Monotonicity: Find the K-th number in two sorted arrays

Monotonicity is a powerful yet difficult to find characteristics in problem-solving. In this post, we explore a problem where monotonicity isn't obvious and then leverage the monotonicity for binary search.

Given two sorted arrays of integers, $A$ and $B$, find the $K$-th smallest integer from A and B in $O(\min(\log{N}, \log{M}))$ time. $N$ and $M$ are the size of $A$ and $B$ respectively.

Without loss of generality, we can assume A and B are of the same length N. Because both A and B are sorted, we can merge A and B into a sorted array C in $O(N)$ and the answer would be C[K-1]. We can stop when there are K elements in C, so the time complexity is $O(K)$. Can we do better?

Yes. Of those final K numbers merged into C, some are from A, others are from B. If there is an oracle that tells us, i numbers are from A, then we know the answer is max(A[i-1], B[K-i-1]). The approach above essentially enumerates i from 1 to N. Can we do better?

Yes. The insight is that i exhibits monotonicity. Let's say we try any value i, we know how many numbers in B are less than or equal to A[i-1], say j. If i+j < K, we know we should select more than i numbers from A. Otherwise, we know we must select no more than i numbers from A. This monotonicity means we can binary search $i$ in $O(\log{N})$. Are we there?

No. For each i, finding j requires $O(\log{N})$ itself. In total, the time complexity is $O((\log{N})^2)$. We can improve this approach by shrinking the size of the problem. After a check of i, we have knowledge of which part of A and B to look at. For example, if i+j < K, shrink the problem to: Find the K-(i+j)-th number in A[i:N] and B[j:N]. Otherwise, shrink the problem to: Find the K-th number in A[0:i] and B[0:j]. It gets complicated to analyze the exact time complexity. However, it is obvious that it is more than $O(\log{N})$. Can we do better?

Yes. Can we verify i in $O(1)$? Given i, we know we need to pick K-i numbers from B. In order to make sure i is the answer, we must make sure numbers we select from A and B are indeed the smallest K numbers. That translates to the following two conditions.

  1. Remaining numbers in A are larger than or equal to B[K-i-1], i.e., $B[K-i-1] \le A[i]$.
  2. Remaining numbers in B are larger than or equal to A[i-1], i.e., $A[i-1] \le B[K-i]$.

The head of arrow is larger than or equal to the tail. The dashed arrows represent the problem definition that A and B are sorted. The solid arrows represent conditions we check.

Here is the implementation and test in Go.

 1func FindKthNumberInTwoSortedArrays(a, b []int, k int) int {
 2  n, m := len(a), len(b)
 3  if n > m {
 4    n, m = m, n
 5    a, b = b, a
 6  }
 8  // Handle special cases for code readability.
 9  // Case 1. use 0 number from a
10  if k <= m && a[0] >= b[k-1] {
11    return b[k-1]
12  }
13  // Case 2. use 0 numbers from b
14  if k <= n && b[0] >= a[k-1] {
15    return a[k-1]
16  }
18  // Case 3. use some numbers from a and b.
19  low, high := 1, min(k-1, n)
20  for low < high {
21    // use i numbers in a.
22    i := (low + high) / 2
23    j := k - i
24    if j < 0 || (j < m && a[i-1] > b[j]) {
25      high = i - 1
26      continue
27    }
28    if j > m || (i < n && j > 0 && b[j-1] > a[i]) {
29      low = i + 1
30      continue
31    }
32    low = i
33    break
34  }
36  if k-low-1 >= 0 {
37    return max(a[low-1], b[k-low-1])
38  }
39  return a[low-1]

Appendix A. A series of posts on monotonicity.

Appendix B. Online coding problems to practice

Problem 1. 4. Median of Two Sorted Arrays