Table of Contents
Problem Statement
Valid Triangle Number LeetCode Solution – Given an integer array nums
, return the number of triplets chosen from the array that can make triangles if we take them as side lengths of a triangle.
Input: nums = [2,2,3,4] Output: 3 Explanation: Valid combinations are: 2,3,4 (using the first 2) 2,3,4 (using the second 2) 2,2,3
Explanation
Once we sort the given array, we need to find the right limit of the index k for a pair of indices (i, j) chosen to find the count of elements satisfying nums[i] + nums[j] > nums[k] for the triplet (nums[i], nums[j], nums[k]) to form a valid triangle.
We can find this right limit by simply traversing the index ‘s values starting from the index k=j+1 for a pair (i, j) chosen and stopping at the first value of k not satisfying the above inequality. Again, the count of elements nums[k] satisfying nums[i] + nums[j] > nums[k] for the pair of indices (i, j) chosen is given by k – j – 1 as discussed in the last approach.
Further, as discussed in the last approach, when we choose a higher value of index j for a particular i chosen, we need not start from the index j + 1. Instead, we can start off directly from the value of k where we left for the last index j. This helps to save redundant computations.
Code for Valid Triangle Number LeetCode Solution
Java Code:
public class Solution { public int triangleNumber(int[] nums) { int count = 0; Arrays.sort(nums); for (int i = 0; i < nums.length - 2; i++) { int k = i + 2; for (int j = i + 1; j < nums.length - 1 && nums[i] != 0; j++) { while (k < nums.length && nums[i] + nums[j] > nums[k]) k++; count += k - j - 1; } } return count; } }
C++ Code:
class Solution { public: int triangleNumber(vector<int>& nums) { sort(nums.begin(), nums.end()); int n = nums.size(), ans = 0; for (int k = 2; k < n; ++k) { int i = 0, j = k - 1; while (i < j) { if (nums[i] + nums[j] > nums[k]) { ans += j - i; j -= 1; } else { i += 1; } } } return ans; } };
Complexity Analysis for Valid Triangle Number LeetCode Solution
Time Complexity
O(n^2)
Space Complexity
O(1)