breadth-first search

This last detail is the only difference with the breadth-first search algorithm.

This can be found by a breadth-first search, as we let edges have unit length.

Part (b) shows a breadth-first search tree of the same graph.

Breadth-first search can be used to solve many problems in graph theory, for example:

The breadth-first search algorithm uses a queue to visit cells in increasing distance order from the start until the finish is reached.

This means that if there is a solution breadth-first search will find it regardless of the kind of graph.

A breadth-first search partitions the vertices of the graph into layers.

For even girth 2k, one can similarly form a breadth-first search tree starting from the midpoint of a single edge.

This can be computed by breadth-first search.

We shall now look at ways of reducing the potential combinatorial explosion of breadth-first search.

See Breadth-first search for more information.

A naive breadth-first search of these graphs would quickly consume all the memory of any modern computer.

Alternatively, a similar procedure may be used with breadth-first search in place of depth-first search.

The breadth-first search algorithm is used when the search is only limited to two operations.

Beam search uses breadth-first search to build its search tree.

The Lee algorithm is one possible solution for maze routing problems based on Breadth-first search.

The breadth-first search technique works just as well on such queries, but constructing an efficient oracle is more challenging.

Proceed from that node using either depth-first or breadth-first search, counting all nodes reached.

Breadth-first search is admissible, i.e. guaranteed to find the highest scoring path through the graph.

When a trie is used to sort numbers, the number representations must all be the same length unless you are willing to perform a breadth-first search.

Apriori uses breadth-first search and a Hash tree structure to count candidate item sets efficiently.

Another important algorithm is based on breadth-first search (BFS).

We would expect an anchoring of the path at one end or the other to reduce at least a portion of the breadth-first search.

Thus, simple depth-first or breadth-first searches do not traverse every infinite tree, and are not efficient on very large trees.

The lexicographic breadth-first search algorithm is based on the idea of partition refinement and was first developed by .