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This approach uses a greedy strategy with the help of a queue to track the index of flips. The idea is to traverse through the array and only flip when necessary, using a queue to efficiently track start points of flips that affect the current position. When flipping, add the index to a queue. Dequeue any index that is outside the current consideration (i.e., more than 'k' steps away).
The time complexity is O(n), where n is the length of nums, because we make a single pass through the array, and the space complexity is O(n) due to the auxiliary array used to track flips.
1from collections import deque
2
3def minKBitFlips(nums, k):
4 flip = deque()
5 flips = 0
6 flip_count = 0
7 for i in range(len(nums)):
8 if flip and flip[0] + k <= i:
9 flip.popleft()
10 flip_count -= 1
11 if nums[i] == flip_count % 2:
12 if i + k > len(nums):
13 return -1
14 flips += 1
15 flip.append(i)
16 flip_count += 1
17 return flips
18
19print(minKBitFlips([0, 0, 0, 1, 0, 1, 1, 0], 3)) # Output: 3
20Uses a deque to handle the effect of previous flips, checking current bit flip status via XOR. The approach handles flips by performing operations on bit positions with necessary adjustment for past operations using a queue-like structure.
This approach utilizes a sliding window technique with a flipping effect flag, which acts as a state tracker for ongoing flip operations. Using an in-place element flipping marker in the array enables this technique to minimize memory usage and avoid using additional data structures to track state.
Time complexity is O(n) because only single iteration and local modification are performed, while the space complexity is O(1), assisting state via in-place array manipulation.
This Java solution employs a dynamic-dependent variable `flip` that toggles based on in-place flips denoted by `A[i] += 2`, simulating a local flip state tracker, reducing memory overhead.