Proof of product rule for gradients

In summary, the del operator is a vector operator that can be multiplied by a scalar using the usual product. When applied to a dot product of two vector fields, it yields a vector field. However, if the dot product is constant, the result will be 0. The dot product of two vector fields is a scalar field, and the gradient of a scalar field is a vector field. Therefore, the product rule still holds in this case.
  • #1
Alvise_Souta
4
1
product rule.png

Can someone please help me prove this product rule? I'm not accustomed to seeing the del operator used on a dot product. My understanding tells me that a dot product produces a scalar and I'm tempted to evaluate the left hand side as scalar 0 but the rule says it yields a vector. I'm very confused
 
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  • #2
Alvise_Souta said:
My understanding tells me that a dot product produces a scalar and I'm tempted to evaluate the left hand side as scalar 0 but the rule says it yields a vector.
Del operator ##\nabla## is a vector operator, following the rule for well-defined operations involving a vector and a scalar, a del operator can be multiplied by a scalar using the usual product. ##\mathbf{A}\cdot \mathbf{B}## is a scalar, but a vector (operator) ##\nabla## comes in from the left, therefore the "product" ##\nabla (\mathbf{A}\cdot \mathbf{B})## will yield a vector.
 
  • #3
So it becomes a vector because it doesn't evaluate to 0 since the del operator is used on a scalar function rather than a scalar constant?
 
  • #4
Alvise_Souta said:
So it becomes a vector because it doesn't evaluate to 0 since the del operator is used on a scalar function rather than a scalar constant?
Well that's a matter of whether ##\mathbf{A}\cdot \mathbf{B}## is coordinate dependent or not. If this dot product turns out to be constant, then ##\nabla(\mathbf{A}\cdot \mathbf{B})=0##.
 
  • #5
A and B are meant to be interpreted as vector fields (physics texts frequently conflate vector fields with vectors, using the word "vector" to refer to both vectors and vector fields, where the exact meaning is meant to be gleaned from context): functions that assign a unique vector to each point. The dot product of two vector fields is therefore a scalar field, as it is meant to be interpreted as the function that assigns the dot product of the two vectors assigned by A and B, respectively, at each point to each point. The gradient of a scalar field is a vector field (or covector field, depending on how formal you want to get).
 

Related to Proof of product rule for gradients

What is the product rule for gradients?

The product rule for gradients is a formula used in calculus to find the gradient of a product of two functions. It states that the gradient of a product of two functions is equal to the first function multiplied by the gradient of the second function, plus the second function multiplied by the gradient of the first function.

How is the product rule for gradients derived?

The product rule for gradients can be derived using the chain rule and the definition of the dot product. By taking the partial derivatives of the product of two functions with respect to each variable, the product rule can be obtained.

When is the product rule for gradients used?

The product rule for gradients is used when finding the gradient of a product of two functions, such as in multivariable calculus or in physics problems involving vector quantities.

What are the limitations of the product rule for gradients?

The product rule for gradients is limited to finding the gradient of a product of two functions. It cannot be applied to products of more than two functions or to functions that are not differentiable.

Can the product rule for gradients be extended to higher dimensions?

Yes, the product rule for gradients can be extended to higher dimensions by using the generalized product rule, which involves taking the gradient of a product of multiple functions and multiplying it by the gradients of the individual functions. This can be applied to functions with any number of variables.

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