For an optimization course we are encouraged to think of an algorithm for solving the Travelling Salesman Problem. I have an idea for a solution, and I think it's pretty rad. The steps for the algorithm are listed below. The order of the steps correspond to the order of the images.
Find the center of the points (red dot)
Travel around the center
Remove the largest line
The main sub-problem here is step 2. How does one go about traveling around a center point? Is there a good algorithm to do this?
I have another question about the algorithm's performance. How bad is this algorithm? Can you show me an example where it would give a worst-case answer?
Related
I'm trying to link points in the plane, ie draw a graph, but using only axis-aligned lines. I found the KDTree algorithm
to be quite promising and close to what I need
but it does not make sure the segments are as small as possible.
The result I'm looking for is closer to
I have also read up on
https://en.wikipedia.org/wiki/Delaunay_triangulation
because initially, I thought that would be it;
but it turns out its way off:
- based on circles and triangles
- traces a perimeter
- nodes have multiple connections (>=3)
Can you point me towards an algorithm that already exists?
or can you help me with drafting a new algorithm?
PS: Only 1000-1100 points so efficiency is not super important.
In terms of Goals and Costs, reaching all nodes is the Goal
and the length of all segments is the Cost.
Thanks to MBo, I now know that this is known as 'The Steiner Tree Problem'. This is the subject of a 1992 book (of the same name) demonstrating that it is an NP-hard problem.
This is the Rectilinear variant of that. There are a few approximate algorithms or heuristic algorithms known to (help) solve it.
( HAN, HAN4, LBH, HWAB, HWAD, BEA are listed inside
https://www.sciencedirect.com/science/article/pii/0166218X9390010L )
I haven't found anything yet that a "practitioner" might be able to actually use. Still looking.
It seems like a 'good' way forward is:
Compute edges using Delaunay triangulation.
Label each edge with its length or rectilinear distance.
Find the minimum spanning tree (with algorithms such as Borůvka's, Prim's, or Kruskal's).
Add Steiner points restricted to the Hanan grid.
Move edges to the grid.
Still unclear about that last step. How?
In an effort to dust off my somewhat rusty programming skills I'm trying to solve this graph traversal problem I ran across.
I want to find a path that visits all coordinates (vertices) on a 10x10 grid. There are some movement restrictions like only being able to move 3 steps in either direction (x+/-3 OR y+/-3) or 2 steps diagonally (x+/-2 AND y+/-2). From what I understand these restrictions don't really matter much since it's still just a graph with vertices and edges and I can model this easily enough in my solution.
I got so far as to being able to solve this problem for a 6x6 grid using a "simple" DFS strategy (at least I think that's what I've produced :). But going bigger than that I run into performance problems since the O(n) of my algorithm is kinda crap. 7x7 takes like 45 mins on my computer so 10x10 is just forget-about-it.
I figured out that a 5x5 grid can always be solved so I guess one viable strat would be to divide the 10x10 into 4x5x5. But that doesn't feel like a proper solution and even tho it would solve grids with sides of multiples of 5 I would still not be able to solve 8x8 and 11x11 etc.
So my question here is about what strategies can be applied to optimize for this particular problem?
Your problem is the Hamiltonian path problem, which is NP-complete for arbitrary graphs. This means there is no known efficient algorithm, so trying to solve this for arbitrary graphs will be fairly fruitless.
Instead, use the fact that you're solving it on a grid. You can simply go row-by-row, turning around at the ends.
If you have a limited set of moves you can do on a grid you can also look at knight's tour literature.
I know that when your sweepline encounters three centers of your array, you have to check if there is something called "circle points".
I understand that circle points are the poles of the circle that goes through the other 3 points, but my questions is, which is the efficient way to do this, because what you really want is the center of the circle which is the vertex of three Voronoi poligons, so what came to my head is to find the three mediatrices and the intersection of the three will be the center, but it seems to me that if I do that the algorithm will be mor closely to a brute force algorithm, I hope you could help me with this, thanks in advance
EDIT: I think it's worth saying that I'm working on Julia, and that I've already done two brute force algorithms, one aproximate and one exact
There is a rather good and detailed description of this algorithm in this course book:
https://www.springer.com/gp/book/9783540779735
They explain how efficiency is obtained by adding pointers between the status tree and the parts of the diagram being constructed.
Maybe it can help. I have not implemented the algorithm myself.
I have a final set of tiles in which every edge can have on of four colors.
The task is to find a maximal possible square build from a given set (finite) of this tiles. Tiles can be rotated.
I need to design 3 algorithms for finding a solution for this task. One complete and two aproximations.
Obviously it is my task for Algorithms class so Im not asking about complete solutions (as this would be unfair) but for some directions.
Im already designed a kind of complete algorithm (using backtracking - search for a square of size sqrt(n) - if it could not be found try finding smaller and so on) but I have no idea how to create aproximation algorithms. I think one will be kind of stupid which will find a good answer only in specific cases just to document that it is not a good aproach but still I need one much faster then backtracking and quite good one.
Also is this problem NP-hard one? My backtracking algorithm is exponential one but it doesnt mean that there cannot be a better one...
EDIT: I have complete algorithm with exponential time, could some one give me some hints how to build some kind of aproximation for this problem with polynomial time or something better then exponential?
EDIT2: I have the idea that this problem can be changed to a problem of reducting a graph to square grid graph ( http://mathworld.wolfram.com/GridGraph.html ). Still there is a problem if the tiles can be arranged in such a way to build a grid, but this could be a good point to start. Are there any, for example, greedy or any other aproximation algorithms for reducting graph to square-grid graph?
Suppose your backtracking algorithm constructs k-by-k squares for increasing values of k.
You can extend the backtracking algorithm with heuristics. So instead of choosing the next tile randomly, choose and attach a tile such that the colors of the free tiles "agree with" those on the square. The big problem is to find the "agreement" heuristics. One possible heuristics is to find the least common color on the free tiles and use it.
I'm trying to implement the 0-extension algorithm.
It is used to colour a graph with a number of colours where some nodes already have a colour assigned and where every edge has a distance. The algorithm calculates an assignment of colours so that neighbouring nodes with the same colour have as much distance between them as possible.
I found this paper explaining the algorithm: http://citeseer.ist.psu.edu/viewdoc/download;jsessionid=1FBA2D22588CABDAA8ECF73B41BD3D72?doi=10.1.1.100.8049&rep=rep1&type=pdf
but I don't see how I need to implement it.
I already asked this question on the "theoretical computer science" site, but halfway the discussion we went beyond the site's scope:
https://cstheory.stackexchange.com/questions/6163/explain-0-extension-algorithm
Can anyone explain this algorithm in layman's terms?
I'm planning to make the final code opensource in the jgrapht package.
The objective of 0-extension is to minimize the total weighted cost of edges with different color endpoints rather than to maximize it, so 0-extension is really a clustering problem rather than a coloring problem. I'm generally skeptical that using a clustering algorithm to color would have good results. If you want something with a theoretical guarantee, you could look into approximations to the MAXCUT problem (really a generalization if there are more than two colors), but I suspect that a local-search algorithm would work better in practice.