I have two sets of points, A and B, and I'm trying to find the closest pair of points where one point is taken from each set. That is, if you were to use the points two draw to lines, I want the two points that allow me to draw the shortest line segment between the two lines.
Looking around, almost everything seems to deal with finding the closest points in 1 set. Although I did find one solution recommending voronoi tesselation to begin with, which seems a bit like overkill, I'm just looking for something a bit nicer than O(n^2).
If it helps, the two sets I'm comparing form lines, although they are not necessarily straight and I'm writing this in C#.
Thanks.
It should be possible to adapt the classical D&C algorithm (as described in the Wikipedia link), by processing all points together and tagging them with an extra bit.
The merging step needs to be modified to accept candidate left-right pairs with a member from every set only. This way, the recursive function will return the closest A-B pair. The O(N.Log(N)) behavior should be preserved.
If the "lines" you mention have a known equation so that point/line distances (or even line/line intersections) can be evaluated quickly, there could be faster solutions.
Related
I wonder if there's any described algorithm that can convert isochrones into approximate area to show a range of some feature (in my problem this feature is a road network).
Example. I have something like on the image beneath:
It's a simple network (where I can arrive from the start point in X minutes or going Y kilometers). I have information of all the nodes and links. Now I need to create an isochrone map that show an approximate range where I can arrive.
Problems:
Convex hull - sucks because of too general approximation,
I can create buffors on roads - so I will get some polygon that shows range, but I will also have the holes by roads that connect into circles.
What I need to obtain is something like this:
I've found some potentially useful information HERE, but there are only some ideas how it could be done. If anyone has any concept, please, help me to solve my problem.
Interesting problem, to get better answers you might want to define exactly what will this area that shows the range (isochrone map) be used for? For example is it illustrative? If you define what kind of approximation you want it could help you solve the problem.
Now here are some ideas.
1) Find all the cycles in the graph (see link), then eliminate edges that are shared between two cycles. Finally take the convex hull of the remaining cycles, this together with all the roads, so that the outliers that do not form cycles are included, will give a good approximation for an isochrome map.
2) A simpler solution is to define a thickness around each point of every road, this thickness should be inversely proportional to how long it takes to arrive at that point from the starting point. I.e. the longer it takes to arrive at the point the less thick. You can then scale the thickness of all points until all wholes are filled, and then you will have an approximate isochrome map. One possible way of implementing this is to run an algorithm that takes all possible routes simultaneously from the starting point, branching off at every new intersection, while tracking how long it took to arrive at each point. During its execution, at every instant of time all previously discovered route should be thickened. At the end you can scale this thickness so as to fill all wholes.
Hopefully this will be of some help. Good luck.
I have solved the problem (it's not so fast and robust, but has to be enough for now).
I generated my possible routes using A* (A-Star) algorithm.
I used #Artur Gower's idea from point one to eliminate cycles and simplify my geometry.
Later I decided to generate 2 types of gemetries (1st - like on the image, 2nd - simple buffers):
1st one:
3. Then I have removed the rest of unnecessary points using Douglas-Peucker algorithm (very fast!).
4. In the end I used Concave Hull algorithm (aka Alpha-Shapes or Non-Convex Hull).
2nd one:
3. Apply a buffer to the existing geometry and take the exterior ring (JTS library made that really easier:)).
I'm looking for a way to convert a 2D array to the fewest possible rectangles like in this example:
X
12345678
--------
1|00000000
2|00011100
3|00111000
Y 4|00111000
5|00111000
6|00000000
to the corner coordinates of the rectangles:
following the (x1,y1);(x2;y2) template
rectangle #1 (4,2);(6,2)
rectangle #2 (3,3);(5,5)
There has been a similar question here before but unfortunately, the link provided in its answer is broken, and I cannot check it anymore.
I'd like to do this in C# but any kind of help is appreciated.
(It doesn't even have to be the fewest possible rectangles, but the fewer the better :) )
Thanks in advance!
I think that you are trying to cover a set of points in the 2D plane with the minimum required number of rectangles. An answer to Find k rectangles so that they cover the maximum number of points said that this was an NP-complete problem and linked to here (which works for me). A google search finds http://2011.cccg.ca/PDFschedule/papers/paper102.pdf.
There papers agree that rectangle covering is NP-complete but do not actually prove it, and the references for this seem to be unusually elusive - https://cstheory.stackexchange.com/questions/3957/prove-that-the-problem-of-rectilinear-picture-compression-is-np-complete
What I take from these documents is this:
It is unlikely that there is an affordable way of getting the absolutely best answer for large problems, so you might have to either spend a lot of time to get exact answers for problems that are in some sense small, by exhausting over all possible alternatives or perhaps using something like branch and bound, or settle for affordable methods - like greedy search, or beam search, or limited discrepancy search - which are not guaranteed to give you the absolutely best answer.
In this case there seem to be more restricted versions of this problem which are not NP-complete. You might possibly read a paper and find that there is some detail of your problem that means that this method applies to you. One example is "AN ALGORITHM FOR CONSTRUCTING REGIONS WITH RECTANGLES:
INDEPENDENCE AND MINIMUM GENERATING SETS
FOR COLLECTIONS OF INTERVALS*" by Franzblau and Kleitman - I found this in the ACM Digital Library, though - I don't know if it is generally accessible. It works for a restricted set of polygons.
This may help you get started. If you convert the binary data to numbers, you get this:
0
28
56
56
56
0
So where ever there are consecutive equal numbers, there is a rectangle.
I know there are quite some questions out there on generating combinations of elements, but I think this one has a certain twist to be worth a new question:
For a pet proejct of mine I've to pre-compute a lot of state to improve the runtime behavior of the application later. One of the steps I struggle with is this:
Given N tuples of two integers (lets call them points from here on, although they aren't in my use case. They roughly are X/Y related, though) I need to compute all valid combinations for a given rule.
The rule might be something like
"Every point included excludes every other point with the same X coordinate"
"Every point included excludes every other point with an odd X coordinate"
I hope and expect that this fact leads to an improvement in the selection process, but my math skills are just being resurrected as I type and I'm unable to come up with an elegant algorithm.
The set of points (N) starts small, but outgrows 64 soon (for the "use long as bitmask" solutions)
I'm doing this in C#, but solutions in any language should be fine if it explains the underlying idea
Thanks.
Update in response to Vlad's answer:
Maybe my idea to generalize the question was a bad one. My rules above were invented on the fly and just placeholders. One realistic rule would look like this:
"Every point included excludes every other point in the triagle above the chosen point"
By that rule and by choosing (2,1) I'd exclude
(2,2) - directly above
(1,3) (2,3) (3,3) - next line
and so on
So the rules are fixed, not general. They are unfortunately more complex than the X/Y samples I initially gave.
How about "the x coordinate of every point included is the exact sum of some subset of the y coordinates of the other included points". If you can come up with a fast algorithm for that simply-stated constraint problem then you will become very famous indeed.
My point being that the problem as stated is so vague as to admit NP-complete or NP-hard problems. Constraint optimization problems are incredibly hard; if you cannot put extremely tight bounds on the problem then it very rapidly becomes not analyzable by machines in polynomial time.
For some special rule types your task seems to be simple. For example, for your example rule #1 you need to choose a subset of all possible values of X, and than for each value from the subset assign an arbitrary Y.
For generic rules I doubt that it's possible to build an efficient algorithm without any AI.
My understanding of the problem is: Given a method bool property( Point x ) const, find all points the set for which property() is true. Is that reasonable?
The brute-force approach is to run all the points through property(), and store the ones which return true. The time complexity of this would be O( N ) where (a) N is the total number of points, and (b) the property() method is O( 1 ). I guess you are looking for improvements from O( N ). Is that right?
For certain kind of properties, it is possible to improve from O( N ) provided suitable data structure is used to store the points and suitable pre-computation (e.g. sorting) is done. However, this may not be true for any arbitrary property.
I have a directed graph with weighted edges (weights are all positive).
Now, I'm looking for an efficient algorithm or code (specifically, C#) to find the longest path between two given vertices.
This is exactly equivalent to a shortest-path algorithm with all negative weights. To do that, you need to verify that there are no negative-weight cycles (which in your original case is probably equivalent to verifying no positive-weight cycles). Best bet is to take the additive inverse of the weights and run Bellman-Ford, then take the additive inverse of the result.
David Berger's answer is correct, unless you mean a simple path, where each vertex can occur at most once, in which case Bellman-Ford will not give the longest path. Since you say the weights are positive, it's not possible for a longest path to exist when the graph has a cycle (reachable from the source), unless you mean simple path. The longest simple path problem is NP-complete. See Wikipedia.
So, let's assume you mean a directed acyclic graph (DAG). In linear time, you can compute the longest path to each vertex v from the start vertex s, given that you know the longest path from s->*u for each u where u->v directly. This is easy - you can do a depth first search on your directed graph and compute the longest path for the vertices in reverse order of visiting them. You can also detect back edges whole you DFS using a 3-color marking (opened but not finished vertices are gray). See Wikipedia again for more information. Longest/shortest path finding on a DAG is sometimes called the Viterbi algorithm (even though it was given assuming a specific type of DAG).
I'd attempt the linear time dynamic programming solution first. If you do have cycles, then Bellman-Ford won't solve your problem anyway.
Please refer to the QuickGraph project as it provides .NET data structures implementing graphs, and also provides algorithms to operate on such data structures. I'm certain the algorithm you are looking for is implemented in the library.
Just in case it helps anyone, as I was looking for this for a while but couldn't find it, I used QuickGraph to solve a problem where I had to find the longest path that also complies with a certain rule. It is not very elegant as I did it a bit on brute force once I get the first result, but here it is.
https://github.com/ndsrf/random/blob/master/LongestSkiPath/LongestSkiPath/SkiingResolver.cs#L129-L161
To get the longest path you use an algorithm to find the shortest with lenghts = -1. And then to find subsequent longest paths I start removing edges from that longest path to see if I manage to get a "better" (based on the conditions of the problem) longest path.
I found an interesting pairs matching game at http://xepthu.uhm.vn. The rule is simple, you have to find and connect two identical pokemon but the path between them is not blocked and the direction can't not be changed 3 times. Let's see an example:
I've think alot about the algorithm to check if the path between any 2 selected pokemon is valid but because I'm a newbie so I can't find any solution. Can you suggest me one in C#?
This is basically a path finding problem from graph theory. The fields in the grid are the nodes, and all adjacent fields are connected by an edge.
Path finding is a well-known problem, and there are many algorithms that solve this. Since your graph is quite small, the best solution here is probably just a brute force algorithm. A popular path finding algorithm is Dijkstra's algorithm.
Brute force: Start at some pokemon and explore all possible ways to see if one leads to an identical pokemon. You can stop exploring a way if the way is blocked or has more than 2 turns.
You'll need some "pointer" pointing to a field in the grid. The pointer can move left, right, up or down, provided that the field in that direction is not blocked. Move the pointer to an adjacent field. Remember where you came from and how many turns you made. Repeat this until you've reached your destination. Backtrack if the number of turns reaches 3. Make sure you don't run in circles.
Take a look at planar graphs. You will have to introduce a second condition: no more than four nodes may be traversed (start node - first direction change - second direction change - end node).