Calculating the Gradient of a Complex Exponential Function

In summary, the homework equation for calculating \nabla e^{i\vec{k}\cdot \vec{r}} is as follows: e^{i\vec{k}\cdot \vec{r}}=i\vec{k} e^{i\vec{k}\cdot \vec{r}}.
  • #1
LagrangeEuler
717
20

Homework Statement


Calculate [tex]\nabla e^{i\vec{k}\cdot \vec{r}}[/tex]

Homework Equations


[tex]\nabla f(r)=\frac{df}{dr}\nabla r=\frac{df}{dr}\frac{\vec{r}}{r} [/tex]

The Attempt at a Solution


I have a problem. I know result
[tex]=\nabla e^{i\vec{k}\cdot \vec{r}}=i\vec{k} e^{i\vec{k}\cdot \vec{r}}[/tex]
 
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  • #2
This problem is much clearer when broken up componentwise:
##e^{i \vec k \cdot \vec r } = e^{i (k_x x + k_y y + k_z z) } ##
Where ##\vec k = k_x \hat x + k_y \hat y + k_z \hat z ## and ##\vec r = x \hat x + y \hat y + z \hat z##
 
  • #3
Yes but for that way we need a lot of time. Perhaps
[tex]\nabla=\sum_w\vec{e}_w\frac{\partial}{\partial x_w}[/tex]
[tex]e^{i \vec{k}\cdot \vec{r}}=e^{i\sum_q k_q x_q} [/tex]
but I get a problem with this sums. Maybe
[tex]\sum_w\vec{e}_w\frac{\partial}{\partial x_w}e^{i\sum_q k_q x_q}=[/tex]
[tex]=\sum_w\vec{e}_we^{i\sum_q k_q x_q}(\frac{\partial}{\partial x_w}i\sum_q k_q x_q)=[/tex]
[tex]=e^{i\vec{k}\cdot \vec{r}}i \sum_w \vec{e}_w k_{w}=[/tex]
[tex]=i\vec{k}e^{i\vec{k}\cdot \vec{r}}[/tex]

but again I did not use theorem
[tex]\nabla f(r)=\frac{df}{dr}\nabla r[/tex]
 
  • #4
LagrangeEuler said:
but again I did not use theorem
[tex]\nabla f(r)=\frac{df}{dr}\nabla r[/tex]
let ##f(\vec r) = e^{ik_x \hat x +ik_y \hat y+ik_z \hat z}## and ##\vec r = ik_x \hat x +ik_y \hat y+ik_z \hat z = i \vec k \cdot \vec r##
then use the theorem.
 
  • #5
I think that "relevant equation",
[itex]\nabla f(r)=\frac{df}{dr}\nabla r=\frac{df}{dr}\frac{\vec{r}}{r}[/itex],
is not that relevant here:
[itex]e^{i\vec{k}\cdot \vec{r}}\neq f(r)[/itex] since [itex]r = (x^2 + y^2 + z^2)^{1/2}[/itex].

Rather,
[itex]\nabla f(g(\vec r))=\frac{df}{dg}\nabla g(\vec r)[/itex]
with
[itex] f(g) = e^g [/itex] and [itex] g(\vec r) = i \vec k \cdot \vec r[/itex].
 

1. What is the Nabla problem?

The Nabla problem, also known as the gradient problem, is a mathematical concept that involves finding the gradient of a function in a specific direction. It is commonly used in fields such as physics, engineering, and computer science.

2. What does the gradient represent?

The gradient represents the rate of change of a function with respect to its input variables. It is a vector that points in the direction of the steepest increase of the function.

3. How is the gradient calculated?

The gradient is calculated by taking the partial derivatives of the function with respect to each input variable and combining them into a vector. It can also be represented as the dot product of the del operator (represented by the symbol ∇) and the function.

4. What is the significance of the gradient in optimization?

The gradient plays a crucial role in optimization problems, as it helps determine the direction in which the function will have the steepest increase. It is used to find the minimum or maximum value of a function, which is important in fields like machine learning and data analysis.

5. Are there any applications of the Nabla problem in real life?

Yes, the Nabla problem has various real-life applications, such as in determining the flow of heat or fluid in physics and engineering, in image processing and computer vision, and in optimizing algorithms in computer science. It also has applications in economics, finance, and other fields that involve finding the maximum or minimum value of a function.

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