Maximum Subarray Sum of Fixed Length After at Most K Swaps Python and Java solution?

Both languages solve it with a sliding window and ordered containers. Python typically uses heapq with lazy deletion or a sorted list library, while Java uses PriorityQueue or TreeMap for the same role. The logic is identical in both, so choose whichever you can implement cleanly under time pressure.

What is the best approach for Maximum Subarray Sum of Fixed Length After at Most K Swaps?

The best approach is a sliding window that maintains ordered structures for inside and outside values. It tracks the top K positive swap gains incrementally as the window moves. This runs in O(n log n) time with O(n) space.

What data structure is used in Maximum Subarray Sum of Fixed Length After at Most K Swaps?

The solution uses a sliding window along with ordered structures such as heaps, balanced BSTs, or Fenwick trees. The ordered structures let you quickly find the smallest inside value and largest outside value. Plain arrays alone are not enough for the optimal version.

What is the time complexity of Maximum Subarray Sum of Fixed Length After at Most K Swaps?

The optimal sliding window solution runs in O(n log n) time because each element is inserted and removed from an ordered structure once. A brute force version that re-sorts for every window runs in O(n² log n). Both use O(n) extra space.

Is Maximum Subarray Sum of Fixed Length After at Most K Swaps asked at Google, Amazon, or Meta?

This problem is a Medium-level array and greedy question that appears in contest-style and interview practice sets. Company-specific frequency varies and is not confirmed on FleetCode. Practice the sliding window and heap pattern, since it transfers to many array problems that interviewers ask.

Description

4087. Maximum Subarray Sum of Fixed Length After at Most K Swaps

Q: Is Maximum Subarray Sum of Fixed Length After at Most K Swaps easy or hard?

It is a Medium problem with an acceptance rate of about 62.4%, so it is approachable once you see the greedy swap idea. The hard part is maintaining the top K gains efficiently as the window slides. Practice sliding window with heaps to build the skill.

Q: How to solve Maximum Subarray Sum of Fixed Length After at Most K Swaps in O(n log n)?

Slide a window of length L across the array and keep the inside and outside values in two ordered multisets. Maintain the window sum and the sum of the best K positive gains, where a gain is an outside value minus the inside value it would replace. Update both as elements enter and leave, and record the best total for each window.

Medium62.4% acceptancePremium elsewhere, free here

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