I'm fairly new to heaps and am trying to wrap my head around why min and max heaps are represented as trees when a sorted array appears to both provide min / max properties by default.
And a follow up: what is the advantage of dealing with the complexity of inserting into a heap given an algorithm like quick sort handles sorting very well?
Context: I'm working through CLRS / MIT 6.006 in python and have only seen integer representations of leaf values. Is this more applicable in a language like C where each leaf contains a struct that can't easily be sorted?
Asked By : olingern
Answered By : NP-hard
$\small \texttt{find-min}$ (resp. $\small \texttt{find-max}$), $\small \texttt{delete-min}$ (resp. $\small \texttt{delete-max}$) and $\small \texttt{insert}$ are the three most important operations of a min-heap (resp. max-heap), and they usually have complexity of $\small \mathcal{O}(1)$, $\small \mathcal{O}(\log n)$ and $\small \mathcal{O}(\log n)$ respectively if you implement a min/max-heap by a binary tree.
Now suppose instead you implement a min-heap by a sorted (non-decreasing) array (The case for max-heap is similar). $\small \texttt{find-min}$ and $\small \texttt{delete-min}$ are of $\small \mathcal{O}(1)$ complexity if $\small \texttt{insert}$ is not required in your application, since you can maintain a pointer $\small p$ that always points to the minimum element in your array. When the minimum element is removed, you just need to move $\small p$ one step to the next element in the array.
Dealing with insertion in a sorted array is not trivial. Given a new element $\small e$, we can use binary search to locate its position in the array to insert it. But the point is that if you want to insert it there, you have to move a lot of old elements (can be $\small \mathcal{O}(n)$) around to make a vacancy for the new element to reside. This is quite inefficient for most applications. You may also choose to re-sort the array after an element is inserted, this requires $\small \mathcal{O}(n\log n)$ time however.
The last point, how you implement a data structure really depends on your application. NO single implementation is best for all cases. Analyze your application, find out the most frequent operations, and then decide the appropriate implementation.
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Question Source : http://cs.stackexchange.com/questions/63931
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