Graafi läbimise algoritmid: laiuti (breadth first) ja sügavuti (depth first).
BFS is an uninformed search method that aims to expand and examine all nodes of a graph systematically in search of a solution. In other words, it exhaustively searches the entire graph without considering the goal until it finds it. It does not use a heuristic.
From the standpoint of the algorithm, all child nodes obtained by expanding a node are added to a FIFO queue. In typical implementations, nodes that have not yet been examined for their neighbors are placed in some container (such as a queue or linked list) called "open" and then once examined are placed in the container "closed".
1. Put the ending node (the root node) in the queue.
2. Pull a node from the beginning of the queue and examine it.
* If the searched element is found in this node, quit the search and return a result.
3. Otherwise push all the (so-far-unexamined) successors of this node into the end of the queue, if there are any.
4. If the queue is empty, every node on the graph has been examined -- quit the search and return "not found".
5. Repeat from Step 2.
Depth-first search (DFS) is an algorithm for traversing or searching a tree, tree structure, or graph. Intuitively, one starts at the root (selecting some node as the root in the graph case) and explores as far as possible along each branch before backtracking.
Formally, DFS is an uninformed search that progresses by expanding the first child node of the search tree that appears and thus going deeper and deeper until a goal node is found, or until it hits a node that has no children. Then the search backtracks, returning to the most recent node it hadn't finished exploring. In a non-recursive implementation, all freshly expanded nodes are added to a LIFO stack for expansion.
Space complexity of DFS is much lower than BFS (breadth-first search). It also lends itself much better to heuristic methods of choosing a likely-looking branch. Time complexity of both algorithms are proportional to the number of vertices plus the number of edges in the graphs they traverse ( O(|V| + |E|) ) .
When searching large graphs that can not be fully contained in memory, DFS suffers from non-termination when the length of a path in the search tree is infinite. The simple solution of "remember which nodes I have already seen" doesn't always work because there can be insufficient memory. This can be solved by maintaining an increasing limit on the depth of the tree, which is called iterative deepening depth-first search.
For the following graph:
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.
Performing the same search without remembering previously visited nodes results in visiting nodes in the order A, B, D, F, E, A, B, D, F, E, etc. forever, caught in the A, B, D, F, E cycle and never reaching C or G.
Iterative deepening prevents this loop and will reach the following nodes on the following depths, assuming it proceeds left-to-right as above:
* 0: A
* 1: A (repeated), B, C, E
(Note that iterative deepening has now seen C, when a conventional depth-first search did not.)
* 2: A, B, D, F, C, G, E, F
(Note that it still sees C, but that it came later. Also note that it sees E via a different path, and loops back to F twice.)
* 3: A, B, D, F, E, C, G, E, F, B
For this graph, as more depth is added, the two cycles "ABFE" and "AEFB" will simply get longer before the algorithm gives up and tries another branch.