$\newcommand{\B}[1]{{\bf #1}} \newcommand{\R}[1]{{\rm #1}}$
Fourth Order Runge Kutta

Syntax
 yf = runge_kutta_4(f, ti, yi, dt)

Purpose
See Motivation below as well as the purpose described here. We are given a function $f : \B{R}^n \rightarrow \B{R}^n$, and a point $yi \in \B{R}^n$ such that an unknown function $y : \B{R} \rightarrow \B{R}^n$ satisfies the equations $$\begin{array}{rcl} y( ti ) & = & yi \\ y'(t) & = & f[t, y(t) ] \\ \end{array}$$ We use the Fourth order Runge-Kutta formula (see equation 16.1.2 of Numerical Recipes in Fortran, 2nd ed.) wish to approximate the value of $$yf = y( ti + \Delta t )$$

f
If t is a scalar and y is a vector with size $n$,       k = f(t, y)  returns a vector of size $n$ that is the value of $f(t, y)$ at the specified values.

ti
is a scalar that specifies the value of $ti$ in the problem above.

yi
is a vector of size $n$ that specifies the value of $yi$ in the problem above.

dt
is a scalar that specifies the value of $\Delta t$ in the problem above.

yf
is a vector of size $n$ that is the approximation for $y( t + \Delta t )$.

Motivation
This routine makes very few assumptions about the objects used to do these calculations. Thus, smart objects can be used for all sorts of purposes; for example, computing derivatives of the solution of an ODE. The table below lists the assumptions on the objects passed into runge_kutta_4. In this table, s and t are scalars, d is a decimal number (i.e., a float) and u and v are vectors with size $n$.
 operation result d * s scalar s + t scalar s * u vector with size $n$ d * u vector with size $n$ s * u vector with size $n$ u + v vector with size $n$

Source Code  def runge_kutta_4(f, ti, yi, dt) : k1 = dt * f(ti , yi) k2 = dt * f(ti + .5*dt , yi + .5*k1) k3 = dt * f(ti + .5*dt , yi + .5*k2) k4 = dt * f(ti + dt , yi + k3) yf = yi + (1./6.) * ( k1 + 2.*k2 + 2.*k3 + k4 ) return yf 
Example
1. The file runge_kutta_4_correct.py contains an example and test of using runge_kutta_4 to solve an ODE.
2. The file runge_kutta_4_ad.py contains an example and test of differentiating the numerical solution of an ODE.
3. The file runge_kutta_4_cpp.py contains an example and test of using pycppad adfun object to evaluate python functions with C++ speed of execution.