Calculating the Gradient of a Complex Exponential Function

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Homework Help Overview

The discussion revolves around calculating the gradient of the complex exponential function \( e^{i\vec{k}\cdot \vec{r}} \). Participants are exploring the mathematical properties and implications of this expression within the context of vector calculus.

Discussion Character

  • Mathematical reasoning, Problem interpretation, Assumption checking

Approaches and Questions Raised

  • Participants discuss breaking down the expression componentwise and consider different approaches to applying the gradient operator. There are attempts to express the gradient in terms of partial derivatives and to relate it to known theorems.

Discussion Status

The discussion is ongoing, with various interpretations of the relevant equations being explored. Some participants suggest that certain equations may not apply directly to this problem, while others propose alternative formulations to clarify the gradient calculation.

Contextual Notes

There is a noted complexity in applying the gradient operator due to the nature of the function and the assumptions about the variables involved. Participants are questioning the relevance of certain equations in this specific context.

LagrangeEuler
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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]
 
Last edited:
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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##
 
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]
 
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.
 
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].
 

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