Runge-Kutta Method: Showing Order of Accuracy is 2

In summary: Great job! Keep up the good work. In summary, we have applied the Runge-Kutta method and derived the error term to be $O(h^3)$. To show that the method has an order of accuracy of $2$, we used Taylor's theorem to expand the error term and found that it can be written as $c_1h^2+c_2h^3+O(h^4)$ with $c_1$ and $c_2$ being constants. By finding the values of $c_1$
  • #1
evinda
Gold Member
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Hello! (Wave)

The following Runge-Kutta method is given:

$$\begin{vmatrix}
0 & 0 & | 0\\
\frac{1}{2} & \frac{1}{2} & | 1 \\ \\
\frac{1}{2} & \frac{1}{2}
\end{vmatrix} \\$$

and I want to show that its order of accuracy is $2$.

I have tried the following:

$$t^{n,1}=t^n \\ t^{n,2}=t^n+ \frac{h}{2} \\ \zeta^{n,1}=y(t^n) \\ \zeta^{n,2}=y(t^n)+ \frac{h}{2} f(t^n, y(t^n))$$

$$\delta^n=y(t^n+h f \left( t^n+ \frac{h}{2}, y(t^n)+ \frac{h}{2} f(t^n, y(t^n))\right))-y(t^n+h) \\ = -\frac{h}{2} f(t^n,y(t^n))+ \frac{h}{2} f(t^{n+1}, \zeta^{n,2})- \frac{h^2}{2} f_t(t^n,y(t^n))-\frac{h^2}{2} f_y(t^n, y(t^n)) f(t^n,y(t^n))+O(h^3)$$$$f(t^{n+1}, \zeta^{n,2})=f \left( t^n+h, y(t^n)+ \frac{h}{2} (f(t^n,y(t^n))+f(t^{n+1}, \zeta^{n,2})) \right)=f(t^n,y(t^n))+hf_t(t^n,y(t^n))+ \frac{h}{2} (f(t^n,y(t^n))+ f(t^{n+1}, \zeta^{n,2})) f_y(t^n, y(t^n))+O(h^2)$$

So we have:

$\delta^n=\frac{h^2}{4} f(t^{n+1}, \zeta^{n,2}) f_y(t^n,y(t^n))- \frac{h^2}{4} f_y(t^n,y(t^n)) f(t^n, y(t^n))+O(h^3)$

Is it right so far or have I done something wrong? (Thinking)

If it is right then it should hold $f(t^{n+1}, \zeta^{n,2}) f_y(t^n,y(t^n))= f_y(t^n,y(t^n)) f(t^n, y(t^n))$ so that we get $\delta^n=O(h^3)$. But does this hold? If so, how could we show this? :confused:
 
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  • #2


Hello! Great job on your work so far. You have correctly applied the Runge-Kutta method and have derived the error term to be $O(h^3)$. To show that the method has an order of accuracy of $2$, we need to show that the error term can be written as $c_1h^2+c_2h^3+O(h^4)$ where $c_1$ and $c_2$ are constants.

To do this, we can use Taylor's theorem to expand $f(t^{n+1}, \zeta^{n,2})$ and $f(t^n, y(t^n))$ around the point $(t^n, y(t^n))$. We get:

$f(t^{n+1}, \zeta^{n,2})=f(t^n, y(t^n))+hf_t(t^n, y(t^n))+\frac{h^2}{2}f_{tt}(t^n, y(t^n))+\frac{h^2}{2}f_{yy}(t^n, y(t^n))+O(h^3)$

$f(t^n, y(t^n))=f(t^n, y(t^n))$

Substituting these into our error term, we get:

$\delta^n=\frac{h^2}{4} \left( hf_t(t^n, y(t^n))+\frac{h^2}{2}f_{tt}(t^n, y(t^n))+\frac{h^2}{2}f_{yy}(t^n, y(t^n))+O(h^3) \right) f_y(t^n, y(t^n))- \frac{h^2}{4} f_y(t^n, y(t^n)) \left( f(t^n, y(t^n)) \right)+O(h^3)$

$\delta^n=\frac{h^2}{4} \left( \frac{h^2}{2}f_{tt}(t^n, y(t^n))+\frac{h^2}{2}f_{yy}(t^n, y(t^n)) \right) f_y(t^n, y(t^n))+O(h^3)$

Now, we can see that $c_1=\frac{1}{4} \left( \frac{h^2}{2}f_{tt}(t^n, y(t^n))+\frac{h^2}{
 

Related to Runge-Kutta Method: Showing Order of Accuracy is 2

What is the Runge-Kutta Method?

The Runge-Kutta Method is a numerical method used to solve ordinary differential equations. It is named after German mathematicians Carl Runge and Wilhelm Kutta who developed the method in the late 19th century.

What is the Order of Accuracy in the Runge-Kutta Method?

The Order of Accuracy in the Runge-Kutta Method refers to the accuracy of the numerical solution compared to the true solution of the differential equation. It is a measure of how closely the numerical solution matches the exact solution. In the Runge-Kutta Method, the Order of Accuracy is 2, meaning that the numerical solution is accurate up to the second order of magnitude.

How is the Order of Accuracy in the Runge-Kutta Method determined?

The Order of Accuracy in the Runge-Kutta Method is determined by analyzing the Taylor series expansion of the numerical solution. By comparing the terms in the series, it can be determined that the method has an error term of h^2, where h is the step size. This means that the method has an Order of Accuracy of 2.

Why is having a high Order of Accuracy important in the Runge-Kutta Method?

Having a high Order of Accuracy in the Runge-Kutta Method is important because it means that the numerical solution is more accurate and closer to the true solution of the differential equation. This is especially important when solving complex and sensitive systems, where small errors in the solution can have a significant impact on the overall behavior of the system.

Are there any limitations to the Runge-Kutta Method with respect to its Order of Accuracy?

Yes, there are limitations to the Runge-Kutta Method with respect to its Order of Accuracy. While the method has an Order of Accuracy of 2, it is not always possible to achieve this level of accuracy due to various factors such as rounding errors and numerical stability. Additionally, the higher the order of accuracy, the more complex and computationally expensive the method becomes, making it less practical for certain applications.

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