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Kruskal's algorithm [1] finds a minimum spanning forest of an undirected edge-weighted graph.If the graph is connected, it finds a minimum spanning tree.It is a greedy algorithm that in each step adds to the forest the lowest-weight edge that will not form a cycle. [2]
A planar graph and its minimum spanning tree. Each edge is labeled with its weight, which here is roughly proportional to its length. A minimum spanning tree (MST) or minimum weight spanning tree is a subset of the edges of a connected, edge-weighted undirected graph that connects all the vertices together, without any cycles and with the minimum possible total edge weight. [1]
These algorithms find the minimum spanning forest in a possibly disconnected graph; in contrast, the most basic form of Prim's algorithm only finds minimum spanning trees in connected graphs. However, running Prim's algorithm separately for each connected component of the graph, it can also be used to find the minimum spanning forest. [9]
The Aldous-Broder algorithm also produces uniform spanning trees. However, it is one of the least efficient maze algorithms. [2] Pick a random cell as the current cell and mark it as visited. While there are unvisited cells: Pick a random neighbour. If the chosen neighbour has not been visited:
By explicitly constructing the complete graph on n vertices, which has n(n-1)/2 edges, a rectilinear minimum spanning tree can be found using existing algorithms for finding a minimum spanning tree. In particular, using Prim's algorithm with an adjacency matrix yields time complexity O(n 2).
The shortest-path tree from this point to all vertices in the graph is a minimum-diameter spanning tree of the graph. [2] The absolute 1-center problem was introduced long before the first study of the minimum-diameter spanning tree problem, [ 2 ] [ 3 ] and in a graph with n {\displaystyle n} vertices and m {\displaystyle m} edges it can be ...
In an undirected graph G(V, E) and a function w : E → R, let S be the set of all spanning trees T i. Let B(T i) be the maximum weight edge for any spanning tree T i. We define subset of minimum bottleneck spanning trees S′ such that for every T j ∈ S′ and T k ∈ S we have B(T j) ≤ B(T k) for all i and k. [2]
A faster randomized minimum spanning tree algorithm based in part on Borůvka's algorithm due to Karger, Klein, and Tarjan runs in expected O(E) time. [9] The best known (deterministic) minimum spanning tree algorithm by Bernard Chazelle is also based in part on Borůvka's and runs in O( E α( E , V )) time, where α is the inverse Ackermann ...