/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 26 Find \(f_{x}, f_{y},\) and \(f_{... [FREE SOLUTION] | 91Ó°ÊÓ

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Find \(f_{x}, f_{y},\) and \(f_{z}\). $$f(x, y, z)=\left(x^{2}+y^{2}+z^{2}\right)^{-1 / 2}$$

Short Answer

Expert verified
\( f_x = -x(x^2 + y^2 + z^2)^{-3/2}, f_y = -y(x^2 + y^2 + z^2)^{-3/2}, f_z = -z(x^2 + y^2 + z^2)^{-3/2} \).

Step by step solution

01

Understanding the Problem

We are given a function \( f(x, y, z) = \left(x^2 + y^2 + z^2\right)^{-1/2} \) and need to find the partial derivatives \( f_x \), \( f_y \), and \( f_z \). These represent how the function changes with respect to each variable while keeping the others constant.
02

Calculating \( f_x \)

To find the partial derivative of \( f \) with respect to \( x \), use the chain rule. Let \( g(x, y, z) = x^2 + y^2 + z^2 \). The derivative with respect to \( x \) is \( g_x = 2x \). By the chain rule, \( f_x = -\frac{1}{2}(g(x, y, z))^{-3/2} \cdot g_x = -\frac{1}{2}(x^2 + y^2 + z^2)^{-3/2} \cdot 2x = -x(x^2 + y^2 + z^2)^{-3/2} \).
03

Calculating \( f_y \)

Similarly, to find \( f_y \), we again differentiate \( f \) using the chain rule. We have \( g_y = 2y \). Hence, \( f_y = -\frac{1}{2}(g(x, y, z))^{-3/2} \cdot g_y = -\frac{1}{2}(x^2 + y^2 + z^2)^{-3/2} \cdot 2y = -y(x^2 + y^2 + z^2)^{-3/2} \).
04

Calculating \( f_z \)

Finally, for \( f_z \), differentiate \( f \) with respect to \( z \). We have \( g_z = 2z \). Therefore, \( f_z = -\frac{1}{2}(g(x, y, z))^{-3/2} \cdot g_z = -\frac{1}{2}(x^2 + y^2 + z^2)^{-3/2} \cdot 2z = -z(x^2 + y^2 + z^2)^{-3/2} \).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Multivariable Calculus
Multivariable Calculus is an extension of calculus to functions that depend on multiple variables. In the context of the given problem, our function, \( f(x, y, z) = (x^2 + y^2 + z^2)^{-1/2} \), is a multivariable function because it depends on three variables: \(x\), \(y\), and \(z\).
When dealing with multivariable functions, we often want to understand how changes in each variable individually affect the function. This is where partial derivatives come into play.
Partial derivatives allow us to measure the sensitivity of a multivariable function in relation to changes in one of the variables, while keeping the other variables constant. This concept is essential for many fields such as physics, engineering, and economics, where real-world problems often involve multiple changing factors.
Chain Rule
The Chain Rule is a fundamental tool in calculus used to find the derivative of composite functions.
In our exercise, the function \( f(x, y, z) = (x^2 + y^2 + z^2)^{-1/2} \) is "composite" because it involves a composition of the outer function \( u^{-1/2} \) and the inner function \( u = x^2 + y^2 + z^2 \).
The Chain Rule helps us take derivatives in a step-by-step manner when the function depends on another function. To find \( f_x \), \( f_y \), and \( f_z \), we first differentiate the inner function and multiply it by the derivative of the outer function, adjusted for the inner function.
For example, finding \( f_x \) involves:
  • Computing \( g_x = 2x \)
  • Using \( u^{-1/2} \)'s derivative \(-\frac{1}{2}u^{-3/2} \)
Combining these gives \( f_x = -x(x^2 + y^2 + z^2)^{-3/2} \). This process ensures we capture how each variable influences the entire function.
Gradient
The gradient is a vector that contains all the partial derivatives of a function, providing information about the function's rate of change and direction of steepest ascent at any point.
For the function \( f(x, y, z) = (x^2 + y^2 + z^2)^{-1/2} \), the gradient is given by the vector \( abla f = (f_x, f_y, f_z) \).
  • \( f_x = -x(x^2 + y^2 + z^2)^{-3/2} \)
  • \( f_y = -y(x^2 + y^2 + z^2)^{-3/2} \)
  • \( f_z = -z(x^2 + y^2 + z^2)^{-3/2} \)
The gradient not only indicates how the function changes with small variations in the variables but also points in the direction of greatest increase of the function. This is extremely useful in optimization problems, where finding maximum or minimum values of functions is required. Understanding the gradient is crucial in fields like data science and machine learning, where optimization is a key application.

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Most popular questions from this chapter

If you cannot make any headway with \(\lim _{(x, y) \rightarrow(0,0)} f(x, y)\) in rectangular coordinates, try changing to polar coordinates. Substitute \(x=r \cos \theta, y=r \sin \theta,\) and investigate the limit of the resulting expression as \(r \rightarrow 0 .\) In other words, try to decide whether there exists a number \(L\) satisfying the following criterion: $$|r|<\delta \Rightarrow|f(r, \theta)-L|<\epsilon$$ If such an \(L\) exists, then $$\lim _{(x, y) \rightarrow(0,0)} f(x, y)=\lim _{r \rightarrow 0} f(r \cos \theta, r \sin \theta)=L$$ For instance, $$\lim _{(x, y) \rightarrow(0,0)} \frac{x^{3}}{x^{2}+y^{2}}=\lim _{r \rightarrow 0} \frac{r^{3} \cos ^{3} \theta}{r^{2}}=\lim _{r \rightarrow 0} r \cos ^{3} \theta=0$$ To verify the last of these equalities, we need to show that Equation (1) is satisfied with \(f(r, \theta)=r \cos ^{3} \theta\) and \(L=0 .\) That is, we need to show that given any \(\epsilon>0,\) there exists a \(\delta>0\) such that for all \(r\) and \(\theta\) $$|r|<\delta \Rightarrow\left|r \cos ^{3} \theta-0\right|<\epsilon$$ since $$\left|r \cos ^{3} \theta\right|=|r|\left|\cos ^{3} \theta\right| \leq|r| \cdot 1=|r|$$ the implication holds for all \(r\) and \(\theta\) if we take \(\delta=\epsilon\) In contrast, $$\frac{x^{2}}{x^{2}+y^{2}}=\frac{r^{2} \cos ^{2} \theta}{r^{2}}=\cos ^{2} \theta$$takes on all values from 0 to 1 regardless of how small \(|r|\) is, so that \(\lim _{(x, y) \rightarrow(0,0)} x^{2} /\left(x^{2}+y^{2}\right)\) does not exist. In each of these instances, the existence or nonexistence of the limit as \(r \rightarrow 0\) is fairly clear. Shifting to polar coordinates does not always help, however, and may even tempt us to false conclusions. For example, the limit may exist along every straight line (or ray) \(\theta=\) constant and yet fail to exist in the broader sense. Example 5 illustrates this point. In polar coordinates, \(f(x, y)=\left(2 x^{2} y\right) /\left(x^{4}+y^{2}\right)\) becomes $$f(r \cos \theta, r \sin \theta)=\frac{r \cos \theta \sin 2 \theta}{r^{2} \cos ^{4} \theta+\sin ^{2} \theta}$$for \(r \neq 0 .\) If we hold \(\theta\) constant and let \(r \rightarrow 0,\) the limit is \(0 .\) On the path \(y=x^{2},\) however, we have \(r \sin \theta=r^{2} \cos ^{2} \theta\) and $$\begin{aligned} f(r \cos \theta, r \sin \theta) &=\frac{r \cos \theta \sin 2 \theta}{r^{2} \cos ^{4} \theta+\left(r \cos ^{2} \theta\right)^{2}} \\ &=\frac{2 r \cos ^{2} \theta \sin \theta}{2 r^{2} \cos ^{4} \theta}=\frac{r \sin \theta}{r^{2} \cos ^{2} \theta}=1 \end{aligned}$$ Find the limit of \(f\) as \((x, y) \rightarrow(0,0)\) or show that the limit does not exist. $$f(x, y)=\tan ^{-1}\left(\frac{|x|+|y|}{x^{2}+y^{2}}\right)$$

The Sandwich Theorem for functions of two variables states that if \(g(x, y) \leq f(x, y) \leq h(x, y)\) for all \((x, y) \neq\left(x_{0}, y_{0}\right)\) in a disk centered at \(\left(x_{0}, y_{0}\right)\) and if \(g\) and \(h\) have the same finite limit \(L\) as \((x, y) \rightarrow\left(x_{0}, y_{0}\right)\) then$$\lim _{(x, y) \rightarrow\left(x_{0}, y_{0}\right)} f(x, y)=L$$ Use this result to support your answers to the questions. Does knowing that \(|\cos (1 / y)| \leq 1\) tell you anything about $$\lim _{(x, y) \rightarrow(0,0)} x \cos \frac{1}{y} ?$$ Give reasons for your answer.

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