I have to make a efficient algorithm that gets two point that are the most distant from each other and I am trying to get the O(nlogn) complexity.
I searched for an efficient algorithm, but all I could find is for the closest points.
The output should just print the 2 points.
What I found by now is this algorithm from GeeksForGeeks : https://www.geeksforgeeks.org/closest-pair-of-points-using-divide-and-conquer-algorithm/
but is for the closest points.
And this solution:
mukeshiiitm.wordpress.com/2008/05/27/find-the-farthest-pair-of-points/
that looks way to complicated for a homework.
First method, looks weird, plus I don't think it works for my problem.
Any help would be appreciated.
Well this is homework so no code examples :)
Here's O(n log n) approach:
Find Convex Hull of all given points using any of O(n log n) convex hull algorithms
Now we can notice that 2 farthest points will still be on convex hull
We can find such points using Rotating calipers technique in O(n) time
I have an algorithm problem here. It is different from the normal Fermat Point problem.
Given a set of n points in the plane, I need to find which one can minimize the sum of distances to the rest of n-1 points.
Is there any algorithm you know of run less than O(n^2)?
Thank you.
One solution is to assume median is close to the mean and for a subset of points close to the mean exhaustively calculate sum of distances. You can choose klog(n) points closest to the mean, where k is an arbitrarily chosen constant (complexity nlog(n)).
Another possible solution is Delaunay Triangulation. This triangulation is possible in O(nlogn) time. The triangulation results in a graph with one vertex for each point and edges to satisfy delauney triangulation.
Once you have the triangulation, you can start at any point and compare sum-of-distances of that point to its neighbors and keep moving iteratively. You can stop when the current point has the minimum sum-of-distance compared to its neighbors. Intuitively, this will halt at the global optimal point.
I think the underlying assumption here is that you have a dataset of points which you can easily bound, as many algorithms which would be "good enough" in practice may not be rigorous enough for theory and/or may not scale well for arbitrarily large solutions.
A very simple solution which is probably "good enough" is to sort the coordinates on the Y ordinate, then do a stable sort on the X ordinate.
Take the rectangle defined by the min(X,Y) and max(X,Y) values, complexity O(1) as the values will be at known locations in the sorted dataset.
Now, working from the center of your sorted dataset, find coordinate values as close as possible to {Xctr = Xmin + (Xmax - Xmin) / 2, Yctr = Ymin + (Ymax - Ymin) / 2} -- complexity O(N) bounded by your minimization criteria, distance being the familiar radius from {Xctr,Yctr}.
The worst case complexity would be comparing your centroid to every other point, but once you get away from the middle points you will not be improving the global optimal and should terminate the search.
In this problem r is a fixed positive integer. You are given N rectangles, all the same size, in the plane. The sides are either vertical or horizontal. We assume the area of the intersection of all N rectangles has non-zero area. The problem is how to find N-r of these rectangles, so as to maximize the area of the intersection. This problem arises in practical microscopy when one repeatedly images a given biological specimen, and alignment changes slightly during this process, due to physical reasons (e.g. differential expansion of parts of the microscope and camera). I have expressed the problem for dimension d=2. There is a similar problem for each d>0. For d=1, an O(N log(N)) solution is obtained by sorting the lefthand endpoints of the intervals. But let's stick with d=2. If r=1, one can again solve the problem in time O(N log(N)) by sorting coordinates of the corners.
So, is the original problem solved by solving first the case (N,1) obtaining N-1 rectangles, then solving the case (N-1,1), getting N-2 rectangles, and so on, until we reduce to N-r rectangles? I would be interested to see an explicit counter-example to this optimistic attempted procedure. It would be even more interesting if the procedure works (proof please!), but that seems over-optimistic.
If r is fixed at some value r>1, and N is large, is this problem in one of the NP classes?
Thanks for any thoughts about this.
David
Since the intersection of axis-aligned rectangles is an axis-aligned rectangle, there are O(N4) possible intersections (O(N) lefts, O(N) rights, O(N) tops, O(N) bottoms). The obvious O(N5) algorithm is to try all of these, checking for each whether it's contained in at least N - r rectangles.
An improvement to O(N3) is to try all O(N2) intervals in the X dimension and run the 1D algorithm in the Y dimension on those rectangles that contain the given X-interval. (The rectangles need to be sorted only once.)
How large is N? I expect that fancy data structures might lead to an O(N2 log N) algorithm, but it wouldn't be worth your time if a cubic algorithm suffices.
I think I have a counter-example. Let's say you have r := N-2. I.e. you want to find two rectangles with maximum overlapping. Let's say you have to rectangles covering the same area (=maximum overlapping). Those two will be the optimal result in the end.
Now we need to construct some more rectangles, such that at least one of those two get removed in a reduction step.
Let's say we have three rectangles which overlap a lot..but they are not optimal. They have a very small overlapping area with the other two rectangles.
Now if you want to optimize the area for four rectangles, you will remove one of the two optimal rectangles, right? Or maybe you don't HAVE to, but you're not sure which decision is optimal.
So, I think your reduction algorithm is not quite correct. Atm I'm not sure if there is a good algorithm for this or in which complexity class this belongs to, though. If I have time I think about it :)
Postscript. This is pretty defective, but may spark some ideas. It's especially defective where there are outliers in a quadrant that are near the X and Y axes - they will tend to reinforce each other, as if they were both at 45 degrees, pushing the solution away from that quadrant in a way that may not make sense.
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If r is a lot smaller than N, and N is fairly large, consider this:
Find the average center.
Sort the rectangles into 2 sequences by (X - center.x) + (Y - center.y) and (X - center.x) - (Y - center.y), where X and Y are the center of each rectangle.
For any solution, all of the reject rectangles will be members of up to 4 subsequences, each of which is a head or tail of each of the 2 sequences. Assuming N is a lot bigger than r, most the time will be in sorting the sequences - O(n log n).
To find the solution, first find the intersection given by removing the r rectangles at the head and tail of each sequence. Use this base intersection to eliminate consideration of the "core" set of rectangles that you know will be in the solution. This will reduce the intersection computations to just working with up to 4*r + 1 rectangles.
Each of the 4 sequence heads and tails should be associated with an array of r rectangles, each entry representing the intersection given by intersecting the "core" with the i innermost rectangles from the head or tail. This precomputation reduces the complexity of finding the solution from O(r^4) to O(r^3).
This is not perfect, but it should be close.
Defects with a small r will come from should-be-rejects that are at off angles, with alternatives that are slightly better but on one of the 2 axes. The maximum error is probably computable. If this is a concern, use a real area-of-non-intersection computation instead of the simple "X+Y" difference formula I used.
Here is an explicit counter-example (with N=4 and r=2) to the greedy algorithm proposed by the asker.
The maximum intersection between three of these rectangles is between the black, blue, and green rectangles. But, it's clear that the maximum intersection between any two of these three is smaller than intersection between the black and the red rectangles.
I now have an algorithm, pretty similar to Ed Staub's above, with the same time estimates. It's a bit different from Ed's, since it is valid for all r
The counter-example by mhum to the greedy algorithm is neat. Take a look.
I'm still trying to get used to this site. Somehow an earlier answer by me was truncated to two sentences. Thanks to everyone for their contributions, particularly to mhum whose counter-example to the greedy algorithm is satisfying. I now have an answer to my own question. I believe it is as good as possible, but lower bounds on complexity are too difficult for me. My solution is similar to Ed Staub's above and gives the same complexity estimates, but works for any value of r>0.
One of my rectangles is determined by its lower left corner. Let S be the set of lower left corners. In time O(N log(N)) we sort S into Sx according to the sizes of the x-coordinates. We don't care about the order within Sx between two lower left corners with the same x-coord. Similarly the sorted sequence Sy is defined by using the sizes of the y-coords. Now let u1, u2, u3 and u4 be non-negative integers with u1+u2+u3+u4=r. We compute what happens to the area when we remove various rectangles that we now name explicitly. We first remove the u1-sized head of Sx and the u2-sized tail of Sx. Let Syx be the result of removing these u1+u2 entries from Sy. We remove the u3-sized head of Syx and the u4-sized tail of Syx. One can now prove that one of these possible choices of (u1,u2,u3,u4) gives the desired maximal area of intersection. (Email me if you want a pdf of the proof details.) The number of such choices is equal to the number of integer points in the regular tetrahedron in 4-d euclidean space with vertices at the 4 points whose coordinate sum is r and for which 3 of the 4 coordinates are equal to 0. This is bounded by the volume of the tetrahedron, giving a complexity estimate of O(r^3).
So my algorithm has time complexity O(N log(N)) + O(r^3).
I believe this produces a perfect solution.
David's solution is easier to implement, and should be faster in most cases.
This relies on the assumption that for any solution, at least one of the rejects must be a member of the complex hull. Applying this recursively leads to:
Compute a convex hull.
Gather the set of all candidate solutions produced by:
{Remove a hull member, repair the hull} r times
(The hull doesn't really need to be repaired the last time.)
If h is the number of initial hull members, then the complexity is less than
h^r, plus the cost of computing the initial hull. I am assuming that a hull algorithm is chosen such that the sorted data can be kept and reused in the hull repairs.
This is just a thought, but if N is very large, I would probably try a Monte-Carlo algorithm.
The idea would be to generate random points (say, uniformly in the convex hull of all rectangles), and score how each random point performs. If the random point is in N-r or more rectangles, then update the number of hits of each subset of N-r rectangles.
In the end, the N-r rectangle subset with the most random points in it is your answer.
This algorithm has many downsides, the most obvious one being that the result is random and thus not guaranteed to be correct. But as most Monte-Carlo algorithms it scales well, and you should be able to use it with higher dimensions as well.
Given a set of n points, can we find three points that describe a triangle with minimum area in O(n^2)? If yes, how, and if not, can we do better than O(n^3)?
I have found some papers that state that this problem is at least as hard as the problem that asks to find three collinear points (a triangle with area 0). These papers describe an O(n^2) solution to this problem by reducing it to an instance of the 3-sum problem. I couldn't find any solution for what I'm interested in however. See this (look for General Position) for such a paper and more information on 3-sum.
There are O(n2) algorithms for finding the minimum area triangle.
For instance you can find one here: http://www.cs.tufts.edu/comp/163/fall09/CG-lecture9-LA.pdf
If I understood that pdf correctly, the basic idea is as follows:
For each pair of points AB you find the point that is closest to it.
You construct a dual of the points so that lines <-> points.
Line y = mx + c is mapped to point (m,c)
In the dual, for a given point (which corresponds to a segment in original set of points) the nearest line vertically gives us the required point for 1.
Apparently 2 & 3 can be done in O(n2) time.
Also I doubt the papers showed 3SUM-hardness by reducing to 3SUM. It should be the other way round.
There's an algorithm that finds the required area with complexity O(n^2*log(n)).
For each point Pi in set do the following(without loss of generality we can assume that Pi is in the origin or translate the points to make it so).
Then for each points (x1,y1), (x2,y2) the triangle area will be 0.5*|x1*y2-x2*y1| so we need to minimize that value. Instead of iterating through all pairs of remaining points (which gives us O(N^3) complexity) we sort those points using predicate X1 * Y2 < X2 * Y1. It is claimed that to find triangle with minimal area we need to check only the pairs of adjacent points in the sorted array.
So the complexity of this procedure for each point is n*log(n) and the whole algorithm works in O(n^2*log(n))
P.S. Can't quickly find the proof that this algorithm is correct :(, hope will find it it later and post it then.
The problem
Given a set of n points, can we find three points that describe a triangle with minimum area in O(n^2)? If yes, how, and if not, can we do better than O(n^3)
is better resolved in this paper: James King, A Survey of 3sum-Hard Problems, 2004
I have a set of 2D points from which I want to generate a polygon (or collection of polygons) outlining the 'shape' of those points, using the following concept:
For each point in the set, calculate the convex hull of all points within radius R of that point. After doing this for each point, take the union of these convex hulls to produce the final shape.
A brute force approach of actually constructing all these convex hulls is something like O(N^2 + R^2 log R). Is there a known, more efficient algorithm to produce the same result? Or perhaps a different way of expressing the problem?
Note: I am aware of alpha shapes, they are different; I am looking for an algorithm to perform what is described above.
The following solution does not work - disproved experimentally in MATLAB.
Update: I have a proposed solution.
Proposition: take the Delaunay Triangulation of the set of points, remove all triangles having circumradius greater than R. Then take the union of the remaining triangles.
A sweep line algorithm can improve searching for the R-neighbors. Alternatively, you can consider only pairs of points that are in neighboring squares of square grid of width R. Both of these ideas can get rid of the N^2 - of course only if the points are relatively sparse.
I believe that a clever combination of sweeping and convex hull finding cat get rid of the N^2 even if the points are not sparse (as in Olexiy's example), but cannot come up with a concrete algorithm.
Yes, using rotating calipers. My prof wrote some stuff on this, it starts on page 19.
Please let me know if I misunderstood the problem.
I don't see how do you get N^2 time for brute-forcing all convex hulls in the worst case (1). What if almost any 2 points are closer than R - in this case you need at least N^2*logN to just construct the convex hulls, leave alone computing their union.
Also, where does R^2*logR in your estimation comes from?
1 The worst case (as I see it) for a huge N - take a circle of radius R / 2 and randomly place points on its border and just outside it.