Discrete Lagrangian Homework: Minimize S, Find EoM's & Discrete Trajectory

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The discussion focuses on minimizing a discrete Lagrangian to derive discrete equations of motion and trajectories for specific potential energies. Participants analyze the gradient of the discrete action and express concerns about the correctness of the potential energy derivatives in the equations of motion. They confirm that the first term aligns with Newton's second law, while debating the treatment of the potential energy terms. Clarifications are made regarding the simplification of the potential energy derivatives, leading to a consensus that the final expression resembles Newton's law. Overall, the conversation emphasizes the importance of accurately handling derivatives in the context of discrete mechanics.
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Homework Statement


In this exercise, we are given a discrete Lagrangian which looks like this: http://imgur.com/TL0P61r. We have to minimize the discrete S with fixed point r_i and r_f and find the the discrete equations of motions.
In the second part we should derive a discrete trajectory for U(r) = 0 and U(r) = mgz.

Homework Equations


j = t_i + j \Delta t ; \Delta t = (t_f - t_i)/N
r_j = r(t_i + j \Delta t)
v_j = \frac{r_j - r_{j-1}}{\Delta t}[/B]
j = 0, ..., N
U(r) is the potential energy
And the discrete action:
S(r_j) = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(U(\boldsymbol{r_j})+ U(\boldsymbol{r_{j-1}})\right)\right)

The Attempt at a Solution


Since S is a function, I thought I could take the gradient and set that to 0 to derive the discrete equations of motions: <br /> 0 = \frac{\partial}{\partial r_k} S = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\frac{\partial}{\partial r_k}\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(\frac{\partial}{\partial r_k} U(\boldsymbol{r_j})+ \frac{\partial}{\partial r_k}U(\boldsymbol{r_{j-1}})\right)\right)<br /> \\<br /> \rightarrow \sum_{j=1}^N \Delta t \left(m \left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)\left(\frac{\delta_{jk}-\delta_{j-1,k}}{\Delta t}\right) -\frac{1}{2} \left(\frac{\partial U(\boldsymbol{r_j})}{\partial r_k}\delta_{jk} + \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}\right)\right)<br /> \\<br /> \rightarrow 0 = m \left(\frac{-\boldsymbol{r_{k+1}}-\boldsymbol{r_{k-1}}+2\boldsymbol{r_k}}{\Delta t^2}\right) - \frac{1}{2} \left(\frac{\partial U(r_k)}{\partial r_k} + \frac{\partial U(r_{k+1})}{\partial r_k}\right)<br />

The first term seems to be right, it looks like F = ma at least. I am not sure about the second part though because of that factor of 1/2 and I am not sure if the derivatives of the potential energy are correct.
For part b, I think I would have to insert the potential energy in the discrete equations of motions and integrate two times with respect to the time. But I am not sure how an integral would work here because of those indices.
Anyways, I would be really grateful for any advice! Thank you
 
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Paumi said:
Since S is a function, I thought I could take the gradient and set that to 0 to derive the discrete equations of motions: <br /> 0 = \frac{\partial}{\partial r_k} S = \sum_{j=1}^N \Delta t \left(\frac{1}{2}m\frac{\partial}{\partial r_k}\left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)^2 - \frac{1}{2}\left(\frac{\partial}{\partial r_k} U(\boldsymbol{r_j})+ \frac{\partial}{\partial r_k}U(\boldsymbol{r_{j-1}})\right)\right)<br /> \\<br /> \rightarrow \sum_{j=1}^N \Delta t \left(m \left(\frac{\boldsymbol{r_j} - \boldsymbol{r_{j-1}}}{\Delta t}\right)\left(\frac{\delta_{jk}-\delta_{j-1,k}}{\Delta t}\right) -\frac{1}{2} \left(\frac{\partial U(\boldsymbol{r_j})}{\partial r_k}\delta_{jk} + \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}\right)\right)
I think everything is good so far.
\rightarrow 0 = m \left(\frac{-\boldsymbol{r_{k+1}}-\boldsymbol{r_{k-1}}+2\boldsymbol{r_k}}{\Delta t^2}\right) - \frac{1}{2} \left(\frac{\partial U(r_k)}{\partial r_k} + \frac{\partial U(r_{k+1})}{\partial r_k}\right)
I don't think the very last term on the right is correct. You might reconsider what the expression ##\sum_{j=1}^N \frac{\partial U(\boldsymbol{r_{j-1}})}{\partial r_k}\delta_{j-1,k}## reduces to.
 
Wouldn't it reduce to \frac{\partial U(r_{k})}{\partial r_k} j = r_{k+1} because only for j=k+1 the kroenecker delta would give me a 1? And when you would have \frac{\partial U(r_{k+1-1})}{\partial r_k} = \frac{\partial U(r_{k})}{\partial r_k} and then I would have two \frac{\partial U(r_{k})}{\partial r_k} and I could cancle the 1/2 out?
 
Yes.
 
Hey thank you very much! Now the discrete equation looks like Newtons 2nd law just as was asked :')
 

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