#167. Two Sum II - Input Array Is Sorted
- Solved
Description
Given a 1-indexed array of integers numbers
that is already sorted in non-decreasing order, find two numbers such that they add up to a specific target
number. Let these two numbers be numbers[index1]
and numbers[index2]
where 1 <= index1 < index2 <= numbers.length
.
Return the indices of the two numbers, index1
and index2
, added by one as an integer array [index1, index2]
of length 2.
The tests are generated such that there is exactly one solution. You may not use the same element twice.
Your solution must use only constant extra space.
Example 1:
Input: numbers = [2,7,11,15], target = 9
Output: [1,2]
Explanation: The sum of 2 and 7 is 9. Therefore, index1 = 1, index2 = 2. We return [1, 2].
Example 2:
Input: numbers = [2,3,4], target = 6
Output: [1,3]
Explanation: The sum of 2 and 4 is 6. Therefore index1 = 1, index2 = 3. We return [1, 3].
Example 3:
Input: numbers = [-1,0], target = -1
Output: [1,2]
Explanation: The sum of -1 and 0 is -1. Therefore index1 = 1, index2 = 2. We return [1, 2].
Constraints:
- 2 <= numbers.length <= 3 * 104
- -1000 <= numbers[i] <= 1000
- numbers is sorted in non-decreasing order.
- -1000 <= target <= 1000
- The tests are generated such that there is exactly one solution.
My Solution
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public class Solution {
public int[] TwoSum(int[] numbers, int target) {
for(int i = 0; i < numbers.Length - 1; i++) {
for(int j = i + 1; j < numbers.Length; j++) {
if(numbers[i] + numbers[j] == target) {
return [i + 1, j + 1];
}
}
}
return [];
}
}
Runtime
343 ms / Beats 6.39%
Memory
49.59 MB / Beats 92.65%
Big O Notation
Time complexity: O(n^2)
Space complexity: O(1)
Best Solution
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public class Solution {
public int[] TwoSum(int[] numbers, int target) {
int left = 0;
int right = numbers.Length - 1;
while (left < right) {
int total = numbers[left] + numbers[right];
if (total == target) {
return [left + 1, right + 1];
} else if (total > target) {
right--;
} else {
left++;
}
}
return [];
}
}
Runtime
0 ms / Beats 100%
Memory
49.81 MB / Beats 54.73%
Big O Notation
Time complexity: O(n)
Space complexity: O(1)