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Breadth-first search can be used to solve many problems in graph theory, for example: Copying garbage collection , Cheney's algorithm Finding the shortest path between two nodes u and v , with path length measured by number of edges (an advantage over depth-first search ) [ 14 ]
In the theory of linear programming, a basic feasible solution (BFS) is a solution with a minimal set of non-zero variables. Geometrically, each BFS corresponds to a vertex of the polyhedron of feasible solutions.
Best-first search is a class of search algorithms which explores a graph by expanding the most promising node chosen according to a specified rule.. Judea Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function () which, in general, may depend on the description of n, the description of the goal, the information gathered by the search up to ...
The Lee algorithm is one possible solution for maze routing problems based on breadth-first search. It always gives an optimal solution, if one exists, but is slow and requires considerable memory. It always gives an optimal solution, if one exists, but is slow and requires considerable memory.
Breadth-first search can be used to solve many problems in graph theory, for example: finding all vertices within one connected component; Cheney's algorithm; finding the shortest path between two vertices; testing a graph for bipartiteness; Cuthill–McKee algorithm mesh numbering;
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.
Dijkstra's algorithm, as another example of a uniform-cost search algorithm, can be viewed as a special case of A* where = for all x. [ 12 ] [ 13 ] General depth-first search can be implemented using A* by considering that there is a global counter C initialized with a very large value.
In computer science, the Edmonds–Karp algorithm is an implementation of the Ford–Fulkerson method for computing the maximum flow in a flow network in (| | | |) time. The algorithm was first published by Yefim Dinitz in 1970, [1] [2] and independently published by Jack Edmonds and Richard Karp in 1972. [3]