Help with reparameterizing

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In summary, the conversation is about reparameterizing a curve with respect to arc length and using the formula for arclength to do so. The steps involve finding the arclength as a function of t and then solving for t as a function of s to substitute into the parametric equations.
  • #1
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I think that I'm just having problems with my derivitives and integrals. If someone one could show me what the steps of this problem look like it would be greatly appreciated. "Reparameterize the curve with respect to the arc length measured from the point where t=0 in the direction of increasing t. r(t)=e^t*sint i +e^t*cost j"
 
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  • #2
Looks like homework to me!

Step 1: If you want to use arc-length as a parameter, then you will need to know what the arc-length is! The formula for arclength, given x and y in terms of parameter t, taking t= 0 as starting point and t>0 as positive arclength, is:
[tex]s(t)= \int_0^t\sqrt{x'^2+y'^2}dt[/tex].

Step 2: That will give you arclength, s, as a function of t. Now solve for t as a function of s and substitute that formula into your parametric equations.
 
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  • #3


Reparameterizing a curve means to change the parameter used to describe the curve. In this case, we are asked to reparameterize the curve with respect to the arc length, which is the distance measured along the curve.

To do this, we first need to find an expression for the arc length of the curve. This can be done using the arc length formula:

s = ∫√(x'(t)^2 + y'(t)^2) dt

where x'(t) and y'(t) are the derivatives of the x and y components of the curve with respect to t.

In this case, we have r(t) = e^t*sint i + e^t*cost j, so x(t) = e^t*sint and y(t) = e^t*cost. Taking the derivatives, we get x'(t) = e^t*sint + e^t*cost and y'(t) = e^t*cost - e^t*sint.

Plugging these into the arc length formula, we get:

s = ∫√(e^2t*sint^2 + e^2t*cost^2) dt

= ∫√(e^2t) dt

= ∫e^t dt

= e^t + C

Now, we can use this expression for the arc length to reparameterize the curve. We want to find a new parameter, say u, such that when we plug it into the original curve, we get the same points on the curve as when we use t. In other words, we want to find a function u(t) such that r(t) = r(u(t)).

To do this, we can use the inverse function theorem, which states that if a function f is invertible, then the inverse function f^-1 can be used to reparameterize the curve. In our case, we can use the inverse of the arc length function we found earlier, so u = e^t + C.

Plugging this into our original curve, we get:

r(u) = e^(e^t + C)*sint i + e^(e^t + C)*cost j

= e^u*sint i + e^u*cost j

This is the reparameterized curve with respect to the arc length measured from the point where t=0 in the direction of increasing t
 

1. What is reparameterization and why is it important in science?

Reparameterization is the process of transforming a statistical model into a different form without changing the underlying information. It allows for easier interpretation and understanding of the model, and can also improve the efficiency of statistical algorithms.

2. How do I choose the appropriate reparameterization method for my model?

The choice of reparameterization method depends on the specific model and its parameters. It is important to carefully consider the properties of each method and how they may impact the interpretation and performance of the model.

3. Can I reparameterize any statistical model?

In theory, yes, any statistical model can be reparameterized. However, some models may not benefit from reparameterization, or may require more complex techniques to do so. It is important to carefully assess the model and its objectives before deciding to reparameterize.

4. How does reparameterization impact the results of a statistical analysis?

Reparameterization does not change the underlying information of a model, so the results should be the same regardless of the chosen method. However, it can make the interpretation and computation of the results more straightforward and efficient.

5. Are there any potential drawbacks to reparameterization?

While reparameterization can be beneficial in many cases, it may also introduce additional complexity to the model and its interpretation. It is important to carefully consider the trade-offs and potential drawbacks before deciding to reparameterize a model.

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