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  2. Prim's algorithm - Wikipedia

    en.wikipedia.org/wiki/Prim's_algorithm

    Prim's algorithm has many applications, such as in the generation of this maze, which applies Prim's algorithm to a randomly weighted grid graph. The time complexity of Prim's algorithm depends on the data structures used for the graph and for ordering the edges by weight, which can be done using a priority queue. The following table shows the ...

  3. Kruskal's algorithm - Wikipedia

    en.wikipedia.org/wiki/Kruskal's_algorithm

    For a graph with E edges and V vertices, Kruskal's algorithm can be shown to run in time O(E log E) time, with simple data structures. Here, O expresses the time in big O notation , and log is a logarithm to any base (since inside O -notation logarithms to all bases are equivalent, because they are the same up to a constant factor).

  4. Parallel algorithms for minimum spanning trees - Wikipedia

    en.wikipedia.org/wiki/Parallel_algorithms_for...

    Similarly to Prim's algorithm there are components in Kruskal's approach that can not be parallelised in its classical variant. For example, determining whether or not two vertices are in the same subtree is difficult to parallelise, as two union operations might attempt to join the same subtrees at the same time.

  5. Distributed minimum spanning tree - Wikipedia

    en.wikipedia.org/wiki/Distributed_minimum...

    In this model, each process is modeled as a node of a graph. Each communication channel between two processes is an edge of the graph. Two commonly used algorithms for the classical minimum spanning tree problem are Prim's algorithm and Kruskal's algorithm. However, it is difficult to apply these two algorithms in the distributed message ...

  6. Minimum spanning tree - Wikipedia

    en.wikipedia.org/wiki/Minimum_spanning_tree

    Such a tree can be found with algorithms such as Prim's or Kruskal's after multiplying the edge weights by -1 and solving the MST problem on the new graph. A path in the maximum spanning tree is the widest path in the graph between its two endpoints: among all possible paths, it maximizes the weight of the minimum-weight edge. [21]

  7. Expected linear time MST algorithm - Wikipedia

    en.wikipedia.org/wiki/Expected_linear_time_MST...

    The key insight to the algorithm is a random sampling step which partitions a graph into two subgraphs by randomly selecting edges to include in each subgraph. The algorithm recursively finds the minimum spanning forest of the first subproblem and uses the solution in conjunction with a linear time verification algorithm to discard edges in the graph that cannot be in the minimum spanning tree.

  8. Euclidean minimum spanning tree - Wikipedia

    en.wikipedia.org/wiki/Euclidean_minimum_spanning...

    For points in any dimension, the minimum spanning tree can be constructed in time () by constructing a complete graph with an edge between every pair of points, weighted by Euclidean distance, and then applying a graph minimum spanning tree algorithm such as the Prim–Dijkstra–Jarník algorithm or Borůvka's algorithm on it.

  9. Minimum spanning tree-based segmentation - Wikipedia

    en.wikipedia.org/wiki/Minimum_spanning_tree...

    A minimum spanning tree (MST) is a minimum-weight, cycle-free subset of a graph's edges such that all nodes are connected. In 2004, Felzenszwalb introduced a segmentation method [4] based on Kruskal's MST algorithm. Edges are considered in increasing order of weight; their endpoint pixels are merged into a region if this doesn't cause a cycle ...