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  2. Kruskal's algorithm - Wikipedia

    en.wikipedia.org/wiki/Kruskal's_algorithm

    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 ]

  3. 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.

  4. Distributed minimum spanning tree - Wikipedia

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

    For example, Kruskal's algorithm processes edges in turn, deciding whether to include the edge in the MST based on whether it would form a cycle with all previously chosen edges. Both Prim's algorithm and Kruskal's algorithm require processes to know the state of the whole graph, which is very difficult to discover in the message-passing model.

  5. Prim's algorithm - Wikipedia

    en.wikipedia.org/wiki/Prim's_algorithm

    A demo for Prim's algorithm based on Euclidean distance. In computer science, Prim's algorithm is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. This means it finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized. The ...

  6. Greedy algorithm - Wikipedia

    en.wikipedia.org/wiki/Greedy_algorithm

    Examples of such greedy algorithms are Kruskal's algorithm and Prim's algorithm for finding minimum spanning trees and the algorithm for finding optimum Huffman trees. Greedy algorithms appear in the network routing as well. Using greedy routing, a message is forwarded to the neighbouring node which is "closest" to the destination.

  7. Reverse-delete algorithm - Wikipedia

    en.wikipedia.org/wiki/Reverse-delete_algorithm

    It is the reverse of Kruskal's algorithm, which is another greedy algorithm to find a minimum spanning tree. Kruskal’s algorithm starts with an empty graph and adds edges while the Reverse-Delete algorithm starts with the original graph and deletes edges from it. The algorithm works as follows: Start with graph G, which contains a list of ...

  8. Kruskal's tree theorem - Wikipedia

    en.wikipedia.org/wiki/Kruskal's_tree_theorem

    The version given here is that proven by Nash-Williams; Kruskal's formulation is somewhat stronger. All trees we consider are finite. Given a tree T with a root, and given vertices v, w, call w a successor of v if the unique path from the root to w contains v, and call w an immediate successor of v if additionally the path from v to w contains no other vertex.

  9. Maze generation algorithm - Wikipedia

    en.wikipedia.org/wiki/Maze_generation_algorithm

    An animation of generating a 30 by 20 maze using Kruskal's algorithm. This algorithm is a randomized version of Kruskal's algorithm. Create a list of all walls, and create a set for each cell, each containing just that one cell. For each wall, in some random order: If the cells divided by this wall belong to distinct sets: Remove the current wall.