semantics

Resizability

`is_resizeable<State>::type`

`boost::true_type` or `boost::false_type`

Determines resizeability of the state type, returns `boost::true_type` if the state is resizeable.

Resizability

`is_resizeable<State>::value`

`bool`

Same as above, but with `bool` value.

Defining `type` to be `true_type` and `value` as `true` tells odeint that your state is resizeable. By default, odeint now expects the support of `boost::size(x)` and a `x.resize( boost::size(y) )` member function for resizing:

Name

Expression

Type

Semantics

Get size

```boost::size( x )```

`size_type`

Returns the current size of x.

Resize

```x.resize( boost::size( y ) )```

`void`

Resizes x to have the same size as y.

##### Using the container interface

As a first example we take the most simple case and implement our own vector `my_vector` which will provide a container interface. This makes Boost.Range working out-of-box. We add a little functionality to our vector which makes it allocate some default capacity by construction. This is helpful when using resizing as then a resize can be assured to not require a new allocation.

```template< int MAX_N >
class my_vector
{
typedef std::vector< double > vector;

public:
typedef vector::iterator iterator;
typedef vector::const_iterator const_iterator;

public:
my_vector( const size_t N )
: m_v( N )
{
m_v.reserve( MAX_N );
}

my_vector()
: m_v()
{
m_v.reserve( MAX_N );
}

// ... [ implement container interface ]
```

The only thing that has to be done other than defining is thus declaring my_vector as resizeable:

```// define my_vector as resizeable

namespace boost { namespace numeric { namespace odeint {

template<size_t N>
struct is_resizeable< my_vector<N> >
{
typedef boost::true_type type;
static const bool value = type::value;
};

} } }
```

If we wouldn't specialize the `is_resizeable` template, the code would still compile but odeint would not adjust the size of temporary internal instances of my_vector and hence try to fill zero-sized vectors resulting in segmentation faults! The full example can be found in my_vector.cpp

##### std::list

If your state type does work with Boost.Range, but handles resizing differently you are required to specialize two implementations used by odeint to check a state's size and to resize:

Name

Expression

Type

Semantics

Check size

```same_size_impl<State,State>::same_size(x , y)```

`bool`

Returns true if the size of x equals the size of y.

Resize

```resize_impl<State,State>::resize(x , y)```

`void`

Resizes x to have the same size as y.

As an example we will use a `std::list` as state type in odeint. Because `std::list` is not supported by `boost::size` we have to replace the same_size and resize implementation to get list to work with odeint. The following code shows the required template specializations:

```typedef std::list< double > state_type;

namespace boost { namespace numeric { namespace odeint {

template< >
struct is_resizeable< state_type >
{ // declare resizeability
typedef boost::true_type type;
const static bool value = type::value;
};

template< >
struct same_size_impl< state_type , state_type >
{ // define how to check size
static bool same_size( const state_type &v1 ,
const state_type &v2 )
{
return v1.size() == v2.size();
}
};

template< >
struct resize_impl< state_type , state_type >
{ // define how to resize
static void resize( state_type &v1 ,
const state_type &v2 )
{
v1.resize( v2.size() );
}
};

} } }
```

With these definitions odeint knows how to resize `std::list`s and so they can be used as state types. A complete example can be found in list_lattice.cpp.

#### Algebras and Operations

To provide maximum flexibility odeint is implemented in a highly modularized way. This means it is possible to change the underlying mathematical operations without touching the integration algorithms. The fundamental mathematical operations are those of a vector space, that is addition of `state_types` and multiplication of `state_type`s with a scalar (`time_type`). In odeint this is realized in two concepts: Algebra and Operations. The standard way how this works is by the range algebra which provides functions that apply a specific operation to each of the individual elements of a container based on the Boost.Range library. If your state type is not supported by Boost.Range there are several possibilities to tell odeint how to do algebraic operations:

• Implement `boost::begin` and `boost::end` for your state type so it works with Boost.Range.
• Implement vector-vector addition operator `+` and scalar-vector multiplication operator `*` and use the non-standard `vector_space_algebra`.
• Implement your own algebra that implements the required functions.
##### GSL Vector

In the following example we will try to use the `gsl_vector` type from GSL (GNU Scientific Library) as state type in odeint. We will realize this by implementing a wrapper around the gsl_vector that takes care of construction/destruction. Also, Boost.Range is extended such that it works with `gsl_vector`s as well which required also the implementation of a new `gsl_iterator`.

Note odeint already includes all the code presented here, see gsl_wrapper.hpp, so `gsl_vector`s can be used straight out-of-box. The following description is just for educational purpose.

The GSL is a C library, so `gsl_vector` has neither constructor, nor destructor or any `begin` or `end` function, no iterators at all. So to make it work with odeint plenty of things have to be implemented. Note that all of the work shown here is already included in odeint, so using `gsl_vector`s in odeint doesn't require any further adjustments. We present it here just as an educational example. We start with defining appropriate constructors and destructors. This is done by specializing the `state_wrapper` for `gsl_vector`. State wrappers are used by the steppers internally to create and manage temporary instances of state types:

```template<>
struct state_wrapper< gsl_vector* >
{
typedef double value_type;
typedef gsl_vector* state_type;
typedef state_wrapper< gsl_vector* > state_wrapper_type;

state_type m_v;

state_wrapper( )
{
m_v = gsl_vector_alloc( 1 );
}

state_wrapper( const state_wrapper_type &x )
{
resize( m_v , x.m_v );
gsl_vector_memcpy( m_v , x.m_v );
}

~state_wrapper()
{
gsl_vector_free( m_v );
}

};
```

This `state_wrapper` specialization tells odeint how gsl_vectors are created, copied and destroyed. Next we need resizing, this is required because gsl_vectors are dynamically sized objects:

```template<>
struct is_resizeable< gsl_vector* >
{
typedef boost::true_type type;
const static bool value = type::value;
};

template <>
struct same_size_impl< gsl_vector* , gsl_vector* >
{
static bool same_size( const gsl_vector* x , const gsl_vector* y )
{
return x->size == y->size;
}
};

template <>
struct resize_impl< gsl_vector* , gsl_vector* >
{
static void resize( gsl_vector* x , const gsl_vector* y )
{
gsl_vector_free( x );
x = gsl_vector_alloc( y->size );
}
};
```

Up to now, we defined creation/destruction and resizing, but gsl_vectors also don't support iterators, so we first implement a gsl iterator:

```/*
* defines an iterator for gsl_vector
*/
class gsl_vector_iterator
: public boost::iterator_facade< gsl_vector_iterator , double ,
boost::random_access_traversal_tag >
{
public :

gsl_vector_iterator( void ): m_p(0) , m_stride( 0 ) { }
explicit gsl_vector_iterator( gsl_vector *p ) : m_p( p->data ) , m_stride( p->stride ) { }
friend gsl_vector_iterator end_iterator( gsl_vector * );

private :

friend class boost::iterator_core_access;
friend class const_gsl_vector_iterator;

void increment( void ) { m_p += m_stride; }
void decrement( void ) { m_p -= m_stride; }
void advance( ptrdiff_t n ) { m_p += n*m_stride; }
bool equal( const gsl_vector_iterator &other ) const { return this->m_p == other.m_p; }
bool equal( const const_gsl_vector_iterator &other ) const;
double& dereference( void ) const { return *m_p; }

double *m_p;
size_t m_stride;
};
```

A similar class exists for the `const` version of the iterator. Then we have a function returning the end iterator (similarly for `const` again):

```gsl_vector_iterator end_iterator( gsl_vector *x )
{
gsl_vector_iterator iter( x );
iter.m_p += iter.m_stride * x->size;
return iter;
}
```

Finally, the bindings for Boost.Range are added:

```// template<>
inline gsl_vector_iterator range_begin( gsl_vector *x )
{
return gsl_vector_iterator( x );
}

// template<>
inline gsl_vector_iterator range_end( gsl_vector *x )
{
return end_iterator( x );
}
```

Again with similar definitions for the `const` versions. This eventually makes odeint work with gsl vectors as state types. The full code for these bindings is found in gsl_wrapper.hpp. It might look rather complicated but keep in mind that gsl is a pre-compiled C library.

##### Vector Space Algebra

As seen above, the standard way of performing algebraic operations on container-like state types in odeint is to iterate through the elements of the container and perform the operations element-wise on the underlying value type. This is realized by means of the `range_algebra` that uses Boost.Range for obtaining iterators of the state types. However, there are other ways to implement the algebraic operations on containers, one of which is defining the addition/multiplication operators for the containers directly and then using the `vector_space_algebra`. If you use this algebra, the following operators have to be defined for the state_type:

Name

Expression

Type

Semantics

```x + y```

`state_type`

Calculates the vector sum 'x+y'.

```x += y```

`state_type`

Performs x+y in place.

Scalar multiplication

```a * x ```

`state_type`

Performs multiplication of vector x with scalar a.

Assign scalar multiplication

```x *= a```

`state_type`

Performs in-place multiplication of vector x with scalar a.

Defining these operators makes your state type work with any basic Runge-Kutta stepper. However, if you want to use step-size control, some more functionality is required. Specifically, operations like maxi( |erri| / (alpha * |si|) ) have to be performed. err and s are state_types, alpha is a scalar. As you can see, we need element wise absolute value and division as well as an reduce operation to get the maximum value. So for controlled steppers the following things have to be implemented:

Name

Expression

Type

Semantics

Division

```x / y```

`state_type`

Calculates the element-wise division 'x/y'

Absolute value

```abs( x )```

`state_type`

Element wise absolute value

Reduce

```vector_space_reduce_impl< state_type >::reduce( state , operation , init )```

`value_type`

Performs the `operation` for subsequently each element of `state` and returns the aggregate value. E.g.

```init = operator( init , state );```

```init = operator( init , state )```

`...`

##### Point type

Here we show how to implement the required operators on a state type. As example we define a new class `point3D` representing a three-dimensional vector with components x,y,z and define addition and scalar multiplication operators for it. We use Boost.Operators to reduce the amount of code to be written. The class for the point type looks as follows:

```class point3D :
boost::multiplicative2< point3D , double > > >
{
public:

double x , y , z;

point3D()
: x( 0.0 ) , y( 0.0 ) , z( 0.0 )
{ }

point3D( const double val )
: x( val ) , y( val ) , z( val )
{ }

point3D( const double _x , const double _y , const double _z  )
: x( _x ) , y( _y ) , z( _z )
{ }

point3D& operator+=( const point3D &p )
{
x += p.x; y += p.y; z += p.z;
return *this;
}

point3D& operator*=( const double a )
{
x *= a; y *= a; z *= a;
return *this;
}

};
```

By deriving from Boost.Operators classes we don't have to define outer class operators like ```operator+( point3D , point3D )``` because that is taken care of by the operators library. Note that for simple Runge-Kutta schemes (like `runge_kutta4`) only the `+` and `*` operators are required. If, however, a controlled stepper is used one also needs to specify the division operator `/` because calculation of the error term involves an element wise division of the state types. Additionally, controlled steppers require an `abs` function calculating the element-wise absolute value for the state type:

```// only required for steppers with error control
point3D operator/( const point3D &p1 , const point3D &p2 )
{
return point3D( p1.x/p2.x , p1.y/p2.y , p1.z/p1.z );
}

point3D abs( const point3D &p )
{
return point3D( std::abs(p.x) , std::abs(p.y) , std::abs(p.z) );
}
```

Finally, we have to provide a specialization to calculate the infintity norm of a state:

```// also only for steppers with error control
namespace boost { namespace numeric { namespace odeint {
template<>
struct vector_space_norm_inf< point3D >
{
typedef double result_type;
double operator()( const point3D &p ) const
{
using std::max;
using std::abs;
return max( max( abs( p.x ) , abs( p.y ) ) , abs( p.z ) );
}
};
} } }
```

Again, note that the two last steps were only required if you want to use controlled steppers. For simple steppers definition of the simple `+=` and `*=` operators are sufficient. Having defined such a point type, we can easily perform the integration on a Lorenz system by explicitely configuring the `vector_space_algebra` in the stepper's template argument list:

```const double sigma = 10.0;
const double R = 28.0;
const double b = 8.0 / 3.0;

void lorenz( const point3D &x , point3D &dxdt , const double t )
{
dxdt.x = sigma * ( x.y - x.x );
dxdt.y = R * x.x - x.y - x.x * x.z;
dxdt.z = -b * x.z + x.x * x.y;
}

using namespace boost::numeric::odeint;

int main()
{

point3D x( 10.0 , 5.0 , 5.0 );
// point type defines it's own operators -> use vector_space_algebra !
typedef runge_kutta_dopri5< point3D , double , point3D ,
double , vector_space_algebra > stepper;
int steps = integrate_adaptive( make_controlled<stepper>( 1E-10 , 1E-10 ) , lorenz , x ,
0.0 , 10.0 , 0.1 );
std::cout << x << std::endl;
std::cout << "steps: " << steps << std::endl;
}
```

The whole example can be found in lorenz_point.cpp

Note For the most `state_types`, odeint is able to automatically determine the correct algebra and operations. But if you want to use your own `state_type`, as in this example with `point3D`, you have to manually configure the right algebra/operations, unless your `state_type` works with the default choice of `range_algebra` and `default_operations`.

gsl_vector, gsl_matrix, ublas::matrix, blitz::matrix, thrust