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  2. Depth-first search - Wikipedia

    en.wikipedia.org/wiki/Depth-first_search

    Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking.

  3. Graph traversal - Wikipedia

    en.wikipedia.org/wiki/Graph_traversal

    A depth-first search (DFS) is an algorithm for traversing a finite graph. DFS visits the child vertices before visiting the sibling vertices; that is, it traverses the depth of any particular path before exploring its breadth. A stack (often the program's call stack via recursion) is generally used when implementing the algorithm.

  4. Tree traversal - Wikipedia

    en.wikipedia.org/wiki/Tree_traversal

    In depth-first search (DFS), the search tree is deepened as much as possible before going to the next sibling. To traverse binary trees with depth-first search, perform the following operations at each node: [3] [4] If the current node is empty then return. Execute the following three operations in a certain order: [5] N: Visit the current node.

  5. Iterative deepening depth-first search - Wikipedia

    en.wikipedia.org/wiki/Iterative_deepening_depth...

    function Depth-Limited-Search-Backward(u, Δ, B, F) is prepend u to B if Δ = 0 then if u in F then return u (Reached the marked node, use it as a relay node) remove the head node of B return null foreach parent of u do μ ← Depth-Limited-Search-Backward(parent, Δ − 1, B, F) if μ null then return μ remove the head node of B return null

  6. Breadth-first search - Wikipedia

    en.wikipedia.org/wiki/Breadth-first_search

    Input: A graph G and a starting vertex root of G. Output: Goal state.The parent links trace the shortest path back to root [9]. 1 procedure BFS(G, root) is 2 let Q be a queue 3 label root as explored 4 Q.enqueue(root) 5 while Q is not empty do 6 v := Q.dequeue() 7 if v is the goal then 8 return v 9 for all edges from v to w in G.adjacentEdges(v) do 10 if w is not labeled as explored then 11 ...

  7. Maze-solving algorithm - Wikipedia

    en.wikipedia.org/wiki/Maze-solving_algorithm

    Robot in a wooden maze. A maze-solving algorithm is an automated method for solving a maze.The random mouse, wall follower, Pledge, and Trémaux's algorithms are designed to be used inside the maze by a traveler with no prior knowledge of the maze, whereas the dead-end filling and shortest path algorithms are designed to be used by a person or computer program that can see the whole maze at once.

  8. Strongly connected component - Wikipedia

    en.wikipedia.org/wiki/Strongly_connected_component

    Several algorithms based on depth-first search compute strongly connected components in linear time.. Kosaraju's algorithm uses two passes of depth-first search. The first, in the original graph, is used to choose the order in which the outer loop of the second depth-first search tests vertices for having been visited already and recursively explores them if not.

  9. Maze generation algorithm - Wikipedia

    en.wikipedia.org/wiki/Maze_generation_algorithm

    We begin the algorithm by initializing the maze with one cell chosen arbitrarily. Then we start at a new cell chosen arbitrarily, and perform a random walk until we reach a cell already in the maze—however, if at any point the random walk reaches its own path, forming a loop, we erase the loop from the path before proceeding.