Parametrization in R^3: Comparing Tangent Vectors of Different Curves

In summary, different parametrizations of the same curve in R^3 may result in different tangent vectors at different points on the curve, but at the same point, the tangent vectors will be identical. Using arclength as a parametrization ensures that the tangent vector always has a length of 1.
  • #1
ayalam
13
0
Do different parametrizations of the same curve in R^3 result in identical tangent vectors at a given point on the same curve? Example may be helpful.
 
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  • #2
Here are two different parameterizations of the twisted cubic (a space curve):

[tex]r_1(t)=\left< t,t^2,t^3\right> ,\mbox{ for } 0\leq t\leq 2 [/tex]
and [tex]r_2(u)=\left< e^u,e^{2u},e^{3u}\right> ,\mbox{ for } 0\leq u\leq \ln 2 [/tex]

their parameters are related by [itex]t=e^u[/itex].

Try it.
 
  • #3
I got for r(t) r'(t)=<1,2t,3t^2> and for r'(0)=<1,0,0>

But for r'(u)=<e^u,2e^2u,3e^3u> and r'(0)=<1,2,3>


So i would assume the answer would be no?
 
  • #4
Sorry, I made a typo above: it should read

[tex]r_1(t)=\left< t,t^2,t^3\right> ,\mbox{ for } 1\leq t\leq 2 [/tex]

Since t=0 and u=0 generate different points on the curve in R^3, which are, [itex]r_1(0)=\left< 0,0,0\right> [/itex], and [itex]r_2(0)=\left< 1,1,1\right> [/itex], they shouldn't have the same tangent vectors.

But when evaluated at the same point on the curve, they should be the same, that is

[tex]r_1^{\prime}(t)=\left< 1,2t,3t^2\right> ,\mbox{ for } 1\leq t\leq 2 [/tex]
and [tex]r_2^{\prime}(u)=\left< e^u,2e^{2u},3e^{3u}\right> ,\mbox{ for } 0\leq u\leq \ln 2 [/tex]

Notice that the points generated by the parameterizations are related such that:
[tex]r_1\left( e^u\right) =r_2(u) /mbox{ and } r_2\left( \ln(t) \right) =r_1(t) [/tex]

and hence the relations:[tex]r_1^{\prime}\left( e^u\right)e^u =r_2^{\prime}(u) /mbox{ and } r_2^{\prime}\left( \ln(t) \right) \frac{1}{t} =r_1^{\prime}(t) [/tex] hold for the ranges [itex]1\leq t\leq 2, 0\leq u\leq \ln 2[/itex]

So, I suppose the answer to your question is: no, they're not the same, but there does exist some relation between them (these two, that is). I think... no, I can't figure it out. Different parameterizations of a curve, still describe the same curve, right? Hmmm. Geometry: try parameterizing w.r.t. arc length along the curve, see calculus book: try Stewart.
 
  • #5
Sorry, I made a typo above: it should read

[tex]r_1(t)=\left< t,t^2,t^3\right> [/tex] for 1 [tex]\leq t\leq 2 [/tex]

Since t=0 and u=0 generate different points on the curve in R^3, which are, [itex]r_1(0)=\left< 0,0,0\right> [/itex], and [itex]r_2(0)=\left< 1,1,1\right> [/itex], they shouldn't have the same tangent vectors.

But when evaluated at the same point on the curve, they should be the same, that is

[tex]r_1^{\prime}(t)=\left< 1,2t,3t^2\right> ,\mbox{ for } 1\leq t\leq 2 [/tex]
and [tex]r_2^{\prime}(u)=\left< e^u,2e^{2u},3e^{3u}\right> ,\mbox{ for } 0\leq u\leq \ln 2 [/tex]

Notice that the points generated by the parameterizations are related such that:

[tex]r_1\left( e^u\right) =r_2(u) /mbox{ and } r_2\left( \ln(t) \right) =r_1(t) [/tex]

and hence the relations:

[tex]r_1^{\prime}\left( e^u\right)e^u =r_2^{\prime}(u) \mbox{ and } r_2^{\prime}\left( \ln(t) \right) \frac{1}{t} =r_1^{\prime}(t) [/tex] hold for the ranges [itex]1\leq t\leq 2, 0\leq u\leq \ln 2[/itex]

So, I suppose the answer to your question is: no, they're not the same, but there does exist some relation between them (these two, that is). I think... no, I can't figure it out. Different parameterizations of a curve, still describe the same curve, right? Hmmm. Geometry: try parameterizing w.r.t. arc length along the curve, see calculus book: try Stewart.
 
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  • #6
One way of thinking about this is "dynamically". Think of an object moving along the curve with t equal to the time it is at each point. The "tangent" vector would then be the velocity vector of the object. Different parametrizations can be interpreted as objects moving along the same curve at different speeds. The tangent vector, for different parametrizations, will, of course, always point in the same direction but its length would be the speed. The length of the tangent vector may change for different parametrizations. If you want to be completely "geometric" you should use arclength as parametrization. That way the tangent vector always has length 1.
 

1. What is parametrization and why is it important?

Parametrization is the process of representing a system or phenomenon using a set of parameters. It allows for a simpler and more concise representation of complex systems, making it easier to analyze and understand them. Parametrization is important because it helps scientists and researchers to make predictions, test hypotheses, and ultimately gain a deeper understanding of the world around us.

2. How do you choose the right parameters for a parametrization model?

The choice of parameters for a parametrization model depends on the specific system or phenomenon being studied. Generally, parameters should be chosen based on their relevance and ability to accurately represent the behavior of the system. It is also important to consider the trade-offs between simplicity and accuracy when selecting parameters.

3. Can parametrization be applied to any type of data or system?

Parametrization can be applied to a wide range of data and systems, as long as there is a clear understanding of the underlying principles and behavior of the system. However, some systems may be more challenging to parametrize due to their complexity or lack of data.

4. What are the advantages of using parametrization in scientific research?

There are several advantages to using parametrization in scientific research. It allows for a more concise representation of complex systems, making it easier to analyze and understand them. Parametrization also allows for the testing of multiple scenarios and predictions, which can lead to a deeper understanding of the system. Additionally, parametrization can help to identify key parameters that have a significant impact on the behavior of the system.

5. Are there any limitations or challenges to using parametrization?

Like any scientific method, parametrization has its limitations and challenges. One of the main challenges is selecting the appropriate parameters and accurately representing the system. This can be particularly difficult for complex systems with limited data. Additionally, parametrization may not always capture all aspects of a system, leading to potential inaccuracies in predictions. It is important for researchers to carefully consider the limitations and potential biases when using parametrization in their research.

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