Setting Up Inverse Problems

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In summary, the conversation discusses an operator equation involving a differential equation and its inverse. It is assumed that the operators have inverses and that they commute with each other. The conversation also touches on constructing a Green's function for the inverse problem and the implications of different boundary conditions.
  • #1
member 428835
Hi PF!

I'm reading an article and there is a differential equation cast as an operator equation: $$f_n-d_x^2 f_n = \lambda f$$ where ##f_n = \partial_n f##, which is derivative of ##f## normal to a given parameterized curve. The author casts the ODE as $$B[f_n] = \lambda A[f_n]:\\ B[f_n] \equiv f_n-d_x^2 f_n,\\A[f_n] \equiv f.$$

So if we simply take ##A^{-1}## and ##B^{-1}## to both sides of the operator equation we have $$A^{-1}[f_n] = \lambda B^{-1}[f_n]$$

However, if we rewrite the operator equation as $$B[f_n] = \lambda f \implies\\ f_n = \lambda B^{-1}f.$$

Recall ##A[f_n] \equiv f \implies A^{-1}[f] \equiv f_n##. Then the rewritten operator equation is $$A^{-1}[f] = \lambda B^{-1}[f].$$

Notice one inverse equation operates on ##f## and the other on ##f_n##. How can I tell which is correct? (I know it is ##f##, just not sure where the logic went wrong).

PS sorry, I can't figure out how to label equations here.
 
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  • #2
There are two hidden assumptions embedded in the above steps:

1. The operators A and B both have inverses.
2. The inverse operators ##A^{-1}## and ##B^{-1}## commute with one another.

The second one is implicitly assumed when you say:
joshmccraney said:
So if we simply take ##A^{-1}## and ##B^{-1}## to both sides of the operator equation we have $$A^{-1}[f_n] = \lambda B^{-1}[f_n]$$
If we apply ##A^{-1}B^{-1}## to both sides of the original equation we get:

$$A^{-1}B^{-1}B[f_n] = \lambda A^{-1}B^{-1} A[f_n]$$
which is
$$A^{-1}[f_n] = \lambda A^{-1}B^{-1}A[f_n]$$
but we can't eliminate ##A^{-1}## and ##A## from the RHS unless ##A^{-1}## commutes with ##B^{-1}##.

We run into the same difficulty if we apply ##B^{-1}A^{-1}## to both sides.

Also, to number an equation set it between the delimiters \begin{equation} and \end{equation}. It will then be autonumbered. To number every line in a sequence of equations, use \begin{align} and \end{align}. I've used italics here so that the codes look like codes rather than functioning as codes. Don't use italics when you want to use these codes to make numbered equations.
 
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  • #3
Thanks for the response! I had two follow-up questions: first, what determines "commutes"? I thought if the operators were linear they commuted; is this not the case?

Secondly, when I used the beginalign and endalign preferences and click PREVIEW it updated the equations on every preview click, so very quickly my equation count was in the 20s. Is this just a bug?
 
  • #4
joshmccraney said:
Thanks for the response! I had two follow-up questions: first, what determines "commutes"? I thought if the operators were linear they commuted; is this not the case?
That's right. Matrices are linear operators when used to pre-multiply column vectors, but matrix multiplication is in general non-commutative.

A nice geometric example of commutativity failing is for the 2D Euclidean vector space, where linear operator A rotates a point by 90 degrees anti-clockwise around the origin, and linear operator B reflects a point in the y- axis. Then AB takes the point (1,1) to (-1,-1), via (-1,1), but BA takes (1,1) to itself, via (-1,1).
BA is in fact a reflection in the line y=x, while AB is a reflection in the line y=-x.
Secondly, when I used the beginalign and endalign preferences and click PREVIEW it updated the equations on every preview click, so very quickly my equation count was in the 20s. Is this just a bug?
That sounds like a bug in the MathJax engine that implements latex on the web. One solution might be to, once you're happy with the preview, copy the edit version of your draft post to an editor program (eg Notepad on Windows), delete the draft on the PF web page, then start a new post and copy the text back from the editor to the new draft post. That might reset the equation counter. If that fails, you might have to close the PF page on your browser before remaking the post.
 
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  • #5
Hey, what a good example; thanks! Okay, so I think I have one last question on this matter for you. Let's say I'm solving the inverse problem $$A^{-1}[f] = \lambda B^{-1}[f].$$
We know $$B^{-1}[f] = \int_0^1 G f$$ where I assume ##f=f(x):x\in[0,1]##. ##G## is constructed via ##B[G] = \delta##, where ##\delta## is the Dirac delta function, and thus ##G## is the Green's function of ##B##. When constructing ##G## we need boundary conditions. How would these two situations be different when constructing ##G##, say ##f_n = 0## at ##x=0,1## versus ##f = 0## at ##x=0,1##? Would the ##f_n## boundary conditions imply ##G = 0## at ##x=0,1## since ##B## itself operates on ##f_n##? Then how would we deal with separate problem where boundary conditions are ##f = 0## at ##x=0,1##?
 

1. What is meant by an inverse problem in the context of scientific research?

An inverse problem refers to a type of problem where the goal is to determine the unknown input or cause of a system based on the observed output or effect. In other words, it involves working backward from the result to find the cause or solution.

2. What are some common examples of inverse problems in scientific fields?

Inverse problems can be found in various scientific fields, such as physics, engineering, and geology. Some common examples include determining the location and size of underground oil or mineral deposits based on seismic data, identifying the structure and composition of a material using scattering techniques, and reconstructing the trajectory of a projectile based on its impact location.

3. What are the main challenges in setting up inverse problems?

One of the main challenges in setting up inverse problems is dealing with the inherent uncertainty and noise in the data. This can be caused by measurement errors, modeling inaccuracies, or the complexity of the system itself. Additionally, the lack of complete or precise information about the system can make it difficult to accurately solve the inverse problem.

4. What are some techniques used for solving inverse problems?

There are various techniques used for solving inverse problems, including regularization, optimization, and Bayesian inference. These methods involve incorporating prior knowledge or assumptions about the system to constrain the solution and make it more stable and realistic.

5. How can the validity of the solution to an inverse problem be evaluated?

The validity of the solution to an inverse problem can be evaluated by comparing it to independent data or measurements, performing sensitivity analysis to assess the impact of uncertainties, and using statistical methods to quantify the confidence or uncertainty in the solution. It is also important to consider the physical plausibility of the solution and whether it aligns with known laws and principles in the field.

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