Graduate Numerically calculating the solution for a non-homogeneous ODE system

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The discussion centers on solving a system of non-homogeneous ordinary differential equations (ODEs) numerically, specifically questioning the applicability of the Crank-Nicolson method. It is noted that Crank-Nicolson is typically used for partial differential equations, and using it for ODEs may lead to confusion, particularly regarding the matrix rank. The trapezoidal rule is suggested as an alternative, with clarification that non-homogeneous terms can be incorporated into the discretization matrix. Additionally, the possibility of solving the system analytically using Laplace transforms is mentioned, along with the suggestion to explore Runge-Kutta methods for numerical solutions. Overall, various methods for addressing non-homogeneous ODEs are discussed, emphasizing the importance of appropriate numerical techniques.
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I have been solving system of homogeneous ODE numerically using Crank-nicolson (CN) method but now I have a system of non-homogeneous ODE. It would seem that CN would not work since the rank of the matrix will be less than the dimension of the matrix. Is there any other method that can numerically calculate a system of non-homogeneous ODE?
 
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What are the equations?
 
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It is like

\dot{x_1}=x_1-x_2+x_3+a
\dot{x_2}=x_1+2x_2+x_3 +b
\dot{x_3}=-x_1+x_2+x_3+c

where a,b and c are constants w.r.t. time
 
The Crank-Nicolson scheme is for PDE's, specifically for diffusion equations. How do you use it in a system of ODE's? If you just average x1,x2 and x3 over the current and next time step, you are actually applying the trapezium rule method. Anyway, if the nonhomogeneous terms are constants, they will simply appear on the diagonal of your discretization matrix.
 
Yes I just realized that it is called the trapezium method. I do not understand why they are on the diagonal. Using the case that I provided, how should I construct the matrix?
 
I may be wrong here, but I'm pretty sure this system can be solved analytically with Laplace transforms. If that's not what you're after, the trapezoidal rule should work too.
 
Have you tried runge-kutta methods? I've been using it to solve some classical gravitational dynamics which have this level of difficult.
 

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