Why is this matrix symmetric here?

In summary, the conversation discusses the form of the Lagrangian in large classes of problems where ##L_{2}## is quadratic and ##L_{1}## is linear in terms of generalized velocities. It is then mentioned that under certain assumptions, the Lagrangian can be expressed in terms of a square matrix ##\mathbf{T}## which is symmetric. This is shown to be the case with the expression for kinetic energy in systems with moving constraints. This is further supported by the statement that kinetic energy is a Riemann Metric on Configuration Space, as discussed in a 1968 lecture by Saunders MacLane.
  • #1
Kashmir
468
74
Goldstein 3rd Ed, pg 339

"In large classes of problems, it happens that ##L_{2}## is a quadratic function of the generalized velocities and ##L_{1}## is a linear function of the same variables with the following specific functional dependencies:
##L\left(q_{i}, \dot{q}_{i}, t\right)=L_{0}(q, t)+\dot{q}_{i} a_{i}(q, t)+\dot{q}_{i}^{2} T_{i}(q, t)##

where the ##a_{i}^{\prime} s##and the ##T_{i}##'s are functions of the ##q## 's and ##t##.

Under the given assumptions the Lagrangian can be written as##L(q, \dot{q}, t)=L_{0}(q, t)+\tilde{\dot{q}} \mathbf{a}+\frac{1}{2} \tilde{\mathbf{q}} \mathbf{T} \dot{\mathbf{q}}##"

It's then said that ##\mathbf{T}## is symmetric.
Why is it symmetric? Is it because it's always diagonal? The author didn't say that,instead says it's just a n by n square matrix.

Please give me a hint or an answer. Thank you
 
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  • #2
With moving (i.e. time-dependent) constraints ##\mathbf{r}_a = \mathbf{r}_a(\mathbf{q},t)##, the kinetic energy is indeed generally of the form ##T = \dot{\mathbf{q}}^T \mathbf{A} \dot{\mathbf{q}} + \mathbf{B}^T \dot{\mathbf{q}} + C##, where ##\mathbf{A}##, ##\mathbf{B}## and C are all functions of ##\mathbf{q}## and ##t##.

This follows from the expression ##\dot{\mathbf{r}}_a = \sum_i \dfrac{\partial \mathbf{r}_a}{\partial q^i} \dot{q}^i + \dfrac{\partial \mathbf{r}_a}{\partial t}## for the particle velocities. Using this to write out the kinetic energy ##T = \frac{1}{2} \sum_a m_a \dot{\mathbf{r}}_a \cdot \dot{\mathbf{r}}_a## of the system, do you see why ##A_{ij} = A_{ji}## (i.e. ##\mathbf{A}## is a symmetric matrix and the quadratic form ##\dot{\mathbf{q}}^T \mathbf{A} \dot{\mathbf{q}}## is homogeneous?).
 
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Likes Kashmir and vanhees71
  • #3
ergospherical said:
With moving (i.e. time-dependent) constraints ##\mathbf{r}_a = \mathbf{r}_a(\mathbf{q},t)##, the kinetic energy is indeed generally of the form ##T = \dot{\mathbf{q}}^T \mathbf{A} \dot{\mathbf{q}} + \mathbf{B}^T \dot{\mathbf{q}} + C##, where ##\mathbf{A}##, ##\mathbf{B}## and C are all functions of ##\mathbf{q}## and ##t##.

This follows from the expression ##\dot{\mathbf{r}}_a = \sum_i \dfrac{\partial \mathbf{r}_a}{\partial q^i} \dot{q}^i + \dfrac{\partial \mathbf{r}_a}{\partial t}## for the particle velocities. Using this to write out the kinetic energy ##T = \frac{1}{2} \sum_a m_a \dot{\mathbf{r}}_a \cdot \dot{\mathbf{r}}_a## of the system, do you see why ##A_{ij} = A_{ji}## (i.e. ##\mathbf{A}## is a symmetric matrix and the quadratic form ##\dot{\mathbf{q}}^T \mathbf{A} \dot{\mathbf{q}}## is homogeneous?).
Thankyou sir.
 
  • #4

FAQ: Why is this matrix symmetric here?

Why do we care about symmetry in matrices?

Symmetry in matrices is important because it allows for easier manipulation and analysis of the data represented by the matrix. It also often indicates some underlying pattern or structure in the data.

How can we tell if a matrix is symmetric?

A matrix is symmetric if it is equal to its own transpose. In other words, if you flip the matrix along its main diagonal, the resulting matrix will be identical to the original.

What is the significance of a symmetric matrix?

A symmetric matrix has many useful properties, such as having real eigenvalues and orthogonal eigenvectors. This makes it easier to solve equations involving the matrix and to understand the relationships between its elements.

Can a non-square matrix be symmetric?

No, a non-square matrix cannot be symmetric. Symmetry requires the number of rows and columns to be equal, so only square matrices can be symmetric.

How does symmetry affect matrix operations?

Symmetry can simplify matrix operations, as certain operations (such as multiplication) can be performed more efficiently on symmetric matrices. It can also help identify relationships between different elements of the matrix.

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