更快的次优MST算法?更快、算法、MST

由网友(罚伱噯我①生)分享简介:我挣扎与此有关。 我们可以用生成树Kruskal算法和Prim算法的MST得到的。We can get MST using Kruskal's algorithm or Prim's algorithm for the MST.而对于次优MST,我可以:And for "second-best" MST, I...

我挣扎与此有关。

我们可以用生成树Kruskal算法和Prim算法的MST得到的。

We can get MST using Kruskal's algorithm or Prim's algorithm for the MST.

而对于次优MST,我可以:

And for "second-best" MST, I can:

在第一个GET MST使用上面提及的任何算法。 对于每一个V-1从MST的最佳边缘: 一个。首先删除或者标记边缘 湾继续计算MST没有这种 边缘 C。比较和记录下来的次优MST与 previous迭代 在最后,我们有次优MST first get MST using either of the algorithm mentioned above. For each V-1 of the optimal edge from the MST: a. first remove or flag the edge b. continue calculating MST without that edge c. compare and record down that "second-best" MST with previous iteration In the end we have "second-best" MST

但这个运行在O(VE),其中V是NUM顶点,E是边数。

But this runs in O(VE) where V is num of vertex and E is number of edges.

使用联盟找到不相交集或LCA(最低的共同祖先)?怎样才能获得加速

How can get a speed up using Union-find disjoint set or LCA(lowest common ancester) ?

提示,pseodo code或网页链接指针。

hints, pseodo code, or web links pointers.

任何帮助将是非常美联社preciated!感谢:)

Any help would be highly appreciated! Thanks:)

推荐答案

V 是顶点集和电子是边集。

T 是使用任何标准算法的MST获得的。

Let T be the MST obtained using any of the standard algorithms.

maxEdgeInPath(U,V) T的唯一路径上的最大优势从顶点 U 顶点 v

对于每个顶点 U T上进行BFS这让maxEdgeInPath(U,X)的所有 X 属于以

For each vertex u perform BFS on T. This gives maxEdgeInPath(u,x) for all x belonging to V-u.

查找边缘(X,Y)这不属于 T ,最大限度地减少 W(X,Y) - W(maxEdgeInPath(X,Y))

Find an edge (x,y) which does not belong to T that minimizes w(x,y) - w(maxEdgeInPath(x,y))

2ndMST重量为 W(T)+ W(X,Y) - maxEdgeInPath(X,Y)

Weight of 2ndMST is W(T) + w(x,y) - maxEdgeInPath(x,y)

这是根据在此链路的所提供的算法。我不知道这是否是正确的,我希望有人会在这里添加一个证明。

This is based on the algorithm provided in this link. I'm not sure if this is correct and I hope someone would add a proof here.

Compexity: 为了计算BST 1顶点需要 O(V + E)= O(V) E = V-1 T 因此,总的时间复杂度为 0(V ^ 2)

Compexity: To compute BST for 1 vertex takes O(V+E) = O(V) as E = V-1 in T Hence overall time complexity is O(V^2)

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