Efficient Numerical Method for Inverse Integration with Known Constants a, b, c

In summary, the person is seeking help with computing a complicated integral numerically and also its inverse. The function is too complicated to rearrange, so an iterative method is needed. It is unclear if this is called an 'inverse integration,' but the person is looking for a numerical method to solve it, potentially involving finding the roots of an equation. They provide an integral for reference and mention that a, b, and c are known constants.
  • #1
jimbo_durham
13
0
I have a complicated integral which i need to compute numerically. I can do this in C++ using a version of Simpson's rule. I also need to compute the inverse of this integral (presumably this is what it is called) ie i have

d=integral(f(x)dx)

and i need to be able to compute x given a value of d.

The function f(x) is too complicated to simply rearrange, so i need an iterative way of guessing x, running it in the program, seeing what value of d is given, and then improving my guess of x until i reach my starting d value.

This seems on the surface to be a simple iteration problem, however i cannot find an efficent way of doing this.

Can anyone tell me of a nice way of solving this problem (and even if it is called an 'inverse integration' or not?) and if there is a name given to the numerical method of solving it.

It has been mentioned that there is a 'press' method however i can find no mention of this. Prehaps it involves finding the roots of an equation which takes the value of the integral evaluated at some guessed x, and the roots give the point where the guessed x corresponds to the known d, thus solving the problem? does this ring any bells?

sorry for the long post, thanks in advance for your help
 
Last edited:
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  • #2
Well, one way would be:
Let:
[tex]y(x)=y(0)+\int_{x_{0}}^{x}f(t)dt[/tex]
Or, that is:
[tex]y=y_{0}+\int_{x(0)}^{x(y)}f(t)dt[/tex]
Differentiating the latter expression with respect to y yields:
[tex]1=f(x(y))\frac{dx}{dy}[/tex]
That is, you may solve the following differential equation numerically:
[tex]\frac{dx}{dy}=\frac{1}{f(x)}, x(y_{0})=x(y_{0})[/tex]
 
  • #3
i am not sure how to apply that, i think it is worth me giving you the integral;



[tex]
\begin{equation}

d_{M}=c_{1}\cdot sinh\left[c_{2}\int^{b}_{a}\left[\left(1+z\right)^{2}\cdot\left(1+c_{3}\cdot z\right)-z\cdot\left(2+z\right)\cdot c_{4}\right]^{-\frac{1}{2}}dz\right]

\end{equation}
[/tex]

i am trying to find z given a value of [tex]d_{M}[/tex].
 
  • #4
note in formula, [tex]a, b, c[/tex] are known constants
 

1. What is an inverse integral numerically?

An inverse integral numerically is a method used to approximate the inverse of a definite integral. It involves using numerical integration techniques to calculate the integral of a function, and then using the result to find the inverse of the function.

2. Why is it important to calculate inverse integrals numerically?

Inverse integrals are useful in solving problems involving inverse functions, such as optimization and curve fitting. Calculating them numerically allows for a more accurate and efficient method compared to analytical techniques.

3. What are some common numerical methods used to calculate inverse integrals?

Some common methods include the Newton's method, bisection method, and secant method. These methods involve approximating the inverse function by iteratively solving for the roots of the original function.

4. Are there any limitations to calculating inverse integrals numerically?

Yes, there are some limitations to numerical methods for inverse integrals. These methods are only approximations and may not always provide an exact solution. Additionally, they may not work for all types of functions or may require a large number of iterations for complex functions.

5. How can we improve the accuracy of inverse integral numerical calculations?

One way to improve accuracy is by using more advanced numerical methods, such as the Brent's method or the secant method with false position. Additionally, using smaller step sizes and increasing the number of iterations can also improve accuracy.

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