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  2. 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.

  3. Euclidean shortest path - Wikipedia

    en.wikipedia.org/wiki/Euclidean_shortest_path

    There are many results on computing shortest paths which stays on a polyhedral surface. Given two points s and t, say on the surface of a convex polyhedron, the problem is to compute a shortest path that never leaves the surface and connects s with t. This is a generalization of the problem from 2-dimension but it is much easier than the 3 ...

  4. Path analysis (statistics) - Wikipedia

    en.wikipedia.org/wiki/Path_analysis_(statistics)

    Where residual variances are not explicitly included, or as a more general solution, at any change of direction encountered in a route (except for at two-way arrows), include the variance of the variable at the point of change. That is, in tracing a path from a dependent variable to an independent variable, include the variance of the ...

  5. Pathfinding - Wikipedia

    en.wikipedia.org/wiki/Pathfinding

    Pathfinding or pathing is the search, by a computer application, for the shortest route between two points. It is a more practical variant on solving mazes. This field of research is based heavily on Dijkstra's algorithm for finding the shortest path on a weighted graph.

  6. 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 ...

  7. Global optimization - Wikipedia

    en.wikipedia.org/wiki/Global_optimization

    In this method, random simulations are used to find an approximate solution. Example: The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts (distances between each destination point) needed to determine the optimal path to follow are known with certainty and the goal is to run through the ...

  8. Any-angle path planning - Wikipedia

    en.wikipedia.org/wiki/Any-angle_path_planning

    It references this database to quickly find piece-wise any-angle paths. ANYA [16] - Finds optimal any-angle paths by restricting the search space to the Taut paths (a path where every heading change in the path “wraps” tightly around some obstacle); looking at an interval of points as a node rather than a single point. The fastest online ...

  9. k shortest path routing - Wikipedia

    en.wikipedia.org/wiki/K_shortest_path_routing

    insert path p s = {s} into B with cost 0 while B is not empty and count t < K: – let p u be the shortest cost path in B with cost C – B = B − {p u}, count u = count u + 1 – if u = t then P = P U {p u} – if count u ≤ K then for each vertex v adjacent to u: – let p v be a new path with cost C + w(u, v) formed by concatenating edge ...

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