Min-Max Median Heap: Array vs Priority Queue

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One of the way of getting track of median in real-time: using two heaps (Min & Max heaps)

Min-Max Median Heap

Design of Algorithm:

1. compare value with median
2. if the value is smaller than median, insert it to Max heap,
     else Min heap
3. balance two trees: the difference of size of the node should be under 1,
	 otherwise, pop root from the bigger size heap and push it into smaller size heap.
4. get Median value:
	 if same size of two heaps => median = (minRoot + maxRoot) >> 1,
        more nodes in max heap => median = minRoot,
        else median maxRoot <br>

# Latency comparison

From the graph, median using priority queue shows high latency comapre to that of using array. To program priority queue, I used C++ queue library, and for an array, I made my own implementation, swap node (write operation) every time it compares. So this algorithm could have been optimized as write operation takes more time than read(access) operation.


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