Dynamic order statistic: get k-th element in constant time?

Question

So, I'm trying to implement a Data Structure to handle Dynamic Order Statistic. The Data Structure has following operations:

• add(x): inserts a new element with value x
• get(k): returns the k-th smallest element: k = ceiling(n/a), where n = amount of elements in the data structure and a = constant factor.
• reset: resets the whole datastructuer, i.e. the data structure is "empty after it

I implemented my data structure using a balanced AVL tree. Using this the operations have following time complexity:

• add(x): O(log(n))
• get(k): O(log(n))

Here is my implemenation for the get(k) which uses O(log(n)) time:

public static int get(Node current, int k) {
    int l = tree.sizeLeft(current) + 1;
    if(k == l) {
        return current.value;
    } else if(k < l) {
        if(current.left == null) {
            return current.value;
        }
        return get(current.left, k);
    } else {
        if(current.right == null) {
            return current.value;
        }
        return get(current.right, k);
    }
}

And here's my implementation for the node class:

class Node {
int height, value, bal, size;   // bal = balanceFactor, size = amount of nodes in tree 
                                   rooted at current node
Node leftChild = null;
Node rightChild = null;

public Node(int val) {
    value = val;
    height = 1;
    size = 1; 
}

}

However, my task is to implement a data structure that can handle the above operations and only taking O(1) (constant) time for the operation get(k). (And add(x) still taking O(log(n)) time). Also, I'm not allowed to use a hashmap.

Is it possible to modify my implementation in order to get constant time? Or, what kind of datastructure can handle the get(k) operation in constant time?

Answer 1

As far as I understand the k parameter basically grows with the size of the elements, which means that for each n you know the exact value of k.

If this is the case, then my suggestion is to use a max-heap and and a min-heap. The max-heap organizes elements (smallerequals than the n/a th element) in a heap structure, allowing to access the largest element (root) in constant time. Accordingly the min-heap organizes elements (larger than the n/a th element) in a heap structure, allowing to access the smallest element (root) in constant time.

When new elements arrive (add) you place them in the corresponding heap in O(log n). If the max-heap becomes larger or smaller than (n/a), you rebalance between the two heaps in O(log n)

Your get() function now just needs to return the root element of the max-heap in O(1).

In Java you can use a priority queue for the max-heap (and min-heap)

PriorityQueue<Integer> heap = new PriorityQueue<>(10, Collections.reverseOrder());

The class could look like this

import java.util.Collections;
import java.util.PriorityQueue;

public class DOS
{

    double a;
    PriorityQueue<Integer> heap;
    PriorityQueue<Integer> heap_large_elements;

    public DOS(double a) {
        this.a = a;
        this.heap = new PriorityQueue<>(10, Collections.reverseOrder());
        this.heap_large_elements = new PriorityQueue<>();
    }

    public void add(int x){
        if(heap.size() == 0 || x < heap.peek())
            heap.add(x); // O(log n/a)
        else
            heap_large_elements.add(x); // O(log n)

        //possible rebalance operations
        int n = heap.size() + heap_large_elements.size();
        if(heap.size() > Math.ceil(n/a)){
            heap_large_elements.add(heap.poll()); //O(log n)
        }else if(heap.size() < Math.ceil(n/a)) {
            heap.add(heap_large_elements.poll()); //O(log n)
        }
    }

    public int get(){
        return heap.peek(); //O(1)
    }

    public static void main(String[] args)
    {
        DOS d = new DOS(3);
        d.add(5);d.add(6);d.add(2);d.add(3);d.add(8);d.add(12);d.add(9);
        System.out.println(d.get());
    }

}

Edit (by Cheaty McCheatFace):

Another idea that lets you use your code but is somewhat cheaty, is the following. Whenever, you add an element to your AVL-Tree you calculate the k (=n/a) largest element (as done in your code) and store it. This way, the add()-function still has a O(log n) runtime. The get()-function just retrives the stored value and is in O(1).