Linearizing f(x,y,z) at (1,1,1) for Quick Calculations

In summary, the conversation is about linearizing a function f(x,y,z) = tan^{-1}(xyz) at the point (1,1,1) and whether the general solutions should be included in the linearization process. The formula for linearization is discussed and the question is raised if the general solutions should be included in the linearization.
  • #1
Mindscrape
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I am linearizing the function [tex]f(x,y,z) = tan^{-1}(xyz)[/tex] at the point (1,1,1).

Since [tex]f(x_0,y_0,z_0)= \frac{\pi}{4} + \pi*n[/tex] should I just take the first value or do I have to carry all the solutions through the linearization process?

Um, anybody remember this? I can put up some work if it helps.

[tex]L(x,y,z) = \Delta f = f_x \Delta x + f_y \Delta y + f_z \Delta z[/tex]

So

[tex]L(x,y,z) = f(x_0,y_0,z_0) + f_x(x-x_0) + f_y (y-y_0) + f_z (z-z_0)[/tex]

where [tex]f_x = \frac{\partial f}{\partial x}[/tex] (and f_y and f_z)

So does [tex] L(x,y,z) = \frac{\pi}{4} +\pi n + \frac{1}{2}[(x-1)+(y-1)+(z-1)] [/tex]
?
 
Last edited:
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  • #2
Surely somebody must know. I have doubts that I should include the general solutions, because that would be a strange linearization. I'm not really sure though, since the general solutions would be just as valid, unless I am overlooking something.
 

1. What is "Quick Linearization"?

"Quick Linearization" is a mathematical technique used to approximate a nonlinear function by replacing it with a linear one. This is often done to simplify calculations and make them more manageable.

2. How does "Quick Linearization" work?

First, a nonlinear function is chosen. Then, a point on the curve is selected. The slope of the tangent line at that point is calculated, and this slope is used to create a linear function that approximates the original curve near that point.

3. When is "Quick Linearization" useful?

"Quick Linearization" is useful when dealing with complex nonlinear functions that are difficult to work with. It allows for easier calculations and can provide a good approximation of the original function near a chosen point.

4. What are the limitations of "Quick Linearization"?

One limitation is that the approximation is only accurate near the chosen point. As the distance from the chosen point increases, the accuracy decreases. Additionally, "Quick Linearization" may not work well for highly nonlinear functions with steep curves.

5. How is "Quick Linearization" different from linear regression?

"Quick Linearization" and linear regression are both methods used to approximate nonlinear functions with linear ones. However, linear regression uses statistical techniques to find the best-fit line for a set of data points, while "Quick Linearization" is a mathematical technique that focuses on a specific point on the curve.

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