1. Two Sum

Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.

You may assume that each input would have exactly one solution, and you may not use the same element twice.

You can return the answer in any order.

 

Example 1:

Input: nums = [2,7,11,15], target = 9
Output: [0,1]
Explanation: Because nums[0] + nums[1] == 9, we return [0, 1].

Example 2:

Input: nums = [3,2,4], target = 6
Output: [1,2]

Example 3:

Input: nums = [3,3], target = 6
Output: [0,1]

 

Constraints:

  • 2 <= nums.length <= 104
  • -109 <= nums[i] <= 109
  • -109 <= target <= 109
  • Only one valid answer exists.

 

Follow-up: Can you come up with an algorithm that is less than O(n2) time complexity?


Approach 01: Hash Map (One Pass)



class Solution {
public:
    vector twoSum(vector& nums, int target) {
        unordered_map numMap;
        for (int i = 0; i < nums.size(); i++) {
            int complement = target - nums[i];
            if (numMap.find(complement) != numMap.end()) {
                return {numMap[complement], i};
            }
            numMap[nums[i]] = i;
        }
        return {}; // Should not reach here
    }
};

Time Complexity:

  • O(n): We traverse the list containing n elements only once. Each lookup in the table costs only O(1) time.

Space Complexity:

  • O(n): The extra space required depends on the number of items stored in the hash table, which stores at most n elements.
class Solution:
    def twoSum(self, nums: List[int], target: int) -> List[int]:
        numMap = {}
        for i, num in enumerate(nums):
            complement = target - num
            if complement in numMap:
                return [numMap[complement], i]
            numMap[num] = i
        return []

Time Complexity:

  • O(n): We traverse the list containing n elements only once. Each lookup in the table costs only O(1) time.

Space Complexity:

  • O(n): The extra space required depends on the number of items stored in the hash table, which stores at most n elements.
class Solution {
    public int[] twoSum(int[] nums, int target) {
        Map numMap = new HashMap<>();
        for (int i = 0; i < nums.length; i++) {
            int complement = target - nums[i];
            if (numMap.containsKey(complement)) {
                return new int[] { numMap.get(complement), i };
            }
            numMap.put(nums[i], i);
        }
        return new int[] {};
    }
}

Time Complexity:

  • O(n): We traverse the list containing n elements only once. Each lookup in the table costs only O(1) time.

Space Complexity:

  • O(n): The extra space required depends on the number of items stored in the hash table, which stores at most n elements.

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