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  2. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    The single-source shortest path problem, in which we have to find shortest paths from a source vertex v to all other vertices in the graph. The single-destination shortest path problem, in which we have to find shortest paths from all vertices in the directed graph to a single destination vertex v. This can be reduced to the single-source ...

  3. Dijkstra's algorithm - Wikipedia

    en.wikipedia.org/wiki/Dijkstra's_algorithm

    Dijkstra's algorithm finds the shortest path from a given source node to every other node. [7]: 196–206 It can be used to find the shortest path to a specific destination node, by terminating the algorithm after determining the shortest path to the destination node. For example, if the nodes of the graph represent cities, and the costs of ...

  4. Parallel single-source shortest path algorithm - Wikipedia

    en.wikipedia.org/wiki/Parallel_single-source...

    A central problem in algorithmic graph theory is the shortest path problem.One of the generalizations of the shortest path problem is known as the single-source-shortest-paths (SSSP) problem, which consists of finding the shortest paths from a source vertex to all other vertices in the graph.

  5. Topological sorting - Wikipedia

    en.wikipedia.org/wiki/Topological_sorting

    The topological ordering can also be used to quickly compute shortest paths through a weighted directed acyclic graph. Let V be the list of vertices in such a graph, in topological order. Then the following algorithm computes the shortest path from some source vertex s to all other vertices: [3]

  6. Bellman–Ford algorithm - Wikipedia

    en.wikipedia.org/wiki/Bellman–Ford_algorithm

    The Bellman–Ford algorithm is an algorithm that computes shortest paths from a single source vertex to all of the other vertices in a weighted digraph. [1] It is slower than Dijkstra's algorithm for the same problem, but more versatile, as it is capable of handling graphs in which some of the edge weights are negative numbers. [2]

  7. Suurballe's algorithm - Wikipedia

    en.wikipedia.org/wiki/Suurballe's_algorithm

    The following example shows how Suurballe's algorithm finds the shortest pair of disjoint paths from A to F. Figure A illustrates a weighted graph G. Figure B calculates the shortest path P 1 from A to F (A–B–D–F). Figure C illustrates the shortest path tree T rooted at A, and the computed distances from A to every vertex (u).

  8. Shortest-path tree - Wikipedia

    en.wikipedia.org/wiki/Shortest-path_tree

    The numbers beside the vertices indicate the distance from the root vertex. In mathematics and computer science, a shortest-path tree rooted at a vertex v of a connected, undirected graph G is a spanning tree T of G, such that the path distance from root v to any other vertex u in T is the shortest path distance from v to u in G.

  9. Yen's algorithm - Wikipedia

    en.wikipedia.org/wiki/Yen's_algorithm

    In graph theory, Yen's algorithm computes single-source K-shortest loopless paths for a graph with non-negative edge cost. [1] The algorithm was published by Jin Y. Yen in 1971 and employs any shortest path algorithm to find the best path, then proceeds to find K − 1 deviations of the best path.