I recently had this problem on a test: given a set of points m (all on the x-axis) and a set n of lines with endpoints [l, r] (again on the x-axis), find the minimum subset of n such that all points are covered by a line. Prove that your solution always finds the minimum subset.
The algorithm I wrote for it was something to the effect of: (say lines are stored as arrays with the left endpoint in position 0 and the right in position 1)
algorithm coverPoints(set[] m, set[][] n):
chosenLines = []
while m is not empty:
minX = min(m)
bestLine = n[0]
for i=1 to length of n:
if n[i][0] <= minX and n[i][1] > bestLine[1] then
bestLine = n[i]
add bestLine to chosenLines
for i=0 to length of m:
if m[i] <= bestLine[1] then delete m[i] from m
return chosenLines
I'm just not sure if this always finds the minimum solution. It's a simple greedy algorithm so my gut tells me it won't, but one of my friends who is much better than me at this says that for this problem a greedy algorithm like this always finds the minimal solution. For proving mine always finds the minimal solution I did a very hand wavy proof by contradiction where I made an assumption that probably isn't true at all. I forget exactly what I did.
If this isn't a minimal solution, is there a way to do it in less than something like O(n!) time?
Thanks
Your greedy algorithm IS correct. We can prove this by showing that ANY other covering can only be improved by replacing it with the cover produced by your algorithm.
Let C be a valid covering for a given input (not necessarily an optimal one), and let S be the covering according to your algorithm. Now lets inspect the points p1, p2, ... pk, that represent the min points you deal with at each iteration step. The covering C must cover them all as well. Observe that there is no segment in C covering two of these points; otherwise, your algorithm would have chosen this segment! Therefore, |C|>=k. And what is the cost (segments count) in your algorithm? |S|=k.
That completes the proof.
Two notes:
1) Implementation: Initializing bestLine with n[0] is incorrect, since the loop may be unable to improve it, and n[0] does not necessarily cover minX.
2) Actually this problem is a simplified version of the Set Cover problem. While the original is NP-complete, this variation results to be polynomial.
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