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This approach uses a recursive backtracking strategy, where we build each possible concatenated string by considering elements one by one. We use a set to track characters for uniqueness and maximize the length only if all characters are unique.
Time Complexity: O(2^n), where n is the number of strings.
Space Complexity: O(n), for the recursive stack and current string.
1function maxLength(arr) {
2 function isUnique(s) {
3 return new Set(s).size === s.length;
4 }
5
6 function backtrack(current, index) {
7 if (!isUnique(current)) return 0;
8 let max = current.length;
9 for (let i = index; i < arr.length; i++) {
10 max = Math.max(max, backtrack(current + arr[i], i + 1));
11 }
12 return max;
13 }
14
15 return backtrack('', 0);
16}
17
18const arr = ["un", "iq", "ue"];
19console.log(maxLength(arr));This JavaScript solution explores subsequences via recursion, checking uniqueness by comparing string lengths with Set sizes, ensuring all characters are distinct.
This approach utilizes bitmasking to efficiently determine if characters are unique when combining strings. Each character is represented by a distinct position in a 32-bit integer, allowing for quick checks and updates.
Time Complexity: O(2^n), similar to previous approaches for evaluating combinations.
Space Complexity: O(n), due to the recursive stack with depth dependent on input size.
Here, Python employs integer bitmasking for quick checks against character collisions. Recursive calls traverse subsets while tracking valid formations.