Chapter 10: Problem 9
In Problems 9-14, compute the directional derivative of \(f(x, y)\) at the given point in the indicated direction. $$ f(x, y)=\sqrt{x^{2}+2 y^{2}} \text { at }(-1,2) \text { in the direction }\left[\begin{array}{l} 1 \\ 1 \end{array}\right] $$
Short Answer
Expert verified
The directional derivative at the point (-1, 2) in the specified direction is \(\frac{\sqrt{2}}{2}\).
Step by step solution
01
Determine the Gradient of f
To find the directional derivative, we first need the gradient of the function \( f(x, y) = \sqrt{x^2 + 2y^2} \). The gradient \( abla f \) consists of the partial derivatives \( \frac{\partial f}{\partial x} \) and \( \frac{\partial f}{\partial y} \). Compute them separately:\[ \frac{\partial f}{\partial x} = \frac{x}{\sqrt{x^2 + 2y^2}} \]\[ \frac{\partial f}{\partial y} = \frac{2y}{\sqrt{x^2 + 2y^2}} \]
02
Evaluate Gradient at Given Point
Next, we evaluate the gradient at the point \((-1, 2)\). Substitute \(x = -1\) and \(y = 2\) into the partial derivatives computed:\[ \frac{\partial f}{\partial x} \bigg|_{(-1, 2)} = \frac{-1}{\sqrt{1 + 8}} = \frac{-1}{3} \]\[ \frac{\partial f}{\partial y} \bigg|_{(-1, 2)} = \frac{4}{\sqrt{1 + 8}} = \frac{4}{3} \]
03
Normalize the Direction Vector
The given direction vector is \([1, 1]\). We must normalize it to find the unit direction vector. Compute the magnitude of the vector:\[ \text{Magnitude} = \sqrt{1^2 + 1^2} = \sqrt{2} \]The unit vector \(\mathbf{u}\) is:\[ \mathbf{u} = \left[\frac{1}{\sqrt{2}}, \frac{1}{\sqrt{2}}\right] \]
04
Compute the Directional Derivative
The directional derivative \(D_\mathbf{u} f\) is calculated by taking the dot product of the gradient \(abla f(-1, 2)\) and the unit vector \(\mathbf{u}\):\[ D_\mathbf{u} f = abla f(-1, 2) \cdot \mathbf{u} = \left[\frac{-1}{3}, \frac{4}{3}\right] \cdot \left[\frac{1}{\sqrt{2}}, \frac{1}{\sqrt{2}}\right] \]Calculating the dot product:\[ D_\mathbf{u} f = \frac{-1}{3\sqrt{2}} + \frac{4}{3\sqrt{2}} = \frac{3}{3\sqrt{2}} = \frac{1}{\sqrt{2}} \]
05
Simplify the Result
The expression \(\frac{1}{\sqrt{2}}\) can be rationalized by multiplying both the numerator and denominator by \(\sqrt{2}\) to get:\[ D_\mathbf{u} f = \frac{\sqrt{2}}{2} \]
Unlock Step-by-Step Solutions & Ace Your Exams!
-
Full Textbook Solutions
Get detailed explanations and key concepts
-
Unlimited Al creation
Al flashcards, explanations, exams and more...
-
Ads-free access
To over 500 millions flashcards
-
Money-back guarantee
We refund you if you fail your exam.
Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!
Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Gradient
When exploring the concept of a directional derivative, the **gradient** is the key player. It can be thought of as a vector that points in the direction of the greatest rate of increase of a function. Think of it as a compass giving you directions toward the steepest climb up a hill.
Mathematically, the gradient of a two-variable function, like our given function \( f(x, y) = \sqrt{x^2 + 2y^2} \), is composed of its partial derivatives. So, the gradient \( abla f \) is a vector given by:
Mathematically, the gradient of a two-variable function, like our given function \( f(x, y) = \sqrt{x^2 + 2y^2} \), is composed of its partial derivatives. So, the gradient \( abla f \) is a vector given by:
- \( \frac{\partial f}{\partial x} \) measures how \( f \) changes as you move along the \( x \)-axis
- \( \frac{\partial f}{\partial y} \) measures how \( f \) changes as you move along the \( y \)-axis
Partial Derivatives
In calculus, **partial derivatives** are used to show how a function changes as one of its variables change, while the others are held constant. They are important building blocks for understanding the behavior of a multivariable function.
For the function \( f(x, y) = \sqrt{x^2 + 2y^2} \), its partial derivatives are:
For the function \( f(x, y) = \sqrt{x^2 + 2y^2} \), its partial derivatives are:
- The partial derivative with respect to \( x \) is \( \frac{\partial f}{\partial x} = \frac{x}{\sqrt{x^2 + 2y^2}} \). This tells us how much \( f \) changes with a slight change in \( x \) when \( y \) is kept constant.
- The partial derivative with respect to \( y \) is \( \frac{\partial f}{\partial y} = \frac{2y}{\sqrt{x^2 + 2y^2}} \). This measures the change in \( f \) for a slight change in \( y \) when \( x \) is constant.
Unit Vector
A **unit vector** is a vector that has a magnitude of one. It is often used to describe directions as it keeps the original vector's direction but neutralizes its scale.
To find a unit vector from any given vector, we must divide by its magnitude. For example, if we have a direction vector \([1, 1]\), we calculate its magnitude as \( \sqrt{1^2 + 1^2} = \sqrt{2} \).
Then, the corresponding unit vector is:
To find a unit vector from any given vector, we must divide by its magnitude. For example, if we have a direction vector \([1, 1]\), we calculate its magnitude as \( \sqrt{1^2 + 1^2} = \sqrt{2} \).
Then, the corresponding unit vector is:
- \( \mathbf{u} = \left[\frac{1}{\sqrt{2}}, \frac{1}{\sqrt{2}}\right] \)