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Consider the function \(F(x, y, z)=e^{x y z}\) a. Write \(F\) as a composite function \(f \circ g,\) where \(f\) is a function of one variable and \(g\) is a function of three variables. b. Relate \(\nabla F\) to \(\nabla g\)

Short Answer

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Question: Write the given function F(x, y, z) = e^(xyz) as a composite function f ∘ g, and relate the gradient of F to the gradient of g. Answer: The composite functions are f(u) = e^u and g(x, y, z) = xyz. The gradient of F can be related to the gradient of g by ∇F = F(x,y,z) ∙ ∇g.

Step by step solution

01

a. Writing F as a composite function

We are given \(F(x, y, z)=e^{xyz}\). To write it as a composite function, we can let \(g(x, y, z) = xyz\). Now we need to find a function \(f(u)\) such that \(F(x,y,z) = f \circ g = f(g(x,y,z))\). In our case, we want \(f(u) = e^u\). So, $$ F(x, y, z) = f \circ g = f(g(x, y, z)) = f(xyz) = e^{xyz} $$ Thus, the composite functions are \(f(u) = e^u\) and \(g(x, y, z) = xyz\).
02

b. Relating the gradient of F to the gradient of g

To relate the gradient of F to the gradient of g, we first find the gradient of F and the gradient of g. The gradient of F is given by: $$ ∇F = \begin{bmatrix} \frac{\partial F}{\partial x}\\ \frac{\partial F}{\partial y}\\ \frac{\partial F}{\partial z} \end{bmatrix} $$ Since \(F(x, y, z) = e^{xyz}\), we compute the partial derivatives: $$ \frac{\partial F}{\partial x} = ye^{xyz}, \frac{\partial F}{\partial y} = xe^{xyz}, \frac{\partial F}{\partial z} = xye^{xyz} $$ Thus, the gradient of F is: $$ ∇F = \begin{bmatrix} ye^{xyz}\\ xe^{xyz}\\ xye^{xyz} \end{bmatrix} $$ Now, let's find the gradient of g: $$ ∇g = \begin{bmatrix} \frac{\partial g}{\partial x}\\ \frac{\partial g}{\partial y}\\ \frac{\partial g}{\partial z} \end{bmatrix} $$ Since \(g(x, y, z) = xyz\), we compute the partial derivatives: $$ \frac{\partial g}{\partial x} = yz, \frac{\partial g}{\partial y} = xz, \frac{\partial g}{\partial z} = xy $$ Thus, the gradient of g is: $$ ∇g = \begin{bmatrix} yz\\ xz\\ xy \end{bmatrix} $$ To relate the gradient of F to the gradient of g, we can observe that the gradient of F can be written as a product of the gradient of g and F(x,y,z). That is: $$ ∇F = F(x,y,z) \cdot ∇g $$ Therefore, we find that the gradient of F can be related to the gradient of g by multiplying the elements of the gradient of g by the value of the function F(x, y, z).

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Key Concepts

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

Composite Functions
In multivariable calculus, composite functions are used to simplify complex problems by breaking them into simpler parts. A composite function is formed by combining two functions, where the output of one function becomes the input of another. For example, consider the function \(F(x, y, z) = e^{xyz}\). We can express \(F\) as a composite function \(f \circ g\), where \(g(x, y, z) = xyz\) and \(f(u) = e^u\).
This means that we first use the function \(g\) to compute \(u = xyz\), and then use that result as the input to the function \(f\), resulting in \(f(u) = e^u\). Thus, the original function \(F(x, y, z)\) can be seen as applying \(f\) to the output of \(g\), or \(f(g(x, y, z)) = e^{xyz}\).
Understanding composite functions is essential in calculus as it allows us to apply rules and theorems conveniently. In this way, we can take complex expressions and understand their behavior by examining their simpler components.
Gradient Vector
The gradient vector, often denoted as \( abla \), is a central concept in multivariable calculus and provides direction and rate of fastest increase of scalar fields. For a scalar function \(F(x, y, z)\), the gradient vector \( abla F \) is defined as:
  • \(abla F = \begin{bmatrix} \frac{\partial F}{\partial x} \ \frac{\partial F}{\partial y} \ \frac{\partial F}{\partial z} \end{bmatrix}\)
This vector points in the direction where the function increases most rapidly, making it helpful in optimization problems and understanding geographical surfaces among other applications.
In our example, for \(F(x, y, z) = e^{xyz}\), the gradient is:
  • \(\frac{\partial F}{\partial x} = ye^{xyz}\)
  • \(\frac{\partial F}{\partial y} = xe^{xyz}\)
  • \(\frac{\partial F}{\partial z} = xye^{xyz}\)
Thus, the gradient vector is \( abla F = \begin{bmatrix} ye^{xyz} \ xe^{xyz} \ xye^{xyz} \end{bmatrix} \).
In essence, the gradient gives us vital information both on how rapidly the function is changing and in which direction.
Partial Derivatives
Partial derivatives are a fundamental tool in understanding how functions of several variables behave. They measure how a function changes as each variable is varied independently, holding the others constant. Consider a function \(F(x, y, z)\), where its partial derivatives with respect to \(x\), \(y\), and \(z\) are calculated as:
  • \(\frac{\partial F}{\partial x}\): Keep \(y\) and \(z\) constant, differentiate according to \(x\).
  • \(\frac{\partial F}{\partial y}\): Keep \(x\) and \(z\) constant, differentiate according to \(y\).
  • \(\frac{\partial F}{\partial z}\): Keep \(x\) and \(y\) constant, differentiate according to \(z\).
These derivatives are crucial for determining the rate of change along each individual axis.
In our example with \(F(x, y, z) = e^{xyz}\), the process shows how each variable influences \(F\):
  • \(\frac{\partial F}{partial x} = ye^{xyz}\), indicating how changes in \(x\) affect \(F\).
  • \(\frac{\partial F}{partial y} = xe^{xyz}\), indicating how changes in \(y\) affect \(F\).
  • \(\frac{\partial F}{partial z} = xye^{xyz}\), indicating how changes in \(z\) affect \(F\).
Grasping partial derivatives is key in solving engineering problems, economic models, or any area involving multiple changing variables.

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