Understanding the Notation in Lagrangian Differentiation

In summary: When you differentiate with respect to the covariant derivative, you get two terms which are exactly the same. Hence the factor of 2, which cancels the factor 1/2.
  • #1
shedrick94
30
0
I'm just in need of some clearing up of how to differentiate the lagrangian with respect to the covariant derivatives when solving the E-L equation:

Say we have a lagrangian density field
\begin{equation}
\mathcal{L}=\frac{1}{2}(\partial_{\mu}\hat{\phi})(\partial^{\mu}\hat{\phi})
\end{equation}

When solving for E-L why does:

\begin{equation}
\frac{\partial \mathcal{L}}{\partial (\partial_{\mu}\phi)}= \partial^{\mu}\hat{\phi}
\end{equation}

i.e Why do we lose the factor of 1/2
 
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  • #2
Could it be due to a power rule like ##y=(1/2) * x^2## to ##dy/dx = (1/2) * 2 * x = x## ?
 
  • #3
I always found it useful to actually write out the expression in summation notation to see what's happening.
 
  • #4
jedishrfu said:
Could it be due to a power rule like ##y=(1/2) * x^2## to ##dy/dx = (1/2) * 2 * x = x## ?
Indeed. The rules for functional differentiation follow in many respects the rules for ordinary differentation.

Use the product rule and the fact that derivatives of the fields are linearly independent. Then you get two terms which are exactly the same. Hence the factor of 2, which cancels the factor 1/2.
 
  • #5
By the way, why do you say "covariant derivatives"? Those are just partial derivatives. But in the end that doesn't matter: the covariant derivatives contain a partial derivative on phi plus a connection*phi. Phi and the partial derivative of phi are considered to be linearly independent. Just to be clear (I use deltas for functional derivation), say
$$D_a \phi = \partial_a \phi - A_a \phi $$
then
$$\frac{\delta }{\delta (\partial_a)\phi} \Bigl(\partial_b \phi - A_b \phi \Bigr) = \delta_b^a - 0 = \delta_b^a $$

so

$$\frac{\delta }{\delta (\partial_c)\phi} \Bigl(\eta^{ab} D_a \phi D_b \phi\Bigr) = \eta^{ab} \Bigl(\delta_a^c D_b \phi + D_a \phi \delta_b^c \Bigr) = 2 D_c \phi $$
 
  • #6
Write it as
$$\frac{1}{2} (\partial_{\mu} \phi) (\partial^{\mu} \phi) = \frac{1}{2} (\partial_{\mu} \phi) (\partial_{\nu} \phi)g^{\mu \nu}$$
now differentiate.
 
  • #7
He didn't elaborate on his question but I don't think we're helping. From the question it appears to me that the OP is just learning to deal with the summation notation which can be very confusing to students who have never seen it. Suppose this is just a classical field, no metric, no complex fields. Then the repeated lower and upper index means sum over that index. So, for example, if [tex]\varphi = \varphi ({x_1},{x_2},{x_3})[/tex] then [tex]\frac{1}{2}({\partial _\mu }\varphi )({\partial ^\mu }\varphi ) = \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right) + \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_2}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_2}}}} \right) + \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_3}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_3}}}} \right)[/tex] and [tex]\frac{{\partial L}}{{\partial ({\partial _1}\varphi )}} = \frac{{\partial L}}{{\partial \left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right)}} = \frac{{\partial \varphi }}{{\partial {x_1}}}[/tex] etc.

I really think his issue is notation and I suspect that's what the first response was suggesting but I think we scared the guy away.
 
  • #8
alan2 said:
He didn't elaborate on his question but I don't think we're helping. From the question it appears to me that the OP is just learning to deal with the summation notation which can be very confusing to students who have never seen it. Suppose this is just a classical field, no metric, no complex fields. Then the repeated lower and upper index means sum over that index. So, for example, if [tex]\varphi = \varphi ({x_1},{x_2},{x_3})[/tex] then [tex]\frac{1}{2}({\partial _\mu }\varphi )({\partial ^\mu }\varphi ) = \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right) + \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_2}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_2}}}} \right) + \frac{1}{2}\left( {\frac{{\partial \varphi }}{{\partial {x_3}}}} \right)\left( {\frac{{\partial \varphi }}{{\partial {x_3}}}} \right)[/tex] and [tex]\frac{{\partial L}}{{\partial ({\partial _1}\varphi )}} = \frac{{\partial L}}{{\partial \left( {\frac{{\partial \varphi }}{{\partial {x_1}}}} \right)}} = \frac{{\partial \varphi }}{{\partial {x_1}}}[/tex] etc.

I really think his issue is notation and I suspect that's what the first response was suggesting but I think we scared the guy away.
No you didn't scare me away. I do understand what the notation represents too. However, I saw in my notes that they had differentiated quickly the klein gordon lagrangian to get the klein gordon equation.

It is defnitely a notation thing though. I don't really understand the difference between
\begin{equation}
\partial_{\mu} \\ \partial^{\mu}
\end{equation}

except the factor of -1 in the spatial part.

However, if we are differentiating with respect to the covariant derivative (subscript mu) why do we differentiate the contravariant derivative (superscript mu). Or am I missing somehting entirely? Is it just that we can switch between the supersript and subscript using the raising operator?
 
Last edited:

What is Lagrangian differentiation?

Lagrangian differentiation is a mathematical technique used to find the extrema (maximum or minimum) of a function, subject to a set of constraints. It is based on the Lagrange multipliers method, which involves introducing additional variables to represent the constraints.

How is Lagrangian differentiation different from regular differentiation?

In regular differentiation, we find the extrema of a function by setting its derivative equal to zero. However, in Lagrangian differentiation, we also take into account the constraints by using the Lagrange multipliers method. This allows us to find the extrema of a function subject to certain constraints.

What are some real-world applications of Lagrangian differentiation?

Lagrangian differentiation is used in various fields such as physics, engineering, and economics. It can be used to optimize the design of structures, maximize profits in a business, or find the most efficient path for a spacecraft. It is also used in the calculus of variations, which is used to solve problems involving optimal control and optimization.

What are the limitations of Lagrangian differentiation?

While Lagrangian differentiation is a powerful technique, it has some limitations. It can only be used for functions that are differentiable and have continuous derivatives. It also assumes that the constraints are independent of each other, which may not always be the case.

Are there any alternative methods to Lagrangian differentiation?

Yes, there are other methods for finding extrema subject to constraints, such as the Kuhn-Tucker conditions and the method of undetermined multipliers. However, Lagrangian differentiation is often the most efficient and effective method for solving such problems.

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