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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.
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Q. Write down an algorithm to sort a given list by making use of Quick sort method. Describe the behaviour of Quick sort when input given to us is already sorted.
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