Runge-Kutta 4(5)

Description

Called by xcos, Runge-Kutta is a numerical solver providing an efficient fixed-size step method to solve Initial Value Problems of the form:

CVode and IDA use variable-size steps for the integration.

A drawback of that is the unpredictable computation time. With Runge-Kutta, we do not adapt to the complexity of the problem, but we guarantee a stable computation time.

As of now, this method is explicit, so it is not concerned with Newton or Functional iterations, and not advised for stiff problems.

It is an enhancement of the Euler method, which approximates y_n+1 by truncating the Taylor expansion.

By convention, to use fixed-size steps, the program first computes a fitting h that approaches the simulation parameter max step size.

An important difference of Runge-Kutta with the previous methods is that it computes up to the fourth derivative of y, while the others only use linear combinations of y and y'.

Here, the next value is determined by the present value y_n plus the weighted average of four increments, where each increment is the product of the size of the interval, h, and an estimated slope specified by the function f(t,y):

• k1 is the increment based on the slope at the beginning of the interval, using y_n (Euler's method),
• k2 is the increment based on the slope at the midpoint of the interval, using y_n + h*k1/2 ,
• k3 is again the increment based on the slope at the midpoint, but now using y_n + h*k2/2
• k4 is the increment based on the slope at the end of the interval, using y_n + h*k3

We can see that with the ki, we progress in the derivatives of y_n . So in k4, we are approximating y⁽⁴⁾_n , thus making an error in O(h⁵) .

So the total error is number of steps * O(h⁵) . And since number of steps = interval size / h by definition, the total error is in O(h⁴) .

That error analysis baptized the method Runge-Kutta 4(5), O(h⁵) per step, O(h⁴) in total.

Although the solver works fine for max step size up to 10^-3 , rounding errors sometimes come into play as we approach 4*10^-4 . Indeed, the interval splitting cannot be done properly and we get capricious results.

Examples

The integral block returns its continuous state, we can evaluate it with Runge-Kutta by running the example:

// Import the diagram and set the ending time
loadScicos();
loadXcosLibs();
importXcosDiagram("SCI/modules/xcos/examples/solvers/ODE_Example.xcos");
scs_m.props.tf = 5000;

// Select the solver Runge-Kutta and set the precision
scs_m.props.tol(6) = 6;
scs_m.props.tol(7) = 10^-2;

// Start the timer, launch the simulation and display time
tic();
try xcos_simulate(scs_m, 4); catch disp(lasterror()); end
t = toc();
disp(t, "Time for Runge-Kutta:");

The Scilab console displays:

Time for Runge-Kutta:
 8.906
            

Now, in the following script, we compare the time difference between Runge-Kutta and CVode by running the example with the five solvers in turn: Open the script

Time for BDF / Newton:
 18.894

Time for BDF / Functional:
 18.382

Time for Adams / Newton:
 10.368

Time for Adams / Functional:
 9.815

Time for Runge-Kutta:
 4.743
            

These results show that on a nonstiff problem, for relatively same precision required and forcing the same step size, Runge-Kutta is faster.

Variable-size step ODE solvers are not appropriate for deterministic real-time applications because the computational overhead of taking a time step varies over the course of an application.

See Also

• LSodar
• CVode
• IDA
• Dormand-Prince 4(5)
• Implicit Runge-Kutta 4(5)
• Comparisons
• ode
• ode_discrete
• ode_root
• odedc
• impl

Bibliography

Sundials Documentation

History