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Breadth-first search starts at a given vertex h, which is at level 0. In the first stage, we go toall the vertices that are at the distance of one edge away. When we go there, we markedas "visited," the vertices adjacent to the start vertex s - these vertices are placed into level 1.In the second stage, we go to all the new vertices we can reach at the distance of two edgesaway from the source vertex h. These new vertices, which are adjacent to level 1 vertex and notpreviously assigned to a level, are placed into level 2. The BFS traversal ends when each vertexhas been finished.The BFS(G, a) algorithm creates a breadth-first search tree with the source vertex, s, as its root.The predecessor or parent of any other vertex in the tree is the vertex from which it was firstdeveloped. For every vertex, v, the parent of v is marked in the variable π[v]. Another variable,d[v], calculated by BFS has the number of tree edges on the way from s tov. The breadth-firstsearch needs a FIFO queue, Q, to store red vertices.Algorithm: Breadth-First Search TraversalBFS(V, E, a)1.2. do color[u] ← BLACK3. d[u] ← infinity4. π[u] ← NIL5. color[s] ← RED ? Source vertex find6. d[a] ← 0 ? Start7. π[a] ← NIL ? Stat8. Q ← {} ? Empty queue Q9. ENQUEUE(Q, a)10 while Q is non-empty11. do u ← DEQUEUE(Q) ? That is, u = head[Q]12.13. do if color[v] ← BLACK ? if color is black you've never seen it before14. then color[v] ← RED15. d[v] ← d[u] + 116. π[v] ← u17. ENQUEUE(Q, v)18. DEQUEUE(Q)19. color[u] ← BLACK
AVL trees are applied into the given situations: There are few insertion & deletion operations Short search time is required Input data is sorted or nearly sorted
Draw trace table and determine the output from the below flowchart using following data (NOTE: input of the word "end" stops program and outputs results of survey): Vehicle = c
One can change a binary tree into its mirror image by traversing it in Postorder is the only proecess whcih can convert binary tree into its mirror image.
In worst case Quick Sort has order O (n 2 /2)
Explain an efficient way of storing a sparse matrix in memory. A matrix in which number of zero entries are much higher than the number of non zero entries is called sparse mat
Q. Construct a binary tree whose nodes in inorder and preorder are written as follows: Inorder : 10, 15, 17, 18, 20, 25, 30, 35, 38, 40, 50 Preorder: 20, 15, 10
The two famous methods for traversing are:- a) Depth first traversal b) Breadth first
So far, we now have been concerned only with the representation of single stack. What happens while a data representation is required for several stacks? Let us consider an array X
How sparse matrix stored in the memory of a computer?
The time required to delete a node x from a doubly linked list having n nodes is O (1)
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