Can Fixed Point Iteration Solve Exponential Sum Equations?

In summary, the conversation discusses a problem in solving an equation of the form a*exp(a*x)+b*exp(b*x)-c=0, where the difference between a and b can be large. Different methods such as interval bisection and fixed-point iteration are suggested as potential solutions. It is mentioned that the equation can be solved using fixed-point iteration by writing it in the form x = f(x) and starting with an initial guess. The conversation ends with a request for clarification on the method used.
  • #1
scientist04
1
0
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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  • #2
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!
 
  • #3
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.
 
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  • #4
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?
 
  • #5
RandomMystery said:
How did you do this!
Is it Newton's method?
No. He just solved ae^ay+be^bx=c for y.
 
  • #6
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".
 
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1. What is an exponential sum equation?

An exponential sum equation is a mathematical expression that involves a sum of exponential functions, where the exponent varies over a certain range of values.

2. What is the purpose of an exponential sum equation?

Exponential sum equations are used to model and analyze a wide range of real-world phenomena, such as population growth, economic trends, and signal processing.

3. How is an exponential sum equation solved?

There is no general method for solving exponential sum equations, as the approach depends on the specific form of the equation. Some techniques include using properties of exponential functions, transforming the equation into a linear form, or using numerical methods.

4. What are the applications of exponential sum equations in science?

Exponential sum equations are commonly used in fields such as physics, biology, economics, and engineering to model and predict various phenomena, including growth, decay, and oscillations.

5. Are there any limitations to using exponential sum equations?

While exponential sum equations can accurately model many natural phenomena, they may not always capture the complexity of real-world systems. In some cases, other mathematical models may be more suitable for describing the behavior of a system.

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