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  2. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    A linear-time algorithm for finding a longest path in a tree was proposed by Edsger Dijkstra around 1960, while a formal proof of this algorithm was published in 2002. [15] Furthermore, a longest path can be computed in polynomial time on weighted trees, on block graphs, on cacti, [16] on bipartite permutation graphs, [17] and on Ptolemaic ...

  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. List of algorithms - Wikipedia

    en.wikipedia.org/wiki/List_of_algorithms

    Edmonds' algorithm (also known as Chu–Liu/Edmonds' algorithm): find maximum or minimum branchings; Euclidean minimum spanning tree: algorithms for computing the minimum spanning tree of a set of points in the plane; Longest path problem: find a simple path of maximum length in a given graph; Minimum spanning tree. Borůvka's algorithm ...

  5. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    Find the path of minimum total length between two given nodes and . We use the fact that, if R {\displaystyle R} is a node on the minimal path from P {\displaystyle P} to Q {\displaystyle Q} , knowledge of the latter implies the knowledge of the minimal path from P {\displaystyle P} to R {\displaystyle R} .

  6. Minimum spanning tree - Wikipedia

    en.wikipedia.org/wiki/Minimum_spanning_tree

    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] Maximum spanning trees find applications in parsing algorithms for natural languages [22] and in training algorithms for conditional random fields.

  7. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    Find the Shortest Path: Use a shortest path algorithm (e.g., Dijkstra's algorithm, Bellman-Ford algorithm) to find the shortest path from the source node to the sink node in the residual graph. Augment the Flow: Find the minimum capacity along the shortest path. Increase the flow on the edges of the shortest path by this minimum capacity.

  8. Pointer jumping - Wikipedia

    en.wikipedia.org/wiki/Pointer_jumping

    Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path.

  9. Widest path problem - Wikipedia

    en.wikipedia.org/wiki/Widest_path_problem

    In this graph, the widest path from Maldon to Feering has bandwidth 29, and passes through Clacton, Tiptree, Harwich, and Blaxhall. In graph algorithms, the widest path problem is the problem of finding a path between two designated vertices in a weighted graph, maximizing the weight of the minimum-weight edge in the path.