# ode

ordinary differential equation solver

## Calling Sequence

                y = ode(y0, t0, t, f)
                [y, w, iw] = ode([type,] y0, t0, t [,rtol [,atol]], f [,jac] [,w, iw])
                [y, rd, w, iw] = ode("root", y0, t0, t [,rtol [,atol]], f [,jac],ng, g [,w,iw])
                y = ode("discrete", y0, k0, kvect, f)

## Arguments

y0

  a real vector or matrix, the initial conditions.

t0

  a real scalar, the initial time.

t

  a real vector, the times at which the solution is computed.

f

  a function, external, string or list, the right hand side of the differential equation.

type

  a string, the solver to use. The available solvers are `"adams"`, `"stiff"`, `"rk"`, `"rkf"`, `"fix"`, `"discrete"`, `"roots"`.

rtol

  a real constant or real vector of the same size as `y`, the relative tolerance.

atol

  a real constant or real vectors of the same size as `y`, the absolute tolerance.

jac

  a function, external, string or list, the Jacobian of the function `f`.

w, iw

  real vectors. (INPUT/OUTPUT)

ng

  an integer.

g

  an external (function or character string or list).

k0

  an integer (initial time).

kvect

  an integer vector.

y

  a real vector or matrix. (OUTPUT)

rd

  a real vector. (OUTPUT)

## Description

`ode` solves explicit Ordinary Different Equations defined by:

\begin{eqnarray} \begin{array}{l} \dfrac{dy}{dt} = f(t,y)\\ y(t_0)=y_0 \end{array} \end{eqnarray}

It is an interface to various solvers, in particular to ODEPACK.

In this help, we only describe the use of `ode` for standard explicit ODE systems.

The simplest call of `ode` is: `y = ode(y0,t0,t,f)` where `y0` is the vector of initial conditions, `t0` is the initial time, `t` is the vector of times at which the solution `y` is computed and `y` is matrix of solution vectors `y=[y(t(1)),y(t(2)),...]`.

The input argument `f` defines the right hand side of the first order differential equation. This argument is a function with a specific header.

- If `f` is a Scilab function, its calling sequence must be

      ydot = f(t,y)

  where `t` is a real scalar (the time) and `y` is a real vector (the state) and `ydot` is a real vector (the first order derivative dy/dt).

- If `f` is a string, it is the name of a Fortran subroutine or a C compiled function. For example, if we call `ode(y0,t0,t,"fex")`, then the subroutine `fex` is called.

  The Fortran routine must have the header:

      fex(n,t,y,ydot)

  where `n` is an integer, `t` is a double precision scalar, `y` and `ydot` are double precision vectors.

  The C function must have the header:

      fex(int *n,double *t,double *y,double *ydot)

  where `t` is the time, `y` the state and `ydot` is the state derivative (dy/dt).

  This external can be build in a OS independent way using ilib_for_link and dynamically linked to Scilab by the link function.

- It may happen that the simulator `f` needs extra arguments. In this case, we can use the following feature. The `f` argument can also be a list `lst=list(simuf,u1,u2,...un)` where `simuf` is a Scilab function with syntax: `ydot = f(t,y,u1,u2,...,un)` and `u1`, `u2`, ..., `un` are extra arguments which are automatically passed to the simulator `simuf`.

The function `f` can return a p-by-q matrix instead of a vector. With this matrix notation, we solve the `n=p+q` ODE's system `dY/dt=F(t,Y)` where `Y` is a `p x q` matrix. Then initial conditions, `Y0`, must also be a `p x q` matrix and the result of `ode` is the `p-by-q(T+1)` matrix `[Y(t_0),Y(t_1),...,Y(t_T)]`.

The tolerances `rtol` and `atol` are thresholds for relative and absolute estimated errors. The estimated error on `y(i)` is: `rtol(i)*abs(y(i))+atol(i)` and integration is carried out as far as this error is small for all components of the state. If `rtol` and/or `atol` is a constant `rtol(i)` and/or `atol(i)` are set to this constant value. Default values for `rtol` and `atol` are respectively `rtol=1.d-5` and `atol=1.d-7` for most solvers and `rtol=1.d-3` and `atol=1.d-4` for `"rfk"` and `"fix"`.

For stiff problems, it is better to give the Jacobian of the RHS function as the optional argument `jac`. The Jacobian is an external i.e. a function with specified syntax, or the name of a Fortran subroutine or a C function (character string) with specified calling sequence or a list.

- If `jac` is a function the syntax should be `J=jac(t,y)` where `t` is a real scalar (time) and `y` a real vector (state). The result matrix `J` must evaluate to df/dx i.e. `J(k,i) = dfk/dxi` where `fk` is the `k`-th component of `f`.

- If `jac` is a character string it refers to the name of a Fortran subroutine or a C function.

  The Fortran routine must have the header:

      subroutine fex(n,t,y,ml,mu,J,nrpd)
      integer n,ml,mu,nrpd
      double precision t,y(*),J(*)

  The C function must have the header:

      void fex(int *n,double *t,double *y,int *ml,int *mu,double *J,int *nrpd,)

  In most cases you have not to refer `ml`, `mu` and `nrpd`.

- If `jac` is a list the same conventions as for `f` apply.

Optional arguments `w` and `iw` are vectors for storing information returned by the integration routine (see ode_optional_output for details). When these vectors are provided in RHS of `ode` the integration re-starts with the same parameters as in its previous stop.

More options can be given to ODEPACK solvers by using `%ODEOPTIONS` variable. See odeoptions.

## The solvers

The type of problem solved and the method used depend on the value of the first optional argument `type` which can be one of the following strings:

<not given>:

  `lsoda` solver of package ODEPACK is called by default. It automatically selects between nonstiff predictor-corrector Adams method and stiff Backward Differentiation Formula (BDF) method. It uses nonstiff method initially and dynamically monitors data in order to decide which method to use.

"adams":

  This is for nonstiff problems. `lsode` solver of package ODEPACK is called and it uses the Adams method.

"stiff":

  This is for stiff problems. `lsode` solver of package ODEPACK is called and it uses the BDF method.

"rk":

  Adaptive Runge-Kutta of order 4 (RK4) method.

"rkf":

  The Shampine and Watts program based on Fehlberg's Runge-Kutta pair of order 4 and 5 (RKF45) method is used. This is for non-stiff and mildly stiff problems when derivative evaluations are inexpensive. This method should generally not be used when the user is demanding high accuracy.

"fix":

  Same solver as `"rkf"`, but the user interface is very simple, i.e. only `rtol` and `atol` parameters can be passed to the solver. This is the simplest method to try.

"root":

  ODE solver with rootfinding capabilities. The `lsodar` solver of package ODEPACK is used. It is a variant of the `lsoda` solver where it finds the roots of a given vector function. See help on ode_root for more details.

"discrete":

  Discrete time simulation. See help on ode_discrete for more details.

## Examples

In the following example, we solve the Ordinary Differential Equation `dy/dt=y^2-y*sin(t)+cos(t)` with the initial condition `y(0)=0`. We use the default solver.

    function ydot=f(t,y)
    	ydot=y^2-y*sin(t)+cos(t)
    endfunction
    y0=0;
    t0=0;
    t=0:0.1:%pi;
    y = ode(y0,t0,t,f);
    plot(t,y)

In the following example, we solve the equation `dy/dt=A*y`. The exact solution is `y(t)=expm(A*t)*y(0)`, where `expm` is the matrix exponential. The unknown is the 2-by-1 matrix y(t).

    function ydot=f(t,y)
        ydot=A*y
    endfunction
    function J=Jacobian(t,y)
        J=A
    endfunction
    A=[10,0;0,-1];
    y0=[0;1];
    t0=0;
    t=1;
    ode("stiff",y0,t0,t,f,Jacobian)
    // Compare with exact solution:
    expm(A*t)*y0

In the following example, we solve the ODE `dx/dt = A x(t) + B u(t)` with `u(t)=sin(omega*t)`. Notice the extra arguments of `f`: `A`, `u`, `B`, `omega` are passed to function `f` in a list.

    function xdot=linear(t,x,A,u,B,omega)
        xdot=A*x+B*u(t,omega)
    endfunction
    function ut=u(t,omega)
        ut=sin(omega*t)
    endfunction
    A=[1 1;0 2];
    B=[1;1];
    omega=5;
    y0=[1;0];
    t0=0;
    t=[0.1,0.2,0.5,1];
    ode(y0,t0,t,list(linear,A,u,B,omega))

In the following example, we solve the Riccati differential equation `dX/dt=A'*X + X*A - X'*B*X + C` where initial condition `X(0)` is the 2-by-2 identity matrix.

    function Xdot=ric(t,X,A,B,C)
        Xdot=A'*X+X*A-X'*B*X+C
    endfunction
    A=[1,1;0,2];
    B=[1,0;0,1];
    C=[1,0;0,1];
    y0=eye(A);
    t0=0;
    t=0:0.1:%pi;
    X = ode(y0,t0,t,list(ric,A,B,C))

In the following example, we solve the differential equation `dY/dt=A*Y` where the unknown `Y(t)` is a 2-by-2 matrix. The exact solution is `Y(t)=expm(A*t)`, where `expm` is the matrix exponential.

    function ydot=f(t,y,A)
        ydot=A*y;
    endfunction
    A=[1,1;0,2];
    y0=eye(A);
    t0=0;
    t=1;
    ode(y0,t0,t,list(f,A))
    // Compare with the exact solution:
    expm(A*t)
    ode("adams",y0,t0,t,f)

## With a compiler

The following example requires a C compiler.

    // ---------- Simple one dimension ODE (C coded external)
    ccode=['#include <math.h>'
           'void myode(int *n,double *t,double *y,double *ydot)'
           '{'
           '  ydot[0]=y[0]*y[0]-y[0]*sin(*t)+cos(*t);'
           '}']
    mputl(ccode,TMPDIR+'/myode.c') //create the C file
    // Compile
    ilib_for_link('myode','myode.c',[],'c',TMPDIR+'/Makefile',TMPDIR+'/loader.sce');
    exec(TMPDIR+'/loader.sce') //incremental linking
    y0=0;
    t0=0;
    t=0:0.1:%pi;
    y = ode(y0,t0,t,'myode');

## See Also

- ode_discrete
- ode_root
- dassl
- impl
- odedc
- odeoptions
- csim
- ltitr
- rtitr
- intg

## Bibliography

Alan C. Hindmarsh, "lsode and lsodi, two new initial value ordinary differential equation solvers", ACM-Signum newsletter, vol. 15, no. 4 (1980), pp. 10-11.

## Used Functions

The associated routines can be found in SCI/modules/differential_equations/src/fortran directory: lsode.f lsoda.f lsodar.f