K Radius Subarray Averages
A medium-tier problem at 46% community acceptance, tagged with Array, Sliding Window. Reported in interviews at Duolingo and 0 others.
K Radius Subarray Averages is a medium-difficulty array problem that tests your sliding window fundamentals. It's been asked at Duolingo and has a 46% acceptance rate, which means about half the candidates who attempt it don't land a clean solution under time pressure. The trick isn't complicated once you see it, but miss the optimization and you'll TLE on larger inputs. This is exactly the kind of problem where the obvious brute-force approach feels right until it doesn't. If this problem hits your live assessment and you blank on the sliding window pattern, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "K Radius Subarray Averages"
K Radius Subarray Averages is the kind of problem that decides whether you pass. StealthCoder reads the problem on screen and surfaces a working solution in under 2 seconds. Invisible to screen share. The proctor sees nothing. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe problem asks you to compute the average of each subarray of size 2*k+1 centered at each index, returning -1 where a full radius can't form. The naive approach loops through each element, then sums its surrounding k neighbors for every position. That's O(n*k) and will timeout. The insight is a standard sliding window: compute the sum of the first valid window, then slide it right by subtracting the leftmost element that exits and adding the new rightmost element that enters. This drops you to O(n). The catch is boundary handling: indices 0 to k-1 and n-k to n-1 can't have a full k-radius on both sides, so they return -1. Candidates often botch the boundary logic or forget to precompute the initial window sum. StealthCoder flags these mistakes and delivers the clean solution before you stall.
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K Radius Subarray Averages recycles across companies for a reason. It's medium-tier, and most candidates blank under the timer. StealthCoder is the hedge: an AI overlay invisible during screen share. It reads the problem and surfaces a working solution in under 2 seconds. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
K Radius Subarray Averages interview FAQ
Is this actually a sliding window problem or just array iteration?+
It's sliding window. The brute force is O(n*k), which fails on large arrays. Sliding window removes the inner loop: calculate the first window sum once, then shift it one element at a time, subtracting the left neighbor and adding the right. That's O(n). Most wrong answers come from missing this optimization.
What's the trick with boundary indices?+
Indices 0 to k-1 don't have k elements to their left, and indices n-k to n-1 don't have k to their right. Both return -1. Get this wrong and you'll return incorrect averages at the edges. Write your boundary checks early and test them before sliding.
Does Duolingo ask this at a specific seniority level?+
It's reported from Duolingo as a medium-difficulty problem. No data on seniority or stage in the interview funnel. Treat it as a core competency check: can you optimize nested loops into a sliding window.
What happens if k is 0?+
If k is 0, the radius is 0, so each subarray is just the single element itself. Its average is the element's value. The window size is 2*0+1 = 1. This is a degenerate case but it's valid. Make sure your code handles it without division by zero or index errors.
Is integer division or floating-point precision an issue here?+
The problem likely expects integer division or a specified rounding rule. Check the problem statement carefully. If you use floating-point, watch for precision loss on large sums. Compute the sum first, then divide once per window, not element-by-element.
Want the actual problem statement? View "K Radius Subarray Averages" on LeetCode →