Advanced numerical solution of differential equation

In summary, the explicit Runge-Kutta scheme with a particular choice of $h$ applied to the equation $y'= y(1-y)$ has two spurious fixed points $y=0$ and $y=1$ when $h>2$. To investigate their stability, one can rearrange the scheme and plug in the fixed points to find that $y=0$ and $y=1$ satisfy the iteration, but further analysis with a computer algebra system is needed to determine if there are other real roots. To determine stability, one can use the linearized fixed point equation and check if its derivative is less than, greater than, or equal to 1.
  • #1
rickyflair
2
0
Show that the explicit Runge-Kutta scheme
\begin{equation} \frac {y_{n+1} -y_{n}}{h}= \frac{1}{2} [f(t,y_{n} + f(t+h, y_{n}+hk_{1})]
\end{equation}
where $k_{1} = f(t,y_{n})$

applied to the equation $y'= y(1-y)$ has two spurious fixed points if $h>2$.

Briefy describe how you would investigate their stability.

=> my attempt so far
from $y'= y(1-y)$

$y'= 0$

$y=0$ or
$y=1$ which are the true fixed points.
after that i rearranged the runge kutta scheme
\begin{equation} \frac {y_{n+1} -y_{n}}{h}= \frac{1}{2} [f(t,y_{n} + f(t+h, y_{n}+hk_{1})]
\end{equation}
\begin{equation} y_{n+1} = y_{n} + \frac{h}{2} [f(t,y_{n} + f(t+h, y_{n}+hf(t,y_{n})]
\end{equation}
i try to put the fixed points into above scheme and try to get two two Spurious fixed point for $y_{n}$ but i got struck. for the stability to describe i need to get two Spurious fixed point first. but in general please help to describe stability too because this part i really get confuse. Anyone please help me, it will be really helpful for my other problems too if i got this answer correctly.
 
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  • #2
rickyflair said:
Show that the explicit Runge-Kutta scheme
\begin{equation} \frac {y_{n+1} -y_{n}}{h}= \frac{1}{2} [f(t,y_{n} + f(t+h, y_{n}+hk_{1})]
\end{equation}
where $k_{1} = f(t,y_{n})$

applied to the equation $y'= y(1-y)$ has two spurious fixed points if $h>2$.

Briefy describe how you would investigate their stability.

=> my attempt so far
from $y'= y(1-y)$

$y'= 0$

$y=0$ or
$y=1$ which are the true fixed points.
after that i rearranged the runge kutta scheme
\begin{equation} \frac {y_{n+1} -y_{n}}{h}= \frac{1}{2} [f(t,y_{n} + f(t+h, y_{n}+hk_{1})]
\end{equation}
\begin{equation} y_{n+1} = y_{n} + \frac{h}{2} [f(t,y_{n} + f(t+h, y_{n}+hf(t,y_{n})]
\end{equation}
i try to put the fixed points into above scheme and try to get two two Spurious fixed point for $y_{n}$ but i got struck. for the stability to describe i need to get two Spurious fixed point first. but in general please help to describe stability too because this part i really get confuse. Anyone please help me, it will be really helpful for my other problems too if i got this answer correctly.

Your [itex]f(t,y)[/itex] doesn't actually depend on [itex]t[/itex], so you may as well drop it and write [itex]f(y) = y(1 - y)[/itex].

A fixed point of the iteration will satisfy [itex]y_{n+1} = y_n = y[/itex], which gives you
[tex]
0 = f(y + f(y + hf(y))).
[/tex] Now for [itex]y = 0[/itex] and [itex]y = 1[/itex] you have [itex]f(y) = 0[/itex], so they satisfy the above and are fixed points of the iteration. But [itex]f(y + f(y + hf(y)))[/itex] is here an eighth-order polynomial in [itex]y[/itex], so it might have other real roots aside from [itex]y = 0[/itex] and [itex]y = 1[/itex]. But determining that really requires the aid of a CAS.

For stability: a fixed point [itex]y[/itex] of [itex]y_{n+1} = g(y_n)[/itex] is stable if [itex]|g'(y)| < 1[/itex], unstable if [itex]|g'(y)| > 1[/itex] and indeterminate at linear order if [itex]|g'(y)| = 1[/itex].
 
  • #3
Thank you sir. that was really helpful.
 

FAQ: Advanced numerical solution of differential equation

What is the purpose of using advanced numerical methods to solve differential equations?

The purpose of using advanced numerical methods to solve differential equations is to approximate the solution of a given differential equation with a high degree of accuracy. This allows scientists to model and predict complex systems and phenomena, such as fluid dynamics, chemical reactions, and electrical circuits.

What are some examples of advanced numerical methods used to solve differential equations?

Examples of advanced numerical methods used to solve differential equations include the Runge-Kutta method, the Adams-Bashforth method, the Crank-Nicolson method, and the finite element method. These methods use a combination of iterative processes and approximation techniques to find solutions to differential equations.

How do advanced numerical methods differ from analytical methods for solving differential equations?

Advanced numerical methods use numerical techniques to approximate the solution of a given differential equation, whereas analytical methods involve solving the equation algebraically. Numerical methods are often used for complex or nonlinear equations that cannot be solved analytically.

What are the limitations of using advanced numerical methods to solve differential equations?

One limitation of using advanced numerical methods is that they can be computationally expensive and time-consuming. Additionally, the accuracy of the solution is dependent on the chosen method and the initial conditions of the differential equation. In some cases, numerical methods may also introduce small errors or instabilities in the solution.

How are advanced numerical methods for solving differential equations applied in real-world scenarios?

Advanced numerical methods for solving differential equations are used in a wide range of fields, including physics, engineering, economics, and biology. They are used to model and analyze various systems and phenomena, such as weather patterns, population dynamics, and stock market trends. These methods also play a crucial role in the development of new technologies and research in various scientific disciplines.

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