Stability of an ODE and Euler's method

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Discussion Overview

The discussion revolves around the stability of numerical methods for solving ordinary differential equations (ODEs), specifically focusing on Euler's method. Participants explore the implications of stability conditions and the behavior of solutions over time, questioning the applicability of these methods to various types of ODEs.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about the stability of Euler's method, noting that it appears to yield values that are always decreasing, which raises questions about its usefulness.
  • Another participant asserts that Euler's method is largely ineffective compared to other numerical integration methods, suggesting it may be "completely useless."
  • A different participant questions whether any numerical method can be stable for ODEs with unstable solution families, indicating that the Jacobian's properties might imply instability across methods.
  • This participant also suggests that if an ODE does not have solutions that are monotonically decreasing, it may not be solvable accurately using numerical methods, leading to concerns about the growth of errors in unstable ODEs.

Areas of Agreement / Disagreement

Participants express varying levels of skepticism regarding the effectiveness of Euler's method and the implications of stability conditions. There is no consensus on the applicability of numerical methods to unstable ODEs, with some participants questioning the feasibility of accurate solutions in such cases.

Contextual Notes

Participants reference stability conditions from external sources, indicating that these conditions may apply to various numerical methods, including the trapezoid rule. However, the discussion reveals uncertainty about the implications of these conditions on the accuracy of numerical solutions.

Master J
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I have been thinking about numerical methods for ODEs, and the whole notion of stability confuses me.

Take Euler's method for solving an ODE:

U_n+1 = U_n + h.A.U_n

where U_n = U_n( t ), A is the Jacobian and h is step size.

Rearrange:

U_n+1 = ( 1 + hA ).U_n

This method is only stable if (1 + hA) < 1 ( using the eigenvalues of A). But what does this mean!?? Every value of my function that I am numerically getting is less than the previous value. This seems rather useless, I don't get it? It appears to me that this method can only be used on functions that are strictly decreasing for all increasing t ?
 
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Master J said:
This seems rather useless

Yup. Euler's (forward difference) method IS "rather useless". In fact compared with almost any other numerical integration method, its not so much "rather useless" as "completely useless".

But it's a nice example of something that "obviosuly" look like a good idea, but turns out not to be.
 
Well, I'm still confused.

Say I have an ODE who's solution family y(t) is unstable. That is, for increasing t, the solution curves diverge from each other. In this case, J = df(y, t)/dy < 0.

So does this mean that ANY numerical method I use to solve this ODE will be unstable? With reference to http://courses.engr.illinois.edu/cs450/sp2010/odestability.pdf? there is a condition for all the methods, even the trapezoid rule etc. to be stable. And in each of these it implies that numerical values for each succesive value of y(t) are less than the previous, ie. y(t+h) < y(t).

So, in essence, what I gather here is that unless an ODE has the property that the magnitude of each value of the function y is LESS than the previous value, then it CANNOT be solved with a numerical method accurately?
Or, in another way, errors will always grow in solving an unstable ODE?

All this seems rather strange to me then. We cannot solve an ODE accurately unless the function is monotonically decreasing? What rather tiny area of applicability then!
 
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