Shortest path on dynamic graph

In summary: In that case, you could use software such as MATLAB or R to run simulations and analyze the data. However, if you are looking for a specific software for graphing infimum, I am not aware of one.
  • #1
Dragonfall
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Shortest path on dynamic "graph"

Suppose you have n objects orbiting Earth with velocities v1, ..., vn. Starting from t=0, the objects are at positions x1,..., xn; how do you calculate at what point they will be at a state such that the shortest path (arcs of great cirlces) connecting all of them will exist? Is it possible that no such configuration exists (there will always be a shorter one)?
 
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  • #2
Anyone?
 
  • #3
I don't know how to describe the answer mathematically but I think such a state exists since the satellites have different velocities. So considering an infinite time there will be a high probability for such a state to exist.
 
  • #4
Since you say the paths joining the particles are arcs of great circles, I assume that x1, x2, ... ,xn are each two-dimensional coordinates [itex] x_i = (\theta_i,\phi_i)[/itex] and all of the objects are constrained to a spherical shell with radius greater than the earth.

First, consider the case where the all the objects lie on a great circle (wolog, take [itex]\phi_i[/itex] t be zero for each i). Two cases:

Case 1: For each [itex]i,j[/itex] we have [itex]\frac{v_i}{v_j}[/itex] be a rational number. In this case there is some time t* (in fact infinitely many such times since the motion is periodic in this case) such that [itex] x_i = x_j [/itex] for all i,j. In other words, all the "objects" collide simultaneously at time t*. The proof of this consist of trig identities.

Case 2. If any two of the orbits are not commensurate, then there is always a shorter distance. This is a consequence of the above trig identities and the fact that the image of the Sequence {Sin(n)} is dense in [0,1].

The extension to the general case only requires that we consider not the ratios between the magnitudes of the velocities, but rather all possible ratios between (both of) the components of (each of) the velocities. If these ratios are all rational, then there is a time when the particles will coincide; otherwise there will always be a shorter time. The proof is in the same spirit as the above.
 
  • #5
Suppose I want to graph the infimum something like this, what software would I have to use?
 
  • #6
The infimum is always zero, I apologize for not making that clear.

It sounds like you might be more interested in a statistical analysis, e.g. what is the chance that the path between the objects is smaller than distance x before time t (where time t could be a billion years).
 

1. What is a dynamic graph?

A dynamic graph is a type of graph where the structure and connections between nodes can change over time. This means that nodes can be added or removed, and edges can be added or deleted, making it a constantly evolving and changing network.

2. How is the shortest path on a dynamic graph different from a static graph?

In a static graph, the structure and connections between nodes remain constant, allowing for the calculation of a single shortest path. However, in a dynamic graph, the constant changes in the network make it necessary to continuously update the shortest path as the graph evolves.

3. What is the importance of finding the shortest path on a dynamic graph?

Finding the shortest path on a dynamic graph is essential in many real-world applications, such as navigation systems and transportation networks. It helps in determining the most efficient and cost-effective route between two points, taking into account any changes or disruptions in the network.

4. How is the shortest path on a dynamic graph calculated?

The shortest path on a dynamic graph is typically calculated using algorithms such as Dijkstra's algorithm or the A* algorithm. These algorithms continuously update the shortest path as the graph changes, taking into account the cost or weight of each edge and finding the path with the lowest total cost.

5. What are some challenges in finding the shortest path on a dynamic graph?

One of the main challenges in finding the shortest path on a dynamic graph is the constant changes in the network, which can result in a significant increase in computational time and resources. Additionally, the frequent updates can also lead to potential errors and inconsistencies in the calculated shortest path.

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