Understanding Diagonalization of Lagrangian: Graduate Mechanics Text Explanation

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My Graduate Mechanics text (Walecka and Fetter) says in the chapter on Small Oscillations, that the Modal Matrix Diagonalizes the Lagrangian

[tex]L = \sum_{\sigma=1}^\infty (\dot{\zeta_\sigma}^2 - \omega_\sigma \zeta_\sigma^2)[/tex]

where [tex]\zeta[/tex] are the normal coordinates related to the original coordinates [tex]\eta[/tex] via the modal matrix.

But as far as I understand, the Lagrangian is simply a number, not a matrix. So what does the diagonalization of the Lagrangian mean.
 
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The Lagrangian is not a number, it's a function. In the case of small oscillations, it happens to be a quadratic form, and it can be written as

[tex]L = Q^{T}AQ[/tex],

Where [tex]Q = (q_1, q_2, ..., q_s, \dot{q}_1, \dot{q}_2, ..., \dot{q}_s)[/tex]. When the [tex]q_i[/tex] are normal coordinates, [tex]A[/tex] becomes a diagonal matrix.
 
dx said:
The Lagrangian is not a number, it's a function. In the case of small oscillations, it happens to be a quadratic form, and it can be written as

[tex]L = Q^{T}AQ[/tex],

Where [tex]Q = (q_1, q_2, ..., q_s, \dot{q}_1, \dot{q}_2, ..., \dot{q}_s)[/tex]. When the [tex]q_i[/tex] are normal coordinates, [tex]A[/tex] becomes a diagonal matrix.

Sorry for saying that its a number but I still didn't get it. And I think I basically want to know how can Lagrangian the function be transformed into Lagrangian the matrix. e.g. we have [tex]2L = \dot{\eta}^T m \dot{\eta} - \eta^T v \eta[/tex]. But the [tex]\eta[/tex] are essentially column vectors, giving me a function in the form of a 1x1 matrix after multiplication. So what does it mean to write the Lagrangian in the form of a matrix
 
Well, its not exactly a matrix. But matrices are not the only things that can be diagonalized. Quadratic forms for example can be diagonalized. If you have some random set of generalized coordinates and velocities [tex]X = (x_i, \dot{x}_i)[/tex], then the Lagrangian for small oscillations is of the form

[tex]L = \frac{1}{2}\sum (m_{ik} \dot{x_i} \dot{x_k} - k_{ik} {x_i} {x_k})[/tex].

This can be written in matrix notation as [tex]L = X^{T}AX[/tex], where A is in general not diagonal. This is the modern way of representing quadratic forms. It turns out that there is a specific set of coordinates [tex]Q_i[/tex] in terms of which the Lagrangian simplifies into the form

[tex]L = \frac{1}{2}\sum_{\alpha} (\dot{Q_{\alpha}^2} - {\omega_{\alpha}^2} {Q_{\alpha}^2})[/tex].

When you write this in the form [tex]Q^{T}AQ[/tex], A will be a diagonal matrix.
 
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