I want to divide a graph to subgraphs that each subgraph made from maximum 3 vertices and sum of weight of edges is minimized, the main graph is complete ( have all possible edges ), and edges are weighted.
the main problem that i want to solve is finding close three threes points on a map.
I'm sure this problem is NP-Complete. It's called the minimum k-cut problem.
Try to take a look at this article. It talks about approximation algorithms to solve such problems.
Related
I want to create a set of N random convex disjoint polygons on a plane where each polygon must have at most V vertices, where N and V are parameters for my function, and I'd like to obtain a distribution as close as possible to uniform (every possible set being equally probable). Also I need to randomly select two points on the plane that either match with one of the vertices in the scene or are in empty space (not inside a polygon).
I already implemented for other reasons in the same programming language an AABB tree, Separating Axis Theorem-based collision detection between convex polygons and I can generate a random convex polygon with arbitrary amount of vertices inside a circle of given radius. My best bet thus far:
Generate a random polygon using the function I have available.
Query the AABB tree to test for interception with existing polygons.
If the AABB tree query returns empty set I push the generated polygon into it, otherwise I test with SAT against all the other polygons whose AABB overlaps with the generated one's. If SAT returns "no intersection" I push the polygon, otherwise I discard it.
Repeat from 1 until N polygons are generated.
Generate a random number in {0,1}
If the generated number is 1 I pick a random polygon and a random vertex on it as a point
If the generated number is 0 I generate a random position in (x,y) and test if it falls within some polygon (I might create a tiny AABB around it and exploit the AABB tree to reduce the required number of PiP tests). In case it's not inside any polygon I approve it as a valid point, otherwise I repeat from 5.
Repeat from 5 once more to get the second point.
I think the solution would possibly work, but unfortunately there's no way to guarantee that I can generate N such polygons for very large N, or find two good points in an acceptable time, and I'm programming in React, where long operations run on the main thread blocking the UI till they end. I could circumvent the issue by ejecting from create-react-app and learn Web Workers, which would require probably more time than it's worth for me.
This is definitely non-uniform distribution, but perhaps you could begin by generating N points in the plane and then computing the Voronoi diagram for those points. The Voronoi diagram can be computed in O(n log n) time with Fortune's algorithm. The cells of the Voronoi diagram are convex, so you can then construct a random polygon of the desired number of vertices that lies within each cell of the diagram.
By Balu Ertl - Own work, CC BY-SA 4.0, Link
Ok, here is another proposal. I have little knowledge of js, but could cook up something in Python.
Use Poisson disk sampling with distance parameter d to generate N samples of the centers
For a given center make a circle with R≤d.
Generate V angles using Dirichlet distribution such that sum of angles is equal to 2π. Sort them.
Place vertices on the circle using angles generate at step#3 and connect them. This would be be your polygon
UPDATE
Instead using Poisson disk sampling for step 1, one could use Sobol quasi-random sequences. For N points sampled in the 1x1 box (well, you have to scale it afterwards), least distance between points would be
d = 0.5 * sqrt(D) / N,
where D is dimension of the problem, 2 in your case. So radius of the circle for step 2 would be 0.25 * sqrt(2) / N. This ties nicely N and d.
https://www.sciencedirect.com/science/article/abs/pii/S0378475406002382
I'm trying to use path finding on a series of convex polygons, rather than waypoints. To even further complicate this, the polygons are made by the users, and may have inconsistent vertices. For example:
We know the object is X wide by Y deep, and that the polygons have vertices at certain locations. Is there a specific algorithm to find the fastest way to the goal while keeping the entire object in the polygons (If I understand correctly, A* only works on waypoints)? How do you handle the vertices not being the same object but being at the same location?
EDIT: The polygons are convex; It's 2 separate polygons with the edges on the line.
Also, how do you implement * pathfinding, as a node based system wouldn't work in a 'infinite' resolution polygon?
In general, all shortest-path segments will have, as end-points, either polygon vertices or the start and goal points. If you build a graph that includes all those segments (from the start to each "visible" polygon vertex, from the goal to each "visible" polygon vertex, and from each polygon vertex to each other polygon vertex) and run A* on that, you have your optimal path. The cost of building the graph for A* is:
For each vertex, a visibility-test to find visible vertices: the simple algorithm (for each pair of vertices, see if the segment from one to another lies inside the polygon) is O(n^3). Building convex polygons and processing them independently, or using a smarter "radial sweep" algorithm can greatly lower this, but I suspect it is still around O(n^2).
For each query (from a start-point to a goal-point), O(n) for the visibility-test to find all vertices that it can see.
If you are only going to apply A* once, then the price of building the fixed part of the A* graph for a single traversal may be somewhat steep. An alternative is to build the graph incrementally as you use it:
Java code implementing the above approach can be found here.
The polygons in your drawing are not convex. For convex polygons, you can place a way point in the middle of each each edge and then apply A*. And, of course, you need to fix inconsistent vertices.
Is it possible to implement the Christofides algorithm for an directed Graph?
Suppose you have an undirected Graph, in which every vertex has an edges in both ways to every other in the graph (not to itself). But the weights of the edges, don't necessarily have do be the same in both ways (unsymmetrical).
For Example you think of a Street Map, in which there are a lot of oneway streets.
We now want to find an approximation for the traveling salesman tour through all the vertices.
First of all the Christoffides algorithm is not defined for such an TSP, because the Minimum Spanning Tree ist not defined for an directed Graph.
But still we start the algorithm by finding the optimum branching with Edmonds algorithm to the start point of the tour as the root.
Then we find a minimal perfect matching for the branching, so that it becomes an Eulerian graph. This will happen with the Hungarian algorithm, wich finds an minimal matching so that every vertex in the branching has afterwords the same amount of edges coming in an out.
In the last step we find the euler tour and optimize the tour by taking shortcuts.
I have to questions:
Is the way I want to implement the algorithm right, or did I made a
mistake and it can't work
If it works, is it still bounded bei 1,5 of the optimal solution for the tsp?
This is a homework question. I do not want the solution - I'm offering the solution I've been thinking of and wish to know whether is it good or why is it flawed.
My motivation is to find what edges of an unweighted, undirected graph are not a part of any MST. This problem only makes sense when several edges have the same values, otherwise the MST is unique.
My idea comes from Prim's Algorithm with a slight change - instead of adding the minimum edge from S to T on every step (where S and T being the two sets of vertex) - instead look for the minimum edge and more edges of the same value going from S to the vertex the minimum edge goes to. By doing that, (so I suppose) we will receive a graph containing all the edges which appear in any MST. If this is right, I can simply XOR the edges list with the original graph edges list to find what edges are not in any MST.
Thanks in advance.
Do you add all the edges you find (=those with equal weight)? If so, you will lose some edges:
Consider a pentagon with equal edge costs. You start with 1 node and add the 2 edges to the 2 adjacent nodes. In you next step you would add the 2 edges going from those 2 adjacent nodes to the 2 disconnected nodes and you would be done. However, all edges are of equal cost and they are all valid to be in the MST. The edge between the last 2 nodes is not included by your algorithm but could be part of the MST.
It's even worse. Suppose that last edge is of lower cost. Your algorithm still doesn't include it, yet it's present in every MST. You're adding several edges per step to account for all the possibilities but adding those edges changes the next steps.
The deadline for this project is closing in very quickly and I don't have much time to deal with what it's left. So, instead of looking for the best (and probably more complicated/time consuming) algorithms, I'm looking for the easiest algorithms to implement a few operations on a Graph structure.
The operations I'll need to do is as follows:
List all users in the graph network given a distance X
List all users in the graph network given a distance X and the type of relation
Calculate the shortest path between 2 users on the graph network given a type of relation
Calculate the maximum distance between 2 users on the graph network
Calculate the most distant connected users on the graph network
A few notes about my Graph implementation:
The edge node has 2 properties, one is of type char and another int. They represent the type of relation and weight, respectively.
The Graph is implemented with linked lists, for both the vertices and edges. I mean, each vertex points to the next one and each vertex also points to the head of a different linked list, the edges for that specific vertex.
What I know about what I need to do:
I don't know if this is the easiest as I said above, but for the shortest path between 2 users, I believe the Dijkstra algorithm is what people seem to recommend pretty often so I think I'm going with that.
I've been searching and searching and I'm finding it hard to implement this algorithm, does anyone know of any tutorial or something easy to understand so I can implement this algorithm myself? If possible, with C source code examples, it would help a lot. I see many examples with math notations but that just confuses me even more.
Do you think it would help if I "converted" the graph to an adjacency matrix to represent the links weight and relation type? Would it be easier to perform the algorithm on that instead of the linked lists? I could easily implement a function to do that conversion when needed. I'm saying this because I got the feeling it would be easier after reading a couple of pages about the subject, but I could be wrong.
I don't have any ideas about the other 4 operations, suggestions?
List all users in the graph network given a distance X
A distance X from what? from a starting node or a distance X between themselves? Can you give an example? This may or may not be as simple as doing a BF search or running Dijkstra.
Assuming you start at a certain node and want to list all nodes that have distances X to the starting node, just run BFS from the starting node. When you are about to insert a new node in the queue, check if the distance from the starting node to the node you want to insert the new node from + the weight of the edge from the node you want to insert the new node from to the new node is <= X. If it's strictly lower, insert the new node and if it is equal just print the new node (and only insert it if you can also have 0 as an edge weight).
List all users in the graph network given a distance X and the type of relation
See above. Just factor in the type of relation into the BFS: if the type of the parent is different than that of the node you are trying to insert into the queue, don't insert it.
Calculate the shortest path between 2 users on the graph network given a type of relation
The algorithm depends on a number of factors:
How often will you need to calculate this?
How many nodes do you have?
Since you want easy, the easiest are Roy-Floyd and Dijkstra's.
Using Roy-Floyd is cubic in the number of nodes, so inefficient. Only use this if you can afford to run it once and then answer each query in O(1). Use this if you can afford to keep an adjacency matrix in memory.
Dijkstra's is quadratic in the number of nodes if you want to keep it simple, but you'll have to run it each time you want to calculate the distance between two nodes. If you want to use Dijkstra's, use an adjacency list.
Here are C implementations: Roy-Floyd and Dijkstra_1, Dijkstra_2. You can find a lot on google with "<algorithm name> c implementation".
Edit: Roy-Floyd is out of the question for 18 000 nodes, as is an adjacency matrix. It would take way too much time to build and way too much memory. Your best bet is to either use Dijkstra's algorithm for each query, but preferably implementing Dijkstra using a heap - in the links I provided, use a heap to find the minimum. If you run the classical Dijkstra on each query, that could also take a very long time.
Another option is to use the Bellman-Ford algorithm on each query, which will give you O(Nodes*Edges) runtime per query. However, this is a big overestimate IF you don't implement it as Wikipedia tells you to. Instead, use a queue similar to the one used in BFS. Whenever a node updates its distance from the source, insert that node back into the queue. This will be very fast in practice, and will also work for negative weights. I suggest you use either this or the Dijkstra with heap, since classical Dijkstra might take a long time on 18 000 nodes.
Calculate the maximum distance between 2 users on the graph network
The simplest way is to use backtracking: try all possibilities and keep the longest path found. This is NP-complete, so polynomial solutions don't exist.
This is really bad if you have 18 000 nodes, I don't know any algorithm (simple or otherwise) that will work reasonably fast for so many nodes. Consider approximating it using greedy algorithms. Or maybe your graph has certain properties that you could take advantage of. For example, is it a DAG (Directed Acyclic Graph)?
Calculate the most distant connected users on the graph network
Meaning you want to find the diameter of the graph. The simplest way to do this is to find the distances between each two nodes (all pairs shortest paths - either run Roy-Floyd or Dijkstra between each two nodes and pick the two with the maximum distance).
Again, this is very hard to do fast with your number of nodes and edges. I'm afraid you're out of luck on these last two questions, unless your graph has special properties that can be exploited.
Do you think it would help if I "converted" the graph to an adjacency matrix to represent the links weight and relation type? Would it be easier to perform the algorithm on that instead of the linked lists? I could easily implement a function to do that conversion when needed. I'm saying this because I got the feeling it would be easier after reading a couple of pages about the subject, but I could be wrong.
No, adjacency matrix and Roy-Floyd are a very bad idea unless your application targets supercomputers.
This assumes O(E log V) is an acceptable running time, if you're doing something online, this might not be, and it would require some higher powered machinery.
List all users in the graph network given a distance X
Djikstra's algorithm is good for this, for one time use. You can save the result for future use, with a linear scan through all the vertices (or better yet, sort and binary search).
List all users in the graph network given a distance X and the type of relation
Might be nearly the same as above -- just use some function where the weight would be infinity if it is not of the correct relation.
Calculate the shortest path between 2 users on the graph network given a type of relation
Same as above, essentially, just determine early if you match the two users. (Alternatively, you can "meet in the middle", and terminate early if you find someone on both shortest path spanning tree)
Calculate the maximum distance between 2 users on the graph network
Longest path is an NP-complete problem.
Calculate the most distant connected users on the graph network
This is the diameter of the graph, which you can read about on Math World.
As for the adjacency list vs adjacency matrix question, it depends on how densely populated your graph is. Also, if you want to cache results, then the matrix might be the way to go.
The simplest algorithm to compute shortest path between two nodes is Floyd-Warshall. It's just triple-nested for loops; that's it.
It computes ALL-pairs shortest path in O(N^3), so it may do more work than necessary, and will take a while if N is huge.