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Suppose \(f(x, y)=4 x^{2}+3 y^{2}\) a. Calculate the partial derivatives of \(f\) b. Suppose \(f(x, y)=16 .\) Use the implicit function theorem to calculate \(d y / d x\) c. What is the value of \(d y / d x\) if \(x=1, y=2 ?\) d. Graph your results and use it to interpret the results in parts (b) and (c) of this problem.

Short Answer

Expert verified
#Answer# (a) The partial derivatives of f with respect to x and y are \(\frac{\partial f}{\partial x} = 8x\) and \(\frac{\partial f}{\partial y} = 6y\). (b) The derivative of y with respect to x when \(f(x, y) = 16\) is \(\frac{dy}{dx} = -\frac{4x}{3y}\). (c) The value of \(\frac{dy}{dx}\) when \(x = 1\) and \(y = 2\) is \(-\frac{2}{3}\). (d) The graph displays an ellipse described by \(4x^2 + 3y^2 = 16\) and a tangent line at point (1, 2) with a slope of -2/3. It shows that as x increases, y decreases, and the tangent line represents the rate at which the function f(x, y) = 16 changes in the x-direction at point (1, 2).

Step by step solution

01

(a) Compute the partial derivatives of f with respect to x and y

To calculate the partial derivatives, we'll find the derivative of the function concerning x and y, holding other variables constant: \(\frac{\partial f}{\partial x} = \frac{\partial}{\partial x} (4x^2 + 3y^2) = 8x\) \(\frac{\partial f}{\partial y} = \frac{\partial}{\partial y} (4x^2 + 3y^2) = 6y\)
02

(b) Use the implicit function theorem to compute dy/dx

We are given that \(f(x, y) = 16\), so \(4x^2 + 3y^2 = 16\). We can use the implicit function theorem to calculate \(\frac{dy}{dx}\) by finding the ratio between partial derivatives: \(\frac{dy}{dx} = -\frac{\frac{\partial f}{\partial x}}{\frac{\partial f}{\partial y}} = -\frac{8x}{6y} = -\frac{4x}{3y}\)
03

(c) Calculate the value of dy/dx when x=1 and y=2

To find the requested value, plug in the values \(x = 1\) and \(y = 2\) into our equation for \(\frac{dy}{dx}\): \(\frac{dy}{dx} = -\frac{4(1)}{3(2)} = -\frac{4}{6} = -\frac{2}{3}\)
04

(d) Graph your results and use it to interpret the results in parts (b) and (c)

To graph the ellipse described by \(4x^2 + 3y^2 = 16\) and the tangent line at the point \((1, 2)\), we can draw the ellipse and the tangent line on a coordinate plane, then make the following observations: 1. The tangent line at point \((1, 2)\) has a slope of \(-\frac{2}{3}\), which was calculated in part (c). 2. The tangent line represents the rate at which the function \(f(x, y) = 16\) is changing in the x-direction at the point \((1, 2)\). 3. The negative slope indicates that as x increases, y decreases. The graph would show the ellipse originating from the function \(f(x, y) = 16\) and the tangent line at point \((1, 2)\) with a slope of \(-\frac{2}{3}\). This visual representation can help to interpret the derivative calculated in part (c) and further understand the relationship between x and y.

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Key Concepts

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

Partial Derivatives
Partial derivatives play a crucial role in understanding how a function behaves with respect to each of its variables. Let's take the function f(x, y) = 4x^2 + 3y^2 from the exercise. The partial derivative with respect to x is found by treating y as a constant and differentiating f with respect to x. Conversely, the partial derivative with respect to y is calculated by treating x as constant and differentiating with respect to y.

In our case, the partial derivatives are \(\frac{\partial f}{\partial x} = 8x\) and \(\frac{\partial f}{\partial y} = 6y\). Understanding these derivatives is essential as they indicate the rate of change of the function along the axes of the respective variables. For instance, the derivative \(\frac{\partial f}{\partial x}\) tells us how f changes in the x-direction for a small change in x, holding y constant.
dy/dx Calculation
When dealing with functions involving two variables, such as f(x, y), we often need to understand how these variables are interconnected. The implicit function theorem is a powerful tool that comes in handy, particularly in situations where y is defined implicitly by an equation in terms of x. It provides us with a method to find \(\frac{dy}{dx}\) without the need to solve y explicitly.

The theorem states that under certain conditions, if we have an equation f(x, y) = c, where c is a constant, we can view y as an implicit function of x and determine \(\frac{dy}{dx}\) as -\frac{\frac{\partial f}{\partial x}}{\frac{\partial f}{\partial y}}. For the given exercise, \(\frac{dy}{dx} = -\frac{8x}{6y}\) simplifies to -\frac{4x}{3y}. This gives us a formula to compute the rate at which y changes with respect to x at any point on the curve f(x, y) = 16.
Graphical Interpretation
Visualizing mathematical concepts can enhance understanding. The function f(x, y) = 4x^2 + 3y^2 forms an ellipse when set equal to a constant, such as 16. Graphing this equation provides a visual representation of the problem.

By plotting the level curve 4x^2 + 3y^2 = 16, we see the ellipse on the coordinate plane. The point (1, 2) is on this ellipse, and the calculated \(\frac{dy}{dx} = -\frac{2}{3}\) at this point signifies the slope of the tangent to the ellipse at that point. Graphically, the negative slope indicates that as we move to the right along the x-axis (x increasing), we move down along the y-axis (y decreasing). This reinforces the derivative's conceptual meaning as the rate of change of y relative to x, and visually shows the direction and steepness of that change at the point of tangency.

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Most popular questions from this chapter

The height of a ball that is thrown straight up with a certain force is a function of the time ( \(t\) ) from which it is released given by \(f(t)=-0.5 g t^{2}+40 t\) (where \(g\) is a constant determined by gravity). a. How does the value of \(t\) at which the height of the ball is at a maximum depend on the parameter \(g\) ? b. Use your answer to part (a) to describe how maximum height changes as the parameter \(g\) changes. c. Use the envelope theorem to answer part (b) directly. d. On the Earth \(g=32,\) but this value varies somewhat around the globe. If two locations had gravitational constants that differed by \(0.1,\) what would be the difference in the maximum height of a ball tossed in the two places?

Consider the following constrained maximization problem: \\[ \begin{array}{ll} \text { maximize } & y=x_{1}+5 \ln x_{2} \\ \text { subject to } & k-x_{1}-x_{2}=0 \end{array} \\] where \(k\) is a constant that can be assigned any specific value. a show that if \(k=10,\) this problem can be solved as one involving only equality constraints. b. Show that solving this problem for \(k=4\) requires that \\[ x_{1}=-1 \\] c. If the \(x\) 's in this problem must be non-negative, what is the optimal solution when \(k=4 ?\) (This problem may be solved cither intuitively or using the methods outlined in the chapter.) d. What is the solution for this problem when \(k=20 ?\) What do you conclude by comparing this solution with the solution for part (a)?

Here are a few useful relationships related to the covariance of two random variables, \(x_{1}\) and \(x_{2}\) a. \(S h o w \quad t h\) at \(\quad \operatorname{Cov}\left(x_{1}, x_{2}\right)=E\left(x_{1} x_{2}\right)-E\left(x_{1}\right) E\left(x_{2}\right)\) An important implication of this is that if \(\operatorname{Cov}\left(x_{1}, x_{2}\right)=0, E\left(x_{1} x_{2}\right)=E\left(x_{1}\right) E\left(x_{2}\right) .\) That is, the expected value of a product of two random variables is the product of these variables' expected values. b. Show that \(\operatorname{Var}\left(a x_{1}+b x_{2}\right)=a^{2} \operatorname{Var}\left(x_{1}\right)+b^{2} \operatorname{Var}\left(x_{2}\right)+\) \\[ 2 a b \operatorname{Cov}\left(x_{1}, x_{2}\right) \\] c. In Problem \(2.15 \mathrm{d}\) we looked at the variance of \(X=k x_{1}+(1-k) x_{2} 0 \leq k \leq 1 .\) Is the conclusion that this variance is minimized for \(k=0.5\) changed by considering cases where \(\operatorname{Cov}\left(x_{1}, x_{2}\right) \neq 0 ?\) d. The corrdation cocfficicnt between two random variables is defined as \\[ \operatorname{Corr}\left(x_{1}, x_{2}\right)=\frac{\operatorname{Cov}\left(x_{1}, x_{2}\right)}{\sqrt{\operatorname{Var}\left(x_{1}\right) \operatorname{Var}\left(x_{2}\right)}} \\] Explain why \(-1 \leq \operatorname{Corr}\left(x_{1}, x_{2}\right) \leq 1\) and provide some intuition for this result. e. Suppose that the random variable \(y\) is related to the random variable \(x\) by the linear equation \(y=\alpha+\beta x\). Show that \\[ \beta=\frac{\operatorname{Cov}(y, x)}{\operatorname{Var}(x)} \\] Here \(\beta\) is sometimes called the (theoretical) regression coefficient of \(y\) on \(x\). With actual data, the sample analog of this expression is the ordinary least squares (OLS) regression coefficient.

Taylor's theorem shows that any function can be approximated in the vicinity of any convenient point by a series of terms involving the function and its derivatives, Here we look at some applications of the theorem for functions of one and two variables. a Any continuous and differentiable function of a single variable, \(f(x),\) can be approximated near the point \(a\) by the formula \\[ \begin{aligned} f(x)=& f(a)+f(a)(x-a)+0.5 f^{\prime \prime}(a)(x-a)^{2}+\\\ &+\text { terms in } f^{\prime \prime \prime}, f^{\prime \prime \prime}, \ldots . \end{aligned} \\] Using only the first three of these terms results in a quadratic Taylor approximation. Use this approximation together with the definition of concavity to show that any concave function must lie on or below the tangent to the function at point \(a\) b. The quadratic Taylor approximation for any function of two variables, \(f(x, y),\) near the point \((a, b)\) is given by \\[ \begin{aligned} f(x, y)=& f(a, b)+f_{1}(a, b)(x-a)+f_{2}(a, b)(y-b) \\ &+0.5\left[f_{11}(a, b)(x-a)^{2}\right.\\\ &\left.+2 f_{12}(a, b)(x-a)(y-b)+f_{22}(y-b)^{2}\right] \end{aligned} \\] Use this approximation to show that any concave function (as defined by Equation 2.84 ) must lie on or below its tangent plane at \((a, b)\)

Suppose that \(f(x, y)=x y .\) Find the maximum value for \(f\) if \(x\) and \(y\) are constrained to sum to \(1 .\) Solve this problem in two ways: by substitution and by using the Lagrange multiplier method.

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