Gradient of dot product using suffix notation

In summary, the conversation discusses finding the gradient of a constant vector a and position vector r, raised to the power of n, using suffix notation and the chain rule. The attempted solution includes a derivative that is incorrect, but the correct steps are provided to simplify it.
  • #1
spacetimedude
88
1

Homework Statement


Find the gradient of [tex]\underline{\nabla}(\underline{a}\cdot\underline{r})^n[/tex] where a is a constant vector, using suffix notation and chain rule.

Homework Equations


On the previous problem,s I found that grad(a.r)=a and grad(r)=[itex]\underline{\hat{r}}[/itex]

The Attempt at a Solution


[tex]
\underline{e_i}(\frac{\partial }{\partial x_i})(\underline{a}\cdot\underline{r})^n=\underline{e_i}(\frac{\partial }{\partial x_i}(a_jx_j)^n)=\underline{e_i}(n(a_jx_j)^{n-1}(a_j\delta_{ij}))
[/tex]
I'm sure that the last step is wrong so could someone lead me to the right direction?
Thank you!
 
Last edited:
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  • #2
The derivative of [itex](ax)^n[/itex] is NOT [itex]n(ax)^{n-1}[/itex].
 
  • #3
HallsofIvy said:
The derivative of [itex](ax)^n[/itex] is NOT [itex]n(ax)^{n-1}[/itex].
That is what I thought. Could you explain that step?
 
  • #4
Just after I wrote that I noticed that you also had "[itex]a_j\delta_{ij}[/itex]". The derivative of [itex](ax)^n= a^nx^n[/itex] is [itex]na^nx^{n-1}[/itex] but that can also be written as [itex](ax)^{n-2}(a)[/itex] where the last "a" is due to the chain rule. Was that what you meant?
 
  • #5
##na^nx^{n-1}## is confusing. The inner product ##(a_j x_j)^{n-1}## is lost.

When I write it out and then put it back together again ##
\underline{e_i}(n(a_jx_j)^{n-1}(a_j\delta_{ij}))## seems OK.
##
na_i \underline{e_i}\;(a_jx_j)^{n-1}## might be somewhat more elegant,
and even that can be simplified further !
 
Last edited:

Related to Gradient of dot product using suffix notation

What is the gradient of dot product using suffix notation?

The gradient of dot product using suffix notation is a mathematical concept that represents the rate of change of a scalar-valued function with respect to a vector variable. It is commonly used in vector calculus and allows for a more concise and elegant representation of vector operations.

How is the gradient of dot product calculated using suffix notation?

The gradient of dot product is calculated by taking the partial derivative of the dot product function with respect to each component of the vector variable. In suffix notation, this is represented as a summation of the partial derivatives with respect to each component, multiplied by the corresponding unit vector.

What are the benefits of using suffix notation for calculating the gradient of dot product?

Suffix notation allows for a more compact and efficient representation of vector operations, making it easier to perform calculations and derive equations. It also provides a more intuitive understanding of the dot product and gradient, as it aligns with the concept of multiplying and summing components.

Can suffix notation be used for other types of vector operations?

Yes, suffix notation can be used for a variety of vector operations, such as cross product, inner product, and outer product. It is a versatile notation that can simplify many mathematical expressions involving vectors.

Are there any limitations to using suffix notation for the gradient of dot product?

Suffix notation may not be suitable for more complex vector operations or functions with multiple variables. It also requires a good understanding of vector calculus and notation conventions to be used effectively. Other notations, such as matrix notation, may be more suitable for certain types of vector operations.

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