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Dijkstra's algorithm (/ ˈ d aɪ k s t r ə z / DYKE-strəz) is an algorithm for finding the shortest paths between nodes in a weighted graph, which may represent, for example, a road network. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later.
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.
Compute dist(u), the shortest-path distance from root v to vertex u in G using Dijkstra's algorithm or Bellman–Ford algorithm. For all non-root vertices u , we can assign to u a parent vertex p u such that p u is connected to u , and that dist( p u ) + edge_dist( p u , u ) = dist( u ).
This field of research is based heavily on Dijkstra's algorithm for finding the shortest path on a weighted graph. Pathfinding is closely related to the shortest path problem , within graph theory , which examines how to identify the path that best meets some criteria (shortest, cheapest, fastest, etc) between two points in a large network.
It asks not only about a shortest path but also about next k−1 shortest paths (which may be longer than the shortest path). A variation of the problem is the loopless k shortest paths. Finding k shortest paths is possible by extending Dijkstra's algorithm or the Bellman-Ford algorithm. [citation needed]
It can be seen as an extension of Dijkstra's algorithm. A* achieves better performance by using heuristics to guide its search. Compared to Dijkstra's algorithm, the A* algorithm only finds the shortest path from a specified source to a specified goal, and not the shortest-path tree from a specified source to all possible goals.
Find the shortest path tree T rooted at node s by running Dijkstra's algorithm (figure C). This tree contains for every vertex u, a shortest path from s to u. Let P 1 be the shortest cost path from s to t (figure B). The edges in T are called tree edges and the remaining edges (the edges missing from figure C) are called non-tree edges.
These algorithms are based on two different principles, either performing a shortest path algorithm such as Dijkstra's algorithm on a visibility graph derived from the obstacles or (in an approach called the continuous Dijkstra method) propagating a wavefront from one of the points until it meets the other.