169_mejorityElement.md

169. Majority Element (Easy)

O(N^2) Brute force

• For every number in array we count its occurrences.
• If count is greater than n/2, then we return the number.

O(N)Time with extra space

• we Can take a vector or unordered_map to store the frequency of each element.
• We traverse the hash map/ vector to find the n/2 frequency.
• if we found we return the element.
• TC: O(N)
• SC:O(N)/O(N^2) - Yes if we use unordered_map it's worst case time complexity is O(N^2), which occurs when all elements are divisible by prime number and result in collision. But if we use frequency vector it's worst case time complexity is O(N).

Moore's Voting Algorithm

• TC:O(N)
• SC:O(1)
• We maintain 2 variables count and candidate.
• We traverse the array and for every element we do following:
  • If count is 0, then we set candidate as current element.
  • If current element is same as candidate, then we increment count by 1.
  • else we decrement count by 1.
• return candidate.

class Solution {
public:
    // moore's voting algorithm
    int majorityElement(vector<int>& nums)
    {
        int count = 0;
        int candidate = 0;
        for (int x : nums) {
            if (count == 0) {
                candidate = x;
            }
            // count += (x == candidate) ? 1 : -1;
            if (x == candidate) count++;
            else count--;
        }
        return candidate;
    }
};