screen-shot-2016-10-27-at-10-31-39-amMin-max heaps were introduced in [ASSS86] as an efficient way to support heap operations for both minimum and maximum values. Structurally, the min-max heap levels alternate between min-heap condition and max-heap, and hence evaluates grandchildren/grandparents during insertion or search. Min-max heaps can also be generalized to find the k-th smallest element in O(1) time.

An interesting application is finding the running median of a stream of numbers. [ASSS86] describe a simple extension called min-max-median heap that can find the running median in log-linear time complexity (indexed skip lists can too in amortized time complexity). The following code implements a method for finding the running median on each insertion. The trick is to maintain a min-max and a max-min heap such that either heap has at most one more element than the other.

  public double add(int a) {
    if (minHeap.size() == 0) {
      return a;
    // add new element to appropriate heap
    if (a < minHeap.findMax()) {
    } else {
    int minSize = minHeap.size();
    int maxSize = maxHeap.size();
    // resize heaps to enforce size constraint
    if (maxSize == minSize - 2) {
    } else if (minSize == maxSize - 2) {
    minSize = minHeap.size();
    maxSize = maxHeap.size();
    // calculate median
    if (minSize > maxSize) {
        return minHeap.findMax();
    } else if (maxSize > minSize) {
        return maxHeap.findMin();
    return (minHeap.findMax() + maxHeap.findMin()) / 2.0;

[ASSS86] M. D. Atkinson, J.-R. Sack, N. Santoro, and T. Strothotte. Min-max Heaps and Generalized Priority Queues. Communications of the ACM, Vol. 29 No. 10, 1986.