LeetCode Solutions. 1863. Sum of All Subset XOR Totals - DFS vs. Bit Manipulation! скачать в хорошем качестве

LeetCode Solutions. 1863. Sum of All Subset XOR Totals - DFS vs. Bit Manipulation!

Explored LeetCode 1863: Sum of All Subset XOR Totals! This fun problem asks for the sum of XOR totals across all subsets of a given array nums. I tackled it using two different methods: classic recursion and a slick bit manipulation technique. 🔹 Key Strategy: ✅ Approach 1 (DFS): Implemented a recursive solution in Python using a helper function dfs(i, res). This approach systematically explores the two choices for each element (include or exclude) to cover all 2^n subsets. It's intuitive but runs in O(2^n) time. ✅ Approach 2 (Bit Manipulation): Developed a highly efficient O(n) time, O(1) space solution (shown in TypeScript)! The core idea involves calculating res as the bitwise OR of all elements (res |= n in a loop), and the final answer is simply res * 2**(nums.length - 1). ✅ Key Learning: This problem is a fantastic illustration of algorithmic trade-offs. While the recursive dfs(i, res) directly models subset generation, understanding bitwise properties allows for a significantly faster O(n) solution via bit manipulation. A great reminder of the power of bitwise thinking! 🧑‍💻 Check out the approaches: 📌 LeetCode Solution: https://leetcode.com/problems/sum-of-all-s... 📌 GitHub Repository: https://github.com/RuslanTsykaliak/LeetCod... Understanding both recursive and bitwise techniques is vital for a well-rounded problem-solving toolkit. Which approach do you find more elegant or practical for subset problems? Let’s discuss below! ⬇️ Hashtags: #Algorithms #DataStructures #LeetCode #Programming #Coding #Python #TypeScript #Recursion #DFS #BitManipulation #Optimization