Sponsored
Sponsored
This approach uses sorting to calculate the h-index. The idea is to sort the array of citations in descending order. Then, find the maximum number h such that there are h papers with at least h citations. This can be efficiently determined by iterating over the sorted array.
Time Complexity: O(n log n) due to sorting, Space Complexity: O(1) since the sorting is in place.
1def hIndex(citations):
2 citations.sort(reverse=True)
3 for i, c in enumerate(citations):
4 if c < i + 1:
5 return i
6 return len(citations)
7
8citations = [3, 0, 6, 1, 5]
9print("H-Index:", hIndex(citations))The Python solution sorts the array in descending order using Python's built-in sort method. It then iterates over the sorted array to find the appropriate h-index value.
Given the constraints where citation counts do not exceed 1000 and the number of papers is at most 5000, a counting sort or bucket sort can be used. This approach involves creating a frequency array to count citations. Then traverse the frequency array to compute the h-index efficiently.
Time Complexity: O(n + m) where n is citationsSize and m is the maximum citation value, Space Complexity: O(m).
1using System;
public class Solution {
public int HIndex(int[] citations) {
int n = citations.Length;
int[] count = new int[n + 1];
foreach (int c in citations) {
if (c >= n) count[n]++;
else count[c]++;
}
int total = 0;
for (int i = n; i >= 0; i--) {
total += count[i];
if (total >= i) return i;
}
return 0;
}
public static void Main(string[] args) {
int[] citations = {3, 0, 6, 1, 5};
Solution sol = new Solution();
Console.WriteLine("H-Index: " + sol.HIndex(citations));
}
}The C# solution aggregates citation frequencies within a fixed-sized array, iterating in reverse to determine the h-index.