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The derivative of \(f(x, y)\) at \(P_{0}(1,2)\) in the direction of \(\mathbf{i}+\mathbf{j}\) is \(2 \sqrt{2}\) and in the direction of \(-2 \mathbf{j}\) is \(-3 .\) What is the derivative of \(f\) in the direction of \(-\mathbf{i}-2 \mathbf{j} ?\) Give reasons for your answer.

Short Answer

Expert verified
The derivative in direction \(-\mathbf{i} - 2\mathbf{j}\) is \(-\frac{7}{\sqrt{5}}\).

Step by step solution

01

Understanding Directonal Derivatives

The directional derivative of a function \(f(x, y)\) at a point \(P_0(x_0, y_0)\) in the direction of a vector \(\mathbf{v} = \langle a, b \rangle\) is given by \(D_{\mathbf{v}} f(x_0, y_0) = abla f(x_0, y_0) \cdot \mathbf{u}\), where \(\mathbf{u}\) is the unit vector in the direction of \(\mathbf{v}\).
02

Calculate Gradient Using Given Directions

It's given that the directional derivative in the direction of \(\mathbf{i} + \mathbf{j}\) is \(2 \sqrt{2}\). That means \[abla f(1, 2) \cdot \frac{1}{\sqrt{2}}(\mathbf{i} + \mathbf{j}) = 2 \sqrt{2} \\Rightarrow abla f(1, 2) \cdot (1, 1) = 4.\]Similarly, for direction \(-2 \mathbf{j}\), it's given as \(-3\). So,\[abla f(1, 2) \cdot \frac{1}{2}(-2\mathbf{j}) = -3 \\Rightarrow abla f(1, 2) \cdot (0, -1) = -3.\]
03

Solve System of Equations

Use the equations from Step 2: 1. \(f_x + f_y = 4\) 2. \(-f_y = -3 \Rightarrow f_y = 3\)Solving gives \(f_y = 3\), thus substituting in the first equation:\[f_x + 3 = 4 \Rightarrow f_x = 1.\] Hence the gradient vector is \(abla f(1, 2) = \langle 1, 3 \rangle\).
04

Find Unit Vector for Given Direction

The direction vector is \(-\mathbf{i} - 2\mathbf{j}\), which is \((-1, -2)\). The magnitude of this vector is:\[\| ( -1, -2 ) \| = \sqrt{(-1)^2 + (-2)^2} = \sqrt{5}\]The unit vector in this direction is:\[\left( -\frac{1}{\sqrt{5}}, -\frac{2}{\sqrt{5}} \right)\]
05

Calculate The Desired Directional Derivative

Use the gradient \(abla f(1, 2) = \langle 1, 3 \rangle\) and the unit vector in the \(-\mathbf{i} - 2\mathbf{j}\) direction:\[D_{ -\mathbf{i} - 2\mathbf{j}} f = abla f(1, 2) \cdot \left( -\frac{1}{\sqrt{5}}, -\frac{2}{\sqrt{5}} \right) = 1 \times \left(-\frac{1}{\sqrt{5}}\right) + 3 \times \left(-\frac{2}{\sqrt{5}}\right)\]This simplifies to:\[= -\frac{1}{\sqrt{5}} - \frac{6}{\sqrt{5}} = -\frac{7}{\sqrt{5}}\]
06

Conclusion

Thus the derivative of \(f\) in the direction of \(-\mathbf{i} - 2\mathbf{j}\) is \(-\frac{7}{\sqrt{5}}\).

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Key Concepts

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

Gradient Vector
The concept of the gradient vector is vital in understanding directional derivatives. When dealing with functions of several variables, like a function of two variables, say, \( f(x, y) \), the gradient vector \( abla f(x, y) \) is composed of the partial derivatives with respect to each variable. The gradient vector tells us the direction of the steepest ascent from a given point in the domain.
The gradient vector is represented as \( \langle f_x, f_y \rangle \), where:
  • \( f_x \) is the partial derivative of \( f \) with respect to \( x \).
  • \( f_y \) is the partial derivative of \( f \) with respect to \( y \).
In simple terms, the gradient gives us a multi-dimensional perspective on how the function changes, directing us towards the path where the change in function's value is the greatest. Therefore, understanding the gradient is critical for calculating the directional derivative, as it reveals how steeply or gently the function slopes in the specified direction.
Unit Vector
A unit vector plays a crucial role when calculating directional derivatives as it provides the direction without affecting the magnitude. By definition, a unit vector has a magnitude of 1.
To find the unit vector of a given vector \( \mathbf{v} = \langle a, b \rangle \), you divide each component of the vector by its magnitude \( \| \mathbf{v} \| \). The formula goes as follows: \( \mathbf{u} = \frac{1}{\| \mathbf{v} \|} \langle a, b \rangle \).
For example, if the direction vector is \( \langle -1, -2 \rangle \), compute its magnitude first:
  • \( \| \langle -1, -2 \rangle \| = \sqrt{(-1)^2 + (-2)^2} = \sqrt{5} \)
Then, divide each component by this magnitude to find the unit vector:
  • \( \mathbf{u} = \left( -\frac{1}{\sqrt{5}}, -\frac{2}{\sqrt{5}} \right) \)
This ensures that when you're calculating a directional derivative, you're strictly considering the direction, thus giving an accurate rate of change in that direction.
Partial Derivatives
Partial derivatives represent how a function of multiple variables changes as one variable changes, while the other variables remain constant.
Imagine you have a surface in 3D space, governed by \( z = f(x, y) \). The partial derivative \( f_x \) indicates the slope of the tangent line to the surface along the \( x \)-direction, holding \( y \) constant. Meanwhile, \( f_y \) describes the slope along the \( y \)-direction, with \( x \) constant.
In the context of problems involving directional derivatives, partial derivatives are critical because they give the components of the gradient vector. This knowledge reveals how sharply \( f \) changes as you move along either axis. Combined, these derivatives pave the way to express changes in any direction by leveraging the gradient and appropriately scaling it using a unit vector. Thus, understanding partial derivatives is essential to decipher the exact behavior of multivariable functions.

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Most popular questions from this chapter

Gives a function \(f(x, y, z)\) and a positive number \(\epsilon .\) In each exercise, show that there exists a \(\delta>0\) such that for all \((x, y, z)\) $$\sqrt{x^{2}+y^{2}+z^{2}}<\delta \Rightarrow|f(x, y, z)-f(0,0,0)|<\epsilon$$ Show that \(f(x, y, z)=x^{2}+y^{2}+z^{2}\) is continuous at the origin.

Find the minimum distance from the point (2,-1,1) to the plane \(x+y-z=2\).

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. $$f(x, y)=\frac{x^{3}-x y^{2}}{x^{2}+y^{2}}$$

Human blood types are classified by three gene forms \(A, B,\) and \(O .\) Blood types \(A A, B B,\) and \(O O\) are homozygous, and blood types \(A B, A O,\) and \(B O\) are heterozygous. If \(p, q,\) and \(r\) represent the proportions of the three gene forms to the population, respectively, then the Hardy-Weinberg Law asserts that the proportion \(Q\) of heterozygous persons in any specific population is modeled by $$Q(p, q, r)=2(p q+p r+q r)$$ subject to \(p+q+r=1 .\) Find the maximum value of \(Q\)

Find the linearization \(L(x, y, z)\) of the function \(f(x, y, z)\) at \(P_{0} .\) Then find an upper bound for the magnitude of the error \(E\) in the approximation \(f(x, y, z) \approx L(x, y, z)\) over the region \(R\) \(f(x, y, z)=\sqrt{2} \cos x \sin (y+z)\) at \(P_{0}(0,0, \pi / 4)\) R: \(|x| \leq 0.01, \quad|y| \leq 0.01, \quad|z-\pi / 4| \leq 0.01\)

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