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This approach uses binary search to find the position where x would fit in the array or where it is located. From this point, a sliding window is expanded to the left and right to check k closest elements. Given the sorted nature of the array, this is efficient.
Time Complexity: O(log(n-k) + k) where n is the length of the array, and we are moving a window over it. Space Complexity: O(k) for storing the resulting subarray.
1function findClosestElements(arr, k, x) {
2 let left = 0, right = arr.length - k;
3 while (left < right) {
4 const mid = Math.floor((left + right) / 2);
5 if (x - arr[mid] > arr[mid + k] - x)
6 left = mid + 1;
7 else
8 right = mid;
9 }
10 return arr.slice(left, left + k);
11}The JavaScript implementation uses slice to extract the subarray after performing a binary search to identify where the k elements should start from.
This approach initiates two pointers from the start and end of the array and gradually closes inwards by comparing the distance from x from both ends until the closest k elements are left. This is linear after the search initialization.
Time Complexity: O(n). Space Complexity: O(k).
1functionThe JavaScript program uses slice and a two-pointer approach to iterate from both ends inward until it identifies k elements close to x. The solution is efficient and optimal in handling the problem at hand.