Vector analysis for a variational problem

In summary, the conversation discusses a proof involving a variation of the vectorpotential, \vec{A}, represented by \vec{\delta A}. The formula presented is incorrect, as the right hand side should include an additional term - (\nabla \cdot \vec{\delta A})*\vec{A}. This is explained in more detail on page 10 of the attached file.
  • #1
Angelos K
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I'm reading a proof that is via variation. [tex] \vec {\delta A} [/tex] stands for a variation of the vectorpotential [tex] \vec{A} [/tex]. If I understand the argument correctly [many steps are presented as one] it means:

[tex] (\nabla \times \vec{A})* (\nabla \times \vec{\delta A}) = (\nabla \times \nabla \times \vec {A})*\vec{\delta A} [/tex].

I do not believe it. If I calculate the brackets on the left hand side individually I don't get any undifferentiated [tex] \vec{\delta A} [/tex]. What am I doing wrong?

You can find the "whole" calculation on page 10 of the attached file.
 

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  • #2
The formula you have written is incorrect. The right hand side should be (\nabla \times \nabla \times \vec {A})*\vec{\delta A} - (\nabla \cdot \vec{\delta A})* \vec{A}. The first term on the right hand side corresponds to the calculation of the brackets on the left hand side individually, while the second term is the extra contribution that you are missing. To understand why this is true, you can consult page 10 of the attached file, which explains the entire calculation in more detail.
 
  • #3


Vector analysis for a variational problem involves using vector calculus to solve problems involving variations in vector quantities. In this case, the proof you are reading is using variations to solve a problem involving the vector potential, represented by \vec{A}. The notation \vec{\delta A} represents a variation of the vector potential.

The equation you have presented, (\nabla \times \vec{A})* (\nabla \times \vec{\delta A}) = (\nabla \times \nabla \times \vec {A})*\vec{\delta A}, is a key step in the proof. It is showing that the cross product of the curl of \vec{A} and the curl of \vec{\delta A} is equal to the curl of the curl of \vec{A} multiplied by \vec{\delta A}. This step is valid because of the properties of vector calculus, specifically the fact that the curl of a curl is equal to the gradient of the divergence minus the Laplacian.

It is possible that you are not getting the same result when calculating the brackets on the left hand side because you are not considering the properties of vector calculus. It is important to remember that vector calculus involves operations on vector quantities, not just scalar quantities. Therefore, the rules for differentiation and multiplication may not always apply in the same way. I recommend reviewing the properties and rules of vector calculus to better understand this step in the proof.
 

FAQ: Vector analysis for a variational problem

What is vector analysis for a variational problem?

Vector analysis for a variational problem is a mathematical method used to find the optimal solution to a problem, where the solution is represented as a function. This method involves the use of vector calculus and optimization techniques to find the function that minimizes a given functional.

What is a variational problem?

A variational problem is a type of mathematical problem where the goal is to find the function that minimizes or maximizes a certain functional. This functional can be thought of as a rule that assigns a value to each possible function, and the variational problem is to find the function that has the lowest or highest value according to this rule.

What is the difference between a scalar and a vector functional?

A scalar functional is a functional that assigns a single value to a function, while a vector functional assigns a vector of values to a function. In vector analysis for a variational problem, the functional that is minimized is typically a scalar functional, but the solution is represented as a vector function.

What is the Euler-Lagrange equation and how is it used in vector analysis for a variational problem?

The Euler-Lagrange equation is a mathematical equation that is used to find the optimal solution to a variational problem. It is derived from the calculus of variations and involves taking the derivative of the functional with respect to the function being optimized. In vector analysis for a variational problem, the Euler-Lagrange equation is used to find the vector function that minimizes the given scalar functional.

What are some real-world applications of vector analysis for variational problems?

Vector analysis for variational problems has a wide range of applications in various fields such as physics, engineering, and economics. Some examples include finding the shortest path for a particle to travel between two points, determining the shape of a beam that can support the most weight, and finding the optimal control strategy for a manufacturing process to minimize costs.

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