Can Fixed Point Iteration Solve Exponential Sum Equations?

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Discussion Overview

The discussion revolves around solving an equation of the form a*exp(a*x) + b*exp(b*x) - c = 0, particularly focusing on the challenges posed by large differences between constants a and b. Participants explore numerical methods, including fixed-point iteration and interval bisection, to find stable solutions.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant describes the difficulty in solving the equation due to the large disparity between constants a and b, suggesting that numerical algorithms have failed.
  • Another participant proposes interval bisection as a guaranteed converging method if a suitable interval containing a root is chosen, noting the monotonic nature of the function's derivative.
  • A different participant suggests rewriting the equation in the form x = f(x) and applying fixed-point iteration, providing a specific function f(x) that worked successfully for their chosen values of a, b, and c.
  • There is a query about whether the method used is Newton's method, which is clarified to be fixed-point iteration instead.
  • One participant references external resources, including a Wikipedia page and books on numerical analysis, to support the use of fixed-point iteration.

Areas of Agreement / Disagreement

Participants do not reach a consensus on a single method, as multiple approaches are discussed, including interval bisection and fixed-point iteration, with varying levels of endorsement and success reported.

Contextual Notes

Some methods discussed may depend on specific assumptions about the values of a, b, and c, and the effectiveness of the proposed solutions may vary based on these parameters.

scientist04
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Hi,

I currently need to solve a problem which leads to an equation of the form a*exp(a*x)+b*exp(b*x)-c=0. The difference between a and b can often become very big, like a=1000 and b=1. Therefore numerical algorithms I tried so far, fail to solve this equation. Has anyone got any clues on a stable method to solve this equation?

Thanks in advance
 
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I assume you mean a and b are given real constants and you want to solve for a real value of x.

If all else fails, interval bisection is guaranteed to converge if you start with an interval that contains a root.

The derivative of the function is a^2 exp(ax) + b^2 exp(bx) which is positive for any values of a and b, so the function is always monotonic. If you don't know a better guess, you can find an interval that brackets the root by taking wider and wider intervals like
-1<x<1 , -2<x<2, -4<x<4, -8<x<8, -16<x<16, etc.

That is not quick and elegant, but it should work. Once you have got some solutions, you may understand the problem better and be able to invent a better algorithm.

If the equation is in complex arithmetic, things may get much more complicated!
 
You might write the equation in the form

[tex]x = f(x)[/tex]
where
[tex]f(x) = \frac{1}{a} \ln \left( \frac{c - b \exp(bx)}{a} \right)[/tex]

and apply fixed-point iteration. I.e.,

Let [tex]x_0 = \text {initial guess}[/tex]
and [tex]x_{n+1} = f(x_n)[/tex]

It worked like a charm with me for a = 1000, b = 1, c = 3422.23 (chosen to have a root x = 0.00123), starting with x_0 = 0.0000001.
 
Last edited:
awkward said:
You might write the equation in the form

[tex]x = f(x)[/tex]
where
[tex]f(x) = \frac{1}{a} \ln \left( \frac{c - b \exp(bx)}{a} \right)[/tex]

and apply fixed-point iteration. I.e.,

Let [tex]x_0 = \text {initial guess}[/tex]
and [tex]x_{n+1} = f(x_n)[/tex]

It worked like a charm with me for a = 1000, b = 1, c = 3422.23 (chosen to have a root x = 0.00123), starting with x_0 = 0.0000001.
How did you do this!
Is it Newton's method?
 
RandomMystery said:
How did you do this!
Is it Newton's method?
No. He just solved ae^ay+be^bx=c for y.
 
RandomMystery said:
How did you do this!
Is it Newton's method?

As I said, it's fixed point iteration:

http://en.wikipedia.org/wiki/Fixed_point_iteration

You can find many applications of this method in two books on numerical analysis by Acton, "Numerical Methods That Work" and "Real Computing Made Real".
 
Last edited:

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