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If G is a tree, replacing the queue of this breadth-first search algorithm with a stack will yield a depth-first search algorithm. 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. [10]
Queues provide services in computer science, transport, and operations research where various entities such as data, objects, persons, or events are stored and held to be processed later. In these contexts, the queue performs the function of a buffer. Another usage of queues is in the implementation of breadth-first search.
The following pseudo-code of a 1-D distributed memory BFS [5] was originally designed for IBM BlueGene/L systems, which have a 3D torus network architecture. Because the synchronization is the main extra cost for parallelized BFS, the authors of this paper also developed a scalable all-to-all communication based on point-to-point communications .
A breadth-first search (BFS) is another technique for traversing a finite graph. BFS visits the sibling vertices before visiting the child vertices, and a queue is used in the search process. This algorithm is often used to find the shortest path from one vertex to another.
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]
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.
The algorithm is called lexicographic breadth-first search because the order it produces is an ordering that could also have been produced by a breadth-first search, and because if the ordering is used to index the rows and columns of an adjacency matrix of a graph then the algorithm sorts the rows and columns into lexicographical order.
Initialize a queue to hold a partial solution with none of the variables of the problem assigned. Loop until the queue is empty: Take a node N off the queue. If N represents a single candidate solution x and f(x) < B, then x is the best solution so far. Record it and set B ← f(x). Else, branch on N to produce new nodes N i. For each of these: