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  2. 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 ...

  3. Iterative deepening depth-first search - Wikipedia

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

    In computer science, iterative deepening search or more specifically iterative deepening depth-first search [1] (IDS or IDDFS) is a state space/graph search strategy in which a depth-limited version of depth-first search is run repeatedly with increasing depth limits until the goal is found.

  4. MTD(f) - Wikipedia

    en.wikipedia.org/wiki/MTD(f)

    MTD(f) is an alpha-beta game tree search algorithm modified to use ‘zero-window’ initial search bounds, and memory (usually a transposition table) to reuse intermediate search results. MTD(f) is a shortened form of MTD(n,f) which stands for Memory-enhanced Test Driver with node ‘n’ and value ‘f’. [ 1 ]

  5. Database object - Wikipedia

    en.wikipedia.org/wiki/Database_object

    Views, a virtual table that is made as it is queried; Synonyms, alternate names for a table, view, sequence or other object in a database; Stored procedures and user-defined functions; Triggers, procedures which are run automatically based on specific events; Constraints, a constraint on the domain of an attribute; User accounts, schemas and ...

  6. Fringe search - Wikipedia

    en.wikipedia.org/wiki/Fringe_search

    In essence, fringe search is a middle ground between A* and the iterative deepening A* variant (IDA*). If g(x) is the cost of the search path from the first node to the current, and h(x) is the heuristic estimate of the cost from the current node to the goal, then ƒ(x) = g(x) + h(x), and h* is the actual path cost to the goal.

  7. Graph traversal - Wikipedia

    en.wikipedia.org/wiki/Graph_traversal

    procedure BFS(G, v) is create a queue Q enqueue v onto Q mark v while Q is not empty do w ← Q.dequeue() if w is what we are looking for then return w for all edges e in G.adjacentEdges(w) do x ← G.adjacentVertex(w, e) if x is not marked then mark x enqueue x onto Q return null

  8. Depth-first search - Wikipedia

    en.wikipedia.org/wiki/Depth-first_search

    For general graphs, replacing the stack of the iterative depth-first search implementation with a queue would also produce a breadth-first search algorithm, although a somewhat nonstandard one. [7] Another possible implementation of iterative depth-first search uses a stack of iterators of the list of neighbors of a node, instead of a stack of ...

  9. Tree traversal - Wikipedia

    en.wikipedia.org/wiki/Tree_traversal

    In computer science, tree traversal (also known as tree search and walking the tree) is a form of graph traversal and refers to the process of visiting (e.g. retrieving, updating, or deleting) each node in a tree data structure, exactly once.