On covariance of the Lagrange equations

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The discussion presents a deduction of the Lagrange equations using a covariance argument, proposing a modern alternative to traditional terminology in textbooks. It begins by defining a system of particles with specific position vectors and assumes ideal holonomic constraints, leading to a characterization of the system's position through generalized coordinates. The Lagrange-D'Alembert principle is introduced, followed by a reformulation that incorporates covariance, demonstrating how the equations can be expressed in terms of smooth manifolds. The generality of the derived formula indicates its applicability to various smooth functions. This approach emphasizes the mathematical structure underlying the Lagrange equations, enhancing clarity and understanding in the context of modern physics.
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Here is a sketch of deduction of the Lagrange equations by means of the covariance argument. I believe that it is a suitable substitute for the archaic terminology that is employed in most textbooks.

Assume we have ##\nu## particles with masses ##m_1,\ldots,m_\nu## and with position vectors $$\boldsymbol r_i=(x^{3(i-1)+1},x^{3(i-1)+2},x^{3(i-1)+3})\in\mathbb{R}^3,\quad i=1,\ldots,\nu.$$
Thus the position of the system is characterized by a vector ##x=(x^1,\ldots,x^m)^T\in\mathbb{R}^m,\quad m=3\nu.##
Assume also that the system is restricted by ideal holonomic constraints so that ##x=x(t,y)## where ##y=(y^1,\ldots,y^r)## are the local coordinates (generalized coordinates) on a smooth ##r-##dimensional manifold ##Y## and ##y\mapsto x(t,y)## is a smooth embedding of ##Y## into ##\mathbb{R}^m## for each ##t##.
The Lagrange-D'Alembert principle is
$$\sum_i(m_i\boldsymbol{\ddot r_i}-\boldsymbol F_i,\delta \boldsymbol r_i)=0.\qquad (1)$$
Here ##\boldsymbol F_i## are the given forces.
Formula (1) can be rewritten as follows
$$([\mathscr T]_j-f_j)\frac{\partial x^j}{\partial y^s}=0,\qquad (2) $$
where ##f=(f_1,\ldots,f_m)=(\boldsymbol F_1,\ldots,\boldsymbol F_\nu),##
$$\mathscr T=\frac{1}{2}\dot x^TG\dot x,\quad G=\mathrm{diag}(m_1,m_1,m_1,\ldots,m_\nu,m_{\nu},m_{\nu});$$
and
$$[\mathscr T]_j=\frac{d}{dt}\frac{\partial \mathscr T}{\partial \dot x^j}-\frac{\partial \mathscr T}{\partial x^j}.$$
Now employ the covariance:
$$[\mathscr T]_j\Big|_{x=x(t,y)}\frac{\partial x^j}{\partial y^s}=[T]_s,\qquad (3)$$
where ##T(t,y,\dot y)=\mathscr T\mid_{x=x(t,y)}.##

Formula (3) is very general it holds for any smooth ##\mathscr T=\mathscr T(t,x,\dot x).##
It is like to accomplish the pullback of an 1-form from the manifold ##\mathbb{R}^m## to the manifold ##Y##.

Thus (2) takes the form
$$[T]_s=Q_s,\quad Q_s=f_j\frac{\partial x^j}{\partial y^s}.$$
 
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