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To index the skip list and find the i'th value, traverse the skip list while counting down the widths of each traversed link. Descend a level whenever the upcoming width would be too large. For example, to find the node in the fifth position (Node 5), traverse a link of width 1 at the top level.
In computer science, tree traversal (also known as tree search and walking the tree) is a form of graph traversal and refers to the process of visiting (e.g. retrieving, updating, or deleting) each node in a tree data structure, exactly once. Such traversals are classified by the order in which the nodes are visited.
The first and last nodes of a doubly linked list for all practical applications are immediately accessible (i.e., accessible without traversal, and usually called head and tail) and therefore allow traversal of the list from the beginning or end of the list, respectively: e.g., traversing the list from beginning to end, or from end to beginning, in a search of the list for a node with specific ...
record List { Node firstNode // points to first node of list; null for empty list} Traversal of a singly linked list is simple, beginning at the first node and following each next link until reaching the end: node := list.firstNode while node not null (do something with node.data) node := node.next
A universal traversal sequence is a sequence of instructions comprising a graph traversal for any regular graph with a set number of vertices and for any starting vertex. A probabilistic proof was used by Aleliunas et al. to show that there exists a universal traversal sequence with number of instructions proportional to O ( n 5 ) for any ...
A breadth-first traversal creating its output in the top-down order, corecursively, can be also implemented by starting at the root node, outputting its value, [b] then breadth-first traversing the subtrees – i.e., passing on the whole list of subtrees to the next step (not a single subtree, as in the recursive approach) – at the next step ...
Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking.
Add the walls of the cell to the wall list. While there are walls in the list: Pick a random wall from the list. If only one of the cells that the wall divides is visited, then: Make the wall a passage and mark the unvisited cell as part of the maze. Add the neighboring walls of the cell to the wall list. Remove the wall from the list.