Linearization with Polynomials

In summary: Thanks,MikeIn summary, a cubic spline or polynomial can be used to interpolate data that is not linear. Piecewise linear interpolation is used when two points are connected with a linear function. Polygonal approximation and polynomial approximation are two methods for linearization when a model is not linear in the coefficients. Linear regression is a method for linearizing data when the coefficients are linear.
  • #1
MikeSv
35
0
Hi everyone.

I started to look at different linearization techniques like:

-linear interpolation
- spline interpolation
- curve fitting...

Now Iam wondering (and I guess its very stupid) : As polynomials with a degree > 1 are not linear, why can I use them for linearization?

With the method of piecewise linear interpolation its clear. You connect two datapoints with a linear function.

But what about quadratic and cubic splines which are not linear?

Thanks in advance for any help,

Kind regards,

Mike
 
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  • #2
Hi,

Can you give an example where someone claims an interpolation by means of a cubic spline or a polynomial is a linear interpolation ?
 
  • #3
Hi and thanks for your reply.

Iam trying to learn more about how to linearize outputs of a nonlinear sensor and I found a list in a Master thesis on the web which desribes different techniques for linearization.
The list says "Piecewise polynomial or spline interpolation"...

Or is linearization of nonlinear sensors is done with linear polynomials only?

Regards,

Mike
 
  • #4
Hi again.

I found a section in a scientific paper which mention techniques like:
- look up table
- polygonal approximation
- polynomial approximation
- cubic spline interpolation

So I guess that answers one part of my question. Polynomials of higher order can be used?

But then another question came up... Whats the difference between plynomial approximation and spline interpolation?

Thanks again,

Mike
 
  • #5
MikeSv said:
I found a list in a Master thesis on the web which desribes different techniques for linearization.
MikeSv said:
I found a section in a scientific paper
I found somewhere ... tell me what is meant, is not an acceptable quotation. So the demand
BvU said:
Can you give an example where someone claims an interpolation by means of a cubic spline or a polynomial is a linear interpolation ?
is still valid.

E.g. polynomials could be the elements (points) of the phase space and not the object of the linearization process. In this case, we might talk about a linearization between polynomials of any degree by linear polynomials of polynomials. A bit constructed, I admit, but it shows you the difficulties we face, if you say things like "I have read / found / heard ... somewhere / on the web / in an article".
 
  • #6
Hi and thanks for your reply!

Please take into consideration that I have just started with the topic and Iam just confused what is meant by "linearization" of data... Maybe its the same as interpolation in that context?

Regards,

Mike
 
  • #7
MikeSv said:
Maybe its the same as interpolation in that context?

What context? You haven't revealed the title of the scientific paper or quoted an abstract from it.

In mathematics, some nouns like "linearization" can be used with a variety of meanings. In spite of the fact that mathematics frowns on ambiguous terminology, mathematics is cultural phenomenon subject to the habits of human beings. So it frowns on ambiguous terminology and also uses it.
 
  • #8
Hi again.

As I mentioned earlier I would like to take raw data of a non linear sensor (i.e. a thermocouple) and learn how to linearize these.
But Iam not sure which techniques are used for that.

Regards,

Mike
 
  • #10
Difference between spline and polynomial approximation is that that to some order the splines are only piecewise continuous in derivatives usually some order is discontinuous at the fitting points while a polynomial has continuous derivatives of all orders generally.
 
  • #11
In some Statistical treatments a model or expression are considered linear if they are linear in the coefficients. Maybe this is the case with your book
 

What is linearization with polynomials?

Linearization with polynomials is a mathematical process that involves approximating a nonlinear function with a linear function. This is done by using a polynomial, which is a mathematical expression consisting of one or more terms that involve variables raised to non-negative integer powers.

What is the purpose of linearization with polynomials?

The purpose of linearization with polynomials is to simplify a nonlinear function into a linear form, which is easier to work with mathematically. This allows for better understanding and analysis of the function, and also makes it easier to solve equations involving the function.

How is linearization with polynomials done?

Linearization with polynomials is done by finding the tangent line of the function at a specific point. This tangent line is then used as an approximation of the original function. This process can be repeated at different points to get a better approximation of the function.

What are the benefits of linearization with polynomials?

Linearization with polynomials has several benefits, including simplifying complex functions, making it easier to analyze and solve equations involving the function, and providing a visual representation of the function through the tangent line. It is also a commonly used technique in physics and engineering.

What are the limitations of linearization with polynomials?

One limitation of linearization with polynomials is that it only provides an approximation of the original function, and the accuracy of the approximation depends on the choice of the point at which the tangent line is drawn. Additionally, it is not always possible to find a suitable polynomial to linearize a given function.

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