Day 5 - K-th element of two Arrays.md

Difficulty	Source	Tags
Medium	160 Days of Problem Solving	Arrays Divide and Conquer Searching

🚀 Day 5. K-th element of two Arrays 🧠

The problem can be found at the following link: Question Link

💡 Problem Description:

Given two sorted arrays a[] and b[], along with an integer k, the task is to find the element that would appear at the k-th position in the combined sorted array of a[] and b[].

🔍 Example Walkthrough:

Input:

a[] = [2, 3, 6, 7, 9], b[] = [1, 4, 8, 10], k = 5

Output:

6

Explanation: The combined sorted array would be [1, 2, 3, 4, 6, 7, 8, 9, 10]. The 5th element is 6.

Input:

a[] = [100, 112, 256, 349, 770], b[] = [72, 86, 113, 119, 265, 445, 892], k = 7

Output:

256

Explanation: The combined sorted array is [72, 86, 100, 112, 113, 119, 256, 265, 349, 445, 770, 892]. The 7th element is 256.

Constraints:

• $( 1 \leq \text{size of } a, b \leq 10^6 )$
• $( 1 \leq k \leq \text{size of } a + \text{size of } b )$
• $( 0 \leq a[i], b[i] < 10^8 )$

🎯 My Approach:

1. Binary Search on Cuts:

   • The problem can be viewed as finding the correct "cut point" in the arrays a[] and b[] such that the k-th element lies at the boundary between the left and right halves of the merged arrays.
   • Use binary search to adjust the cut point in a[], and calculate the corresponding cut point in b[].

2. Key Observations:

   • The left part of the combined array must consist of the largest k elements from a[] and b[] combined.
   • For the cut to be valid:
     • The largest element on the left part of a[] (cut1) should be ≤ the smallest element on the right part of b[] (cut2).
     • Similarly, the largest element on the left part of b[] (cut2) should be ≤ the smallest element on the right part of a[] (cut1).

3. Binary Search Implementation:

   • Perform binary search on a[]'s possible cut points while ensuring the constraints for valid cuts are satisfied.
   • Return the maximum of the largest elements from both left parts once a valid cut is found.

🕒 Time and Auxiliary Space Complexity

• Expected Time Complexity: O(log(min(a, b))), as the binary search is performed on the smaller array.
• Expected Auxiliary Space Complexity: O(1), as we use only a constant amount of additional space.

📝 Solution Code

Code (C)

int kthElement(int *a, int n, int *b, int m, int k) {
    if (n > m) return kthElement(b, m, a, n, k);

    int low = k > m ? k - m : 0, high = k < n ? k : n;

    while (low <= high) {
        int cut1 = (low + high) / 2;
        int cut2 = k - cut1;

        int l1 = (cut1 > 0) ? a[cut1 - 1] : INT_MIN;
        int l2 = (cut2 > 0) ? b[cut2 - 1] : INT_MIN;
        int r1 = (cut1 < n) ? a[cut1] : INT_MAX;
        int r2 = (cut2 < m) ? b[cut2] : INT_MAX;

        if (l1 <= r2 && l2 <= r1) {
            return l1 > l2 ? l1 : l2;
        } else if (l1 > r2) {
            high = cut1 - 1;
        } else {
            low = cut1 + 1;
        }
    }

    return -1;
}

Code (Cpp)

class Solution {
public:
    int kthElement(vector<int>& a, vector<int>& b, int k) {
        if (a.size() > b.size()) return kthElement(b, a, k);

        int n = a.size(), m = b.size(), low = max(0, k - m), high = min(k, n);

        while (low <= high) {
            int cut1 = (low + high) / 2, cut2 = k - cut1;
            int l1 = cut1 > 0 ? a[cut1 - 1] : INT_MIN;
            int l2 = cut2 > 0 ? b[cut2 - 1] : INT_MIN;
            int r1 = cut1 < n ? a[cut1] : INT_MAX;
            int r2 = cut2 < m ? b[cut2] : INT_MAX;

            if (l1 <= r2 && l2 <= r1) return max(l1, l2);
            (l1 > r2) ? high = cut1 - 1 : low = cut1 + 1;
        }
        return -1;
    }
};

Code (Java)

class Solution {
    public int kthElement(int[] a, int[] b, int k) {
        if (a.length > b.length) return kthElement(b, a, k);

        int n = a.length, m = b.length, low = Math.max(0, k - m), high = Math.min(k, n);

        while (low <= high) {
            int cut1 = (low + high) / 2;
            int cut2 = k - cut1;

            int l1 = (cut1 > 0) ? a[cut1 - 1] : Integer.MIN_VALUE;
            int l2 = (cut2 > 0) ? b[cut2 - 1] : Integer.MIN_VALUE;
            int r1 = (cut1 < n) ? a[cut1] : Integer.MAX_VALUE;
            int r2 = (cut2 < m) ? b[cut2] : Integer.MAX_VALUE;

            if (l1 <= r2 && l2 <= r1) {
                return Math.max(l1, l2);
            } else if (l1 > r2) {
                high = cut1 - 1;
            } else {
                low = cut1 + 1;
            }
        }

        return -1;
    }
}

Code (Python)

class Solution:

    def kthElement(self, a, b, k):
        if len(a) > len(b):
            return self.kthElement(b, a, k)

        n, m = len(a), len(b)
        low, high = max(0, k - m), min(k, n)

        while low <= high:
            cut1 = (low + high) // 2
            cut2 = k - cut1

            l1 = a[cut1 - 1] if cut1 > 0 else float('-inf')
            l2 = b[cut2 - 1] if cut2 > 0 else float('-inf')
            r1 = a[cut1] if cut1 < n else float('inf')
            r2 = b[cut2] if cut2 < m else float('inf')

            if l1 <= r2 and l2 <= r1:
                return max(l1, l2)
            elif l1 > r2:
                high = cut1 - 1
            else:
                low = cut1 + 1

        return -1

🎯 Contribution and Support:

For discussions, questions, or doubts related to this solution, please visit my LinkedIn: Any Questions. Thank you for your input; together, we strive to create a space where learning is a collaborative endeavor.

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