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  2. Jump point search - Wikipedia

    en.wikipedia.org/wiki/Jump_point_search

    In computer science, jump point search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning, [1] eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied.

  3. Any-angle path planning - Wikipedia

    en.wikipedia.org/wiki/Any-angle_path_planning

    The advantage is that all optimizations of grid A* like jump point search will apply. A visibility graph with all the grid points can be searched with A* for the optimal solution in 2D space. However, the performance is problematic since the number of edges in a graph with V {\displaystyle V} vertices is O ( V 2 ) {\displaystyle O(V^{2})} .

  4. Template:Graph traversal algorithms - Wikipedia

    en.wikipedia.org/wiki/Template:Graph_traversal...

    No description. Template parameters [Edit template data] Parameter Description Type Status state state controls when a navbox is expanded or collapsed Suggested values autocollapse collapsed expanded plain off Default autocollapse String optional

  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. Iterative deepening A* - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_A*

    Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph.

  7. Hyperparameter optimization - Wikipedia

    en.wikipedia.org/wiki/Hyperparameter_optimization

    Grid search then trains an SVM with each pair (C, γ) in the Cartesian product of these two sets and evaluates their performance on a held-out validation set (or by internal cross-validation on the training set, in which case multiple SVMs are trained per pair). Finally, the grid search algorithm outputs the settings that achieved the highest ...

  8. Lifelong Planning A* - Wikipedia

    en.wikipedia.org/wiki/Lifelong_Planning_A*

    LPA* maintains two estimates of the start distance g*(n) for each node: . g(n), the previously calculated g-value (start distance) as in A*; rhs(n), a lookahead value based on the g-values of the node's predecessors (the minimum of all g(n' ) + d(n' , n), where n' is a predecessor of n and d(x, y) is the cost of the edge connecting x and y)

  9. Five-point stencil - Wikipedia

    en.wikipedia.org/wiki/Five-point_stencil

    An illustration of the five-point stencil in one and two dimensions (top, and bottom, respectively). In numerical analysis, given a square grid in one or two dimensions, the five-point stencil of a point in the grid is a stencil made up of the point itself together with its four "neighbors".