Sum over subsets transform
You are given an array `a` of length 2^k, indexed by bitmasks 0..2^k-1. Compute the sum-over-subsets transform: return a new array `f` where `f[mask]` equals the sum of `a[sub]` over every submask `sub` of `mask` (including `mask` itself and 0). Lengths up to 2^16 must finish quickly.
Implement
sum_over_submasks(a: list[int]) → list[int]Examples
in
[[1,2,3,4]]out[1,3,4,10]What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 30 min
solution.py
InputExpectedGot
[[1,2,3,4]][1,3,4,10]not run yetsample