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  2. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    Keys: current:= cameFrom [current] total_path. prepend (current) return total_path // A* finds a path from start to goal. // h is the heuristic function. h(n) estimates the cost to reach goal from node n. function A_Star (start, goal, h) // The set of discovered nodes that may need to be (re-)expanded. // Initially, only the start node is known.

  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. Working directory - Wikipedia

    en.wikipedia.org/wiki/Working_directory

    In the C language, the POSIX function chdir() effects the system call which changes the working directory. [11] Its argument is a text string with a path to the new directory, either absolute or relative to the old one. Where available, it can be called by a process to set its working directory. There are similar functions in other languages.

  5. Pathfinding - Wikipedia

    en.wikipedia.org/wiki/Pathfinding

    Multi-agent pathfinding is to find the paths for multiple agents from their current locations to their target locations without colliding with each other, while at the same time optimizing a cost function, such as the sum of the path lengths of all agents. It is a generalization of pathfinding.

  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. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the ...

  7. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    Apply dynamic programming to this path decomposition to find a longest path in time (!), where is the number of vertices in the graph. Since the output path has length at least as large as d {\displaystyle d} , the running time is also bounded by O ( ℓ ! 2 ℓ n ) {\displaystyle O(\ell !2^{\ell }n)} , where ℓ {\displaystyle \ell } is the ...

  8. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})

  9. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    Shortest path (A, C, E, D, F), blue, between vertices A and F in the weighted directed graph. In graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized.