Why Is the Gradient of a Scalar Product Best Evaluated in Cartesian Coordinates?

In summary, it is a well-known result that ##grad[e^{i\vec{k}\cdot \vec{r}}]=i\vec{k}e^{i\vec{k}\cdot \vec{r}}##. However, when considering spherical coordinates, the result is not as straightforward due to the inclusion of the non-zero second term ##\frac{\partial{F}}{\partial{\theta}}## in the gradient expression. The easiest way to derive the result is to evaluate it in Cartesian coordinates. Additionally, it is worth noting that the gradient symbol can be written in Latex as " \nabla".
  • #1
LagrangeEuler
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It is very well known result that ##grad[e^{i\vec{k}\cdot \vec{r}}]=i\vec{k}e^{i\vec{k}\cdot \vec{r}}##. Also ##\vec{k}\cdot \vec{r}=kr\cos \theta## and ##gradf(r)=\frac{df}{dr} grad r##. Then I can write
[tex]grad e^{ikr\cos \theta}=ik\cos \theta e^{i \vec{k}\cdot \vec{r}} \frac{\vec{r}}{r}=ik\frac{\vec{k}\cdot \vec{r}}{kr}e^{i\vec{k}\cdot \vec{r}} \frac{\vec{r}}{r}[/tex]
Somehow it is the same result only if ##\vec{k}=\frac{(\vec{k}\cdot \vec{r})\vec{r}}{r^2}## and this is not the same. Right?
 
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  • #2
These two are not the same, and I believe the "error" is that in spherical coordinates ## \nabla F(r,\theta,\phi)=\frac{\partial{F}}{\partial{r}}\hat{a}_r+\frac{1}{r} \frac{\partial{F}}{\partial{\theta}} \hat{a}_{\theta}+\frac{1}{r sin(\theta)}\frac{\partial{F}}{\partial{\phi}} \hat{a}_{\phi} ##, and not simply ## (df/dr) \nabla r ##. (In this case, the second term ## \frac{\partial{F}}{\partial{\theta}} ## is non-zero and needs to be included.) ## \\ ## Editing... I don't think spherical coordinates is the best approach either.. (what I wrote is not correct because it doesn't properly account for the angle between ## \vec{k} ## and ## \vec{r} ##.) The best/easiest way to see the result that ## \nabla e^{i \vec{k} \cdot \vec{r}} =i \vec{k} e^{i \vec{k} \cdot \vec{r} } ## is to simply write out the expression in Cartesian coordinates and evaluate it. ## \\ ## Additional editing: It may interest you that the gradient symbol can be written in Latex with " \nabla".
 
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FAQ: Why Is the Gradient of a Scalar Product Best Evaluated in Cartesian Coordinates?

1.

What is the gradient of scalar product?

The gradient of scalar product is a mathematical operation that calculates the rate of change of a scalar quantity in a given direction. It is represented by the symbol "∇" and is used in vector calculus to determine the direction of maximum change of a scalar function.

2.

How is the gradient of scalar product calculated?

The gradient of scalar product is calculated by taking the dot product of the vector with the gradient operator. The result is a vector that represents the direction and magnitude of the maximum change of the scalar function.

3.

What is the significance of the gradient of scalar product?

The gradient of scalar product is important in many fields of science and engineering, as it allows us to understand the behavior and changes of scalar quantities in a given space. It is used in areas such as physics, engineering, and economics to analyze and predict changes in various systems.

4.

What is the relationship between the gradient of scalar product and the directional derivative?

The gradient of scalar product is closely related to the directional derivative, as it represents the direction in which the directional derivative is maximum. The directional derivative is a measure of the rate of change of a function in a specific direction, and the gradient provides the direction of maximum change.

5.

How is the gradient of scalar product used in real-world applications?

The gradient of scalar product has many practical applications, such as in navigation systems, weather forecasting, and image processing. It is also used in optimization problems, such as finding the steepest descent in a landscape or the maximum profit in an economy.

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