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It is also possible to use depth-first search to linearly order the vertices of a graph or tree. There are four possible ways of doing this: A preordering is a list of the vertices in the order that they were first visited by the depth-first search algorithm. This is a compact and natural way of describing the progress of the search, as was ...
Randomized depth-first search on a hexagonal grid. The depth-first search algorithm of maze generation is frequently implemented using backtracking. This can be described with a following recursive routine: Given a current cell as a parameter; Mark the current cell as visited; While the current cell has any unvisited neighbour cells
L: Recursively traverse the current node's left subtree. R: Recursively traverse the current node's right subtree. The trace of a traversal is called a sequentialisation of the tree. The traversal trace is a list of each visited node. No one sequentialisation according to pre-, in- or post-order describes the underlying tree uniquely.
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 basic idea of the algorithm is this: a depth-first search (DFS) begins from an arbitrary start node (and subsequent depth-first searches are conducted on any nodes that have not yet been found). As usual with depth-first search, the search visits every node of the graph exactly once, refusing to revisit any node that has already been visited.
Standard examples of single recursion include list traversal, such as in a linear search, or computing the factorial function, while standard examples of multiple recursion include tree traversal, such as in a depth-first search. Single recursion is often much more efficient than multiple recursion, and can generally be replaced by an iterative ...
Heck, you could make the stack explicit in the recursive example by replacing "recursively call DFS(G,w)" with "stack.push(w); tailcall(DFS)" (or "goto top" if you prefer). What's there now is complicating things by changing the algorithm at the same time as it's changing where the stack is.