I was asked this question: You are given two lists of integers, each of

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Asked: May 30, 20262026-05-30T05:58:29+00:00 2026-05-30T05:58:29+00:00

I was asked this question: You are given two lists of integers, each of

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I was asked this question:

You are given two lists of integers, each of which is sorted in ascending order and each of which has length n. All integers in the two lists are different. You wish to find the n-th smallest element of the union of the two lists. (That is, if you concatenated the lists and sorted the resulting list in ascending order, the element which would be at the n-th position.)

My Solution:
Assume that lists are 0-indexed.

O(n) solution:
A straight-forward solution is to observe that the arrays are already sorted,so we can merge them, and stop after n steps. The first n-1 elements do not need to be copied
into a new array, so this solution takes O(n) time and O(1) memory.

O(log2 n) solution:
The O(log2 n) solution uses alternates binary search on each list. In short, it takes the middle element in the current search interval in the first list (l1[p1]) and searches for it in l2. Since the elements are unique, we will find at most 2 values closest to l1[p1]. Depending on their values relative to l1[p1-1] and l1[p1 + 1] and their indices p21 and p22, we either return the n-th element or recurse: If the sum of any out of the (at most) 3 indices in l1 can be combined with one of the (at most) 2 indices in l2 so that l1[p1′] and l2[p2′] would be right next to each other in the sorted union of the two lists and p1′ + p2′ = n or p1′ + p2′ = n + 1, we return one of the 5 elements. If p1 + p2 > n, we recurse to left half of the search interval in l1, otherwise we recurse to the right interval. This way, for out of the O(log n) possible midpoints in l1 we do an O(log n) binary search in l2. Therefore the running time is O(log2 n).

O(log n) solution:
Assuming the lists l1 and l2 have constant access time to any of their elements, we
can use a modified version of binary search to get an O(log n) solution. The easiest approach is to search for an index p1 in just one of the lists and calculate the corresponding index p2 in the other list so that p1 + p2 = n at all times. (This assumes the lists are indexed from 1.)
First we check for the special case when all elements of one list are smaller than any element in the other list:
If l1[n] < l2[0] return l1[n].
If l2[n] < l1[0] return l2[n].
If we do not find the n-th smallest element after this step, call findNth(1,n) with the approximate pseudocode:

findNth(start,end)
p1 = (start + end)/2
p2 = n-p1
if l1[p1] < l2[p2]:
    if l1[p1 + 1] > l2[p2]:
        return l2[p2]
    else:
        return findNth(p1+1, end)
else:
    if l2[p2 + 1] > l1[p1]:
        return l1[p1]
else:
    return findNth(start,p1-1)

Element l2[p2] is returned when l2[p2] is greater than exactly p1 + p2-1 = n-1 elements
(and therefore is the n-th smallest). l1[p1] is returned under the same but symmetric conditions. If l1[p1] < l2[p2] and l1[p1+1] < l2[p2], the rank of l2[p2] is greater than n, so we need to take more elements from l1 and less from l2. Therefore we search for p1 in the upper half of the previous search interval. On the other hand, if l2[p2] < l1[p1] and l2[p2 + 1] < l1[p1], the rank of l1[p1] is greater than n. Therefore the real p1 will lie in the bottom half of our current search interval.Since we are halving the size of the problem at each call to findNth and we need to do only constant work to halve the problem size, the recurrence for this algorithm is T(n) = T(n/2) +O(1), which has an O(log n)-time solution.

Interviewer continually ask me different approaches for above problem.I have proposed above three approaches.Is they are correct?Is there any other best possible solution for this question? Actually this question asked lot of times so please provide some good stuff about it.