Minimum Size Subarray in Infinite Array
A medium-tier problem at 31% community acceptance, tagged with Array, Hash Table, Sliding Window. Reported in interviews at DE Shaw and 0 others.
You're solving an array problem that looks straightforward until you realize the array wraps around infinitely. Minimum Size Subarray Sum in Infinite Array has shown up at DE Shaw and sits at a 31% acceptance rate, which tells you the obvious sliding window approach isn't enough. The trick is recognizing when the optimal subarray spans across the "end" of the array back to the beginning, and how to handle that without actually materializing an infinite structure. Most candidates miss this on their first attempt because they think about linear subarrays only. If this problem hits your live assessment and you blank on the wraparound logic, StealthCoder solves it in seconds, invisible to the proctor.
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Minimum Size Subarray in Infinite Array 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 core trap is assuming every valid subarray is contiguous in a single pass through the array. In an infinite circular array, the subarray with minimum length and target sum might wrap: start near the end, skip the middle, and continue from the beginning. You need prefix sums to calculate any subarray sum in O(1) time, then iterate through potential start positions and use a hash table or binary search to find if a valid ending position exists. The hash table approach stores prefix sums you've seen, letting you check whether prefix[end] minus some earlier prefix equals your target. The winning insight is recognizing the array loops, so you only need to check positions within two full passes to guarantee you've found any possible wraparound pattern. Common failures come from trying to extend the array literally or mishandling the prefix sum logic when the subarray crosses the boundary.
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Minimum Size Subarray in Infinite Array 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.
Minimum Size Subarray in Infinite Array interview FAQ
Why does this feel different from standard sliding window subarray problems?+
Standard sliding window assumes a linear array. Here, the array is circular and infinite, so a valid subarray can wrap from end to start. That rules out simple two-pointer approaches and forces you into prefix sum plus hash table or binary search. The wraparound is the entire point.
What's the actual trick I need to know before the OA?+
Prefix sums let you compute any subarray sum in O(1). Store them in a hash table as you build the prefix array. For each position, check if there's a prior prefix sum such that current prefix minus that sum equals your target. The wrap-around means you only need two passes through the array to catch all patterns.
How do I know if my solution handles wraparound correctly?+
Test a case where the answer subarray explicitly crosses the boundary, like array [1, 2, 3] with target 4. The subarray [3, 1] wraps around and has length 2. If your code doesn't find this, you're not handling the circular logic. Verify against a brute-force version that checks all possible start and end pairs across two passes.
Is this still being asked at DE Shaw?+
It's in their reported questions. With a 31% acceptance rate and the topic mix (array, hash table, sliding window, prefix sum), it's a medium-difficulty screening problem they use to filter out candidates who don't think in terms of prefix sums and circular structures.
What's the time complexity I should target?+
O(n) for building the prefix sum array plus O(n) for the main loop using a hash table to check prior prefixes. Space is O(n) for the hash table. You can't do better because you need to consider all start and end positions in the circular case.
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