Compute iterative sliding-window sum reduction
VmwareSoftware Engineer · Technical ScreenLast updated
This question tests the ability to implement a sliding window algorithm to compute dynamic reductions, focusing on efficient array traversal and state maintenance.
Problem Statement
Given an integer array nums, a positive integer k, and a reduction function op that represents an associative mathematical operation (e.g., addition, multiplication, or bitwise AND), return an array where the i-th element is the result of applying op to all elements in the window nums[i : i + k].
Function Signature
from typing import List, Callable
def sliding_window_reduce(nums: List[int], k: int, op: Callable[[int, int], int]) -> List[int]:
pass
Constraints
1 <= k <= len(nums) <= 10^5- Elements in
numsare integers within[-10^6, 10^6]. - The operation
opis associative.
Example 1
Input: nums = [1, 2, 3, 4, 5], k = 3, op = lambda a, b: a + b
Output: [6, 9, 12]
Explanation: (1+2+3)=6, (2+3+4)=9, (3+4+5)=12.
Example 2
Input: nums = [2, 4, 8, 16], k = 2, op = lambda a, b: a * b
Output: [8, 32, 128]
Explanation: (2*4)=8, (4*8)=32, (8*16)=128.
Edge Cases
k = 1: The output should be identical to the input array.k = len(nums): The output should contain exactly one element (the reduction of the entire array).
Hints
Hint 1
A naive approach would recompute the reduction for every window, leading to O(n * k) time complexity.
Hint 2
Consider if the operation has an inverse (like addition/subtraction or multiplication/division) to allow O(1) updates when the window slides.
Hint 3
If the operation does not have a simple inverse, you can use a deque or two stacks to maintain the reduction in O(n) amortized time.
Follow-up questions
- How would your approach change if the operation 'op' does not have an inverse (e.g., bitwise AND or maximum)?
- What is the space complexity of your implementation, and can you optimize it to O(1) extra space excluding the output array?
- How would you handle the case where 'op' is non-commutative?
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