**Question: **Write an efficient program for printing k largest elements in an array. Elements in array can be in any order.

For example, if given array is [1, 23, 12, 9, 30, 2, 50] and you are asked for the largest 3 elements i.e., k = 3 then your program should print 50, 30 and 23.

Method 1 (Use Bubble k times)

Thanks to Shailendra for suggesting this approach.

1) Modify Bubble Sort to run the outer loop at most k times.

2) Print the last k elements of the array obtained in step 1.

Time Complexity: O(nk)

Like Bubble sort, other sorting algorithms like Selection Sort can also be modified to get the k largest elements.

**Method 2 (Use temporary array)**

K largest elements from arr[0..n-1]

1) Store the first k elements in a temporary array temp[0..k-1].

2) Find the smallest element in temp[], let the smallest element be *min*.

3) For each element *x* in arr[k] to arr[n-1]

If *x *is greater than the min then remove *min *from temp[] and insert *x*.

4) Print final k elements of *temp[]*

Time Complexity: O((n-k)*k). If we want the output sorted then O((n-k)*k + klogk)

Thanks to nesamani1822 for suggesting this method.

**Method 3(Use Sorting)**

1) Sort the elements in descending order in O(nLogn)

2) Print the first k numbers of the sorted array O(k).

Following is the implementation of above.

// Java code for k largest elements in an array import java.util.Arrays; import java.util.Collections; class GFG { public static void kLargest(Integer [] arr, int k) { // Sort the given array arr in reverse order // This method doesn't work with primitive data // types. So, instead of int, Integer type // array will be used Arrays.sort(arr, Collections.reverseOrder()); // Print the first kth largest elements for (int i = 0; i < k; i++) System.out.print(arr[i] + " "); } public static void main(String[] args) { Integer arr[] = new Integer[]{1, 23, 12, 9, 30, 2, 50}; int k = 3; kLargest(arr,k); } } // This code is contributed by Kamal Rawal

Output:

50 30 23

Time complexity: O(nlogn)

**Method 4 (Use Max Heap)**

1) Build a Max Heap tree in O(n)

2) Use Extract Max k times to get k maximum elements from the Max Heap O(klogn)

Time complexity: O(n + klogn)

**Method 5(Use Oder Statistics)**

1) Use order statistic algorithm to find the kth largest element. Please see the topic selection in worst-case linear time O(n)

2) Use QuickSort Partition algorithm to partition around the kth largest number O(n).

3) Sort the k-1 elements (elements greater than the kth largest element) O(kLogk). This step is needed only if sorted output is required.

Time complexity: O(n) if we don’t need the sorted output, otherwise O(n+kLogk)

Thanks to Shilpi for suggesting the first two approaches.

**Method 6 (Use Min Heap)**

This method is mainly an optimization of method 1. Instead of using temp[] array, use Min Heap.

Thanks to geek4u for suggesting this method.

1) Build a Min Heap MH of the first k elements (arr[0] to arr[k-1]) of the given array. O(k)

2) For each element, after the kth element (arr[k] to arr[n-1]), compare it with root of MH.

……a) If the element is greater than the root then make it root and call heapify for MH

……b) Else ignore it.

// The step 2 is O((n-k)*logk)

3) Finally, MH has k largest elements and root of the MH is the kth largest element.

Time Complexity: O(k + (n-k)Logk) without sorted output. If sorted output is needed then O(k + (n-k)Logk + kLogk)

All of the above methods can also be used to find the kth largest (or smallest) element.

### Asked in: Amazon,Microsoft,Walmart labs

Please write comments if you find any of the above explanations/algorithms incorrect, or find better ways to solve the same problem.

**References:**

http://en.wikipedia.org/wiki/Selection_algorithm

Asked by geek4u