Implicit iterative methods oscillating?

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Discussion Overview

The discussion revolves around the stability and accuracy of implicit methods for numerically solving ordinary differential equations (ODEs), specifically focusing on the Implicit Midpoint method applied to a given differential equation. Participants explore the behavior of numerical solutions and the implications of step sizes on stability.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant expresses confusion about the oscillation of the numerical solution using the Implicit Midpoint method, questioning the expected stability of implicit methods.
  • Another participant suggests that the initial step size may be too large, potentially causing instability in the numerical solution.
  • A participant proposes that while implicit methods generally enhance stability, they do not guarantee it, and mentions the backward Euler method as particularly stable.
  • One participant emphasizes the importance of checking the absolute values of derivatives to determine appropriate step sizes, suggesting that the ODE in question is 'stiff' and recommending the Gear method as an alternative.
  • A later reply clarifies that oscillation does not necessarily indicate instability, but rather a poor approximation of the solution.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the reasons for the oscillation in the numerical solution, with differing views on the implications of step sizes and the nature of stability in implicit methods.

Contextual Notes

There are unresolved assumptions regarding the conditions for stability and the specific characteristics of the ODE being solved, as well as the appropriateness of different numerical methods for this case.

maistral
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I'm under the impression that any implicit method for numerically solving ODE should be providing high stability in exchange for accuracy.

I'm trying to solve the differential equation:
dy/dx = -1000y + 3000 - 2000exp(-x) with intial conditions (0,0).

I still can't understand how come the numerical solution using the Implicit Midpoint is oscillating if it is supposed to be a stable method. Judging from the values, they oscillate between ~0 and~2.
t989rt.png


Am I missing something here?
 
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Seems to me your initial step is relatively big: dy/dx (0) = 1000 so 0.05 makes y shoot off.
You sure there aren't any conditions at all for the stability ?

[edit] perhaps this helps -- but your midpoint is left as an exercise (nr 3 on p. 64...)
 
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I see. The implicit midpoint requiring an initial evaluation at dy/dx(0) at the evaluation (y0 + y1)/2 makes it shoot off. Thanks. Is it safe for me to say that implicit methods do strengthen stability, but not guarantee stability? (unless its backward Euler.. that thing is a tank)

I guess I'm really better off with the Gauss-Legendre RK or that BDF thing. Thanks!
 
I think I can agree with your statement. I think that's exactly the subject of the link I gave you.

I don't know what line of work/study you are in, but I find it very useful to check derivative absolute values, no matter which integrator is at work in the program I'm using.
Big values means you want to try small step sizes, but once the values are comparable and relatively smaller you want bigger steps to make reasonable progress.
For your expression I would say that's a 'stiff' ODE and the Gear method comes to mind. Sometimes you have a choice and you simply try one or two.

There's a truckload of litterature on this subject to be found on the internet nowadays, so: happy hunting !
 
The solution oscillating between 0 and 2 does not mean that it is unstable. It's just not a good approximation. An unstable solution grows without bound.

Chet
 
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