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Q. How do we represent a max-heap sequentially? Explain by taking a valid example.
Ans: A max heap is also called as a descending heap, of size n is an almost complete binary tree of n nodes such as the content of each node is less than or equal to the content or matter of its father. If sequential representation of an almost whole binary tree is used, this condition reduces to the condition of inequality. Info[j] <= info[(j-1)/2] for 0 <= ((j-1)/2) < j <= n-1 It is very much clear from this definition that root of the tree contains the largest element in the heap in the descending heap. Any of the paths from the root to a leaf is an ordered list in the descending order.
Ans:
A max heap is also called as a descending heap, of size n is an almost complete binary tree of n nodes such as the content of each node is less than or equal to the content or matter of its father. If sequential representation of an almost whole binary tree is used, this condition reduces to the condition of inequality.
Info[j] <= info[(j-1)/2] for 0 <= ((j-1)/2) < j <= n-1
It is very much clear from this definition that root of the tree contains the largest element in the heap in the descending heap. Any of the paths from the root to a leaf is an ordered list in the descending order.
Using the cohen sutherland. Algorithm. Find the visible portion of the line P(40,80) Q(120,30) inside the window is defined as ABCD A(20,20),B(60,20),C(60,40)and D(20,40)
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Q. What do you mean by the best case complexity of quick sort and outline why it is so. How would its worst case behaviour arise?
Q. Explain the result of inserting the keys given. F, S, Q, K, C, L, H, T, V, W, M, R, N, P, A, B, X, Y, D, Z, E in an order to an empty B-tree of degree-3.
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Post-order Traversal This can be done both iteratively and recursively. The iterative solution would need a change of the in-order traversal algorithm.
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