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If G is a tree, replacing the queue of the 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. [7]
a depth-first search starting at A, assuming that the left edges in the shown graph are chosen before right edges, and assuming the search remembers previously-visited nodes and will not repeat them (since this is a small graph), will visit the nodes in the following order: A, B, D, F, E, C, G.
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.
The Treiber stack algorithm is a scalable lock-free stack utilizing the fine-grained concurrency primitive compare-and-swap. [1] It is believed that R. Kent Treiber was the first to publish it in his 1986 article "Systems Programming: Coping with Parallelism".
Example applications of the stack search algorithm can be found in the literature: Frederick Jelinek. Fast sequential decoding algorithm using a stack. IBM Journal of Research and Development, pp. 675-685, 1969. Ye-Yi Wang and Alex Waibel. Decoding algorithm in statistical machine translation. Proceedings of the 8th conference on European ...
The simplest example that would illustrate the issue would be a N-star-graph with DFS beginning at the central vertex. The proper DFS algorithm will traverse this graph never exceeding stack depth of 1. The algorithm given in this entry will instantly reach stack depth of N, which is already a direct violation of fundamental properties of DFS.
Beam stack search [1] is a search algorithm that combines chronological backtracking (that is, depth-first search) with beam search and is similar to depth-first beam search. [2] Both search algorithms are anytime algorithms that find good but likely sub-optimal solutions quickly, like beam search, then backtrack and continue to find improved ...
A stack (LIFO queue) will yield a depth-first algorithm. A best-first branch and bound algorithm can be obtained by using a priority queue that sorts nodes on their lower bound. [3] Examples of best-first search algorithms with this premise are Dijkstra's algorithm and its descendant A* search. The depth-first variant is recommended when no ...