Solving 1D/2D Eigenvalue Equation for Proving Function

  • Thread starter jonny81
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In summary, the function \mathcal{A} in the 1D case satisfy \mathcal{A}=\frac{48}{m}\sum_{j=1}^\infty \frac{\sin^2(qj/2)}{j^5}=\frac{12}{m}\left[2\zeta(5)-\text{Li}_5(e^{iq})-\text{Li}_5(e^{-iq})\right],with \text{Li}_n(z) the polylogarithm function, and the matrix \mathcal{A} in the 2D case satisfy
  • #1
jonny81
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Hello,

I want to prove that the function [itex]\mathcal{A}[/itex] in the 1D case satisfy

[itex]\mathcal{A}=\frac{48}{m}\sum_{j=1}^\infty \frac{\sin^2(qj/2)}{j^5}=\frac{12}{m}\left[2\zeta(5)-\text{Li}_5(e^{iq})-\text{Li}_5(e^{-iq})\right],[/itex]

with [itex]\text{Li}_n(z)[/itex] the polylogarithm function, and the matrix [itex]\mathcal{A}[/itex] in the 2D case satisfy

[itex]\mathcal{A}=\frac{3}{m}\sum_{j\neq 0}\left[1-\frac{5}{|\vec{r}_j^0|^2}\begin{pmatrix}(x_j^0)^2&x_jy_j\\x_jy_j&(y_j^0)^2\end{pmatrix}\right]\frac{\left(1-e^{i\vec{q}\vec{r}_j^0}\right)}{|\vec{r}_j^0|^5}[/itex]

Can somebody help me, please, to do this? In 2D the equilibrium positions [itex]\vec{r}_i^0 [/itex] form a triangular lattice with basic lattice vectors [itex]a_1 = (1, 0) [/itex] and [itex]a_2 = (1,\sqrt{3})/2[/itex].

Starting point:

[itex]m\vec{\ddot x}_i=\frac{3}{2}\sum_{i\neq j}^{N}\left[\frac{5(\vec{r}_i^0 - \vec{r}_j^0)^2\left(\vec{x}_j-\vec{x}_i\right)}{|\vec{r}_i^0 - \vec{r}_j^0|^7}+\frac{\left(\vec{x}_i-\vec{x}_j\right)}{|\vec{r}_i^0 - \vec{r}_j^0|^5}\right][/itex]

with the ansatz

[itex]\vec{x}_i(t)=\epsilon_\lambda(\vec{q})e^{i\left(\vec{q}\vec{r}_i^0-\omega_\lambda(\vec{q})t\right)}[/itex]
[itex]\vec{\ddot x}_i(t)=-\omega^2\epsilon_\lambda(\vec{q})e^{i\left(\vec{q}\vec{r}_i^0-\omega_\lambda(\vec{q})t\right)}[/itex]

follows

[itex]-\omega^2_\lambda(\vec{q})\epsilon_\lambda(\vec{q})=\mathcal{A}\epsilon_\lambda(\vec{q})[/itex]

Thanks!
 
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  • #2
new to PF?
what have you tried? is this a condense matter problem?
 
  • #3
mjsd said:
new to PF?

Yes!

mjsd said:
what have you tried?

with the ansatz in the equation of motion, i obtain

[itex]\mathcal{A}=\frac{3}{2m}\sum_{i\neq j}\left[1-\frac{5}{|\vec{r}_i^0-\vec{r}_j^0|^2}\left(\vec{r}_i^0-\vec{r}_j^0\right)^2 \right]\frac{\left(1-e^{i\vec{q}(\vec{r}_i^0-\vec{r}_j^0)}\right)}{|\vec{r}_i^0-\vec{r}_j^0|^5}[/itex]

What I have to do next? I do not make any differentiation about 1D and 2D.

mjsd said:
is this a condense matter problem?

I consider a dipolar crystal.
 
  • #4
your question is asked in some kind of a strange way... I don't even know what you are asking...or what are you trying to prove? just sub in the ansatz and check? what are you having problems with? it is difficult for anyone to help without knowing what you are stuck at. Besides in this forum you are meant to show us what you have tried
 

Related to Solving 1D/2D Eigenvalue Equation for Proving Function

1. What is the purpose of solving 1D/2D eigenvalue equations?

The purpose of solving 1D/2D eigenvalue equations is to find the eigenvalues and eigenvectors of a given function. This information can then be used to understand the behavior and properties of the function, such as its stability and convergence.

2. How do you solve 1D/2D eigenvalue equations?

To solve 1D/2D eigenvalue equations, you can use numerical methods such as the power method, inverse iteration, or Jacobi method. These methods involve iteratively solving for the eigenvalues and eigenvectors until a desired level of accuracy is achieved.

3. What are the applications of solving 1D/2D eigenvalue equations?

The applications of solving 1D/2D eigenvalue equations are vast and diverse. It is commonly used in physics, engineering, and other scientific fields to understand the behavior of systems and to design optimal solutions. It is also used in data analysis, signal processing, and machine learning to find important features and patterns in data.

4. Can 1D/2D eigenvalue equations be solved analytically?

In some cases, 1D/2D eigenvalue equations can be solved analytically using algebraic methods. However, in most cases, numerical methods are required due to the complexity of the equations and the large number of eigenvalues and eigenvectors that need to be computed.

5. What are the limitations of solving 1D/2D eigenvalue equations?

The main limitation of solving 1D/2D eigenvalue equations is the computational cost. As the size and complexity of the function increases, the time and resources required for solving the equations also increase. Additionally, the accuracy of the solutions may be affected by the choice of numerical method and the initial guess for the eigenvalues and eigenvectors.

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