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Название |
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This Greedy Algorithm will not work in this graph..
(1, 2) weight = 1
(2, 4) weight = 100
(1, 3) weight = 100
(3, 4) weight = 1
(2, 3) weight = 1
where (x, y) represent edge;
and s = 1, t = 4,
Edit This answer is incorrect because I missed the fact that the cycle must contain both s and t.
this finds a simple cycle but not necessary one that contains $$$s$$$ or $$$t$$$
Ah yes. I missed that fact. Sorry.
2 The best that I can think of is a quadratic time algorithm. Run MST algorithm. Then try adding an arbitrary edge to the MST. There will be a cycle for sure. Now, you can use DFS to check the weight of this cycle. Do this for every edge.
Edit This answer is incorrect because I missed the fact that the cycle must contain both s and t.
This does not work. A minimum cycle can need more than one edge which is not in the MST. example graph:
I missed the point that the cycle must contain s and t so my answer is incorrect for your counterexample.
On a side note, I believe it should be correct if we remove that restriction (and assume non-negative weights). Because there is no way we can add more than 1 edge to the MST to create a minimum cycle unless there are negative weights.
that should be right, yes. I believe with negative weights this becomes NP hard and it can be reduced to a hamilton cicle problem (just set every weight to $$$-1$$$, than there is a hamilton circle iff the weight of the minimal circle is $$$-n$$$).
For problem one we can use Menger's theorem which tells us that there are two vertex disjoint path (those form a simply cycle) between $$$s$$$ and $$$t$$$ iff there is no single vertex which seperates them. This is equivalent to they are in the same biconnected component (vertex connectivity) which can be found in $$$\mathcal{O}(n)$$$. Then those two paths can be found with a bfs.
problem two with negative weights is NP hard... best Solution for problem two with positive weights i can think of right now is to use a min cost max flow based approch...
"This is equivalent to they are in the same biconnected component (vertex connectivity) which can be found in O(n)."
May you suggest how i can find a biconnected component consist of vertex s and t.
I think you can just compute biconnected components of the graph (there are well-known algorithms that you can find online). Then check if s and t are in the same biconnected component.
Can't you just find two vertex-disjoint paths with two dfs ?
The first dfs starts from s and ends to t and finds a path.
The second dfs starts from s and ends to t but uses only vertices that first path did not use.
The union of these paths is the cycle you need. Isn't that right and simpler ?
Here Again, This will be counterexample
(1, 2) weight = 1
(2, 4) weight = 100
(1, 3) weight = 100
(3, 4) weight = 1
(2, 3) weight = 1
where (x, y) represent edge;
and s = 1, t = 4,
what if first dfs find path 1-2-3-4??
Opps, I am sorry. Max-flow is used for vertex-disjoint paths. It's not linear though as you want. My bad !
Maybe we could use max-flow algorithm in linear time though, because we just need to iterate the graph twice only. So what if you build a new graph with the "back" edges as well and run the max flow algorithm twice ?
https://en.wikipedia.org/wiki/Suurballe%27s_algorithm ?
I was not aware of that algorithm. I did not read it very carefully but I think it does the same job. Also we should replace dijkstra with simple bfs here.
How can we count the total number of simple cycles in an undirected graph?