How to Calculate the Sum of All Subset XOR Totals
In many programming interviews, you might encounter questions involving subsets of an array or list of numbers. One common problem is to find the sum of XOR values of all possible subsets of a given array. Understanding how to approach this problem efficiently is important, especially when the input size grows large.
Let’s first understand what the problem asks. Given an array, such as [1, 2, 3], you need to consider all non-empty subsets of this array. For each subset, compute the XOR (exclusive OR) of its elements. Then, sum all these XOR results to get a final answer.
Example: Calculating Manually
Suppose the array is [1, 2, 3].
All non-empty subsets are:
- , XOR = 1
- , XOR = 2
- , XOR = 3
- [1, 2], XOR = 1 ^ 2 = 3
- [1, 3], XOR = 1 ^ 3 = 2
- [2, 3], XOR = 2 ^ 3 = 1
- [1, 2, 3], XOR = 1 ^ 2 ^ 3 = 0
Adding all XORs: 1 + 2 + 3 + 3 + 2 + 1 + 0 = 12
Manually doing this for larger arrays isn't feasible, so we need a more efficient approach.
Efficient Approach
The naive way is to generate all subsets and calculating their XORs, which takes exponential time. For large arrays, this isn't efficient.
Instead, there's a clever mathematical insight. For each bit position, analyze how often that bit contributes to the total sum.
The key realization is:
- Each subset's XOR can be influenced independently by each element's bits.
- For each bit position, the total contribution is determined by the number of subsets where that bit appears in the XOR.
A more straightforward approach in code is to leverage the properties of XOR and combinatorics.
Implementation in Python
Python
Explanation of the Code
- The
totalis the bitwise OR of all numbers in the array. - Every subset either includes or excludes each element.
- Because of symmetry and XOR properties, each bit that is set in the combined OR will contribute to the sum in
2^(n-1)subsets. - Therefore, multiplying this OR value by
2^(n-1)gives the sum of XORs for all subsets.
Example Usage
Python
This approach runs in linear time relative to the input size and efficiently handles large arrays.
Understanding this pattern helps in tackling similar problems involving subsets and bitwise operations, making your solution both elegant and fast.
