Minimized Maximum of Products Distributed to Any Store
A medium-tier problem at 63% community acceptance, tagged with Array, Binary Search, Greedy. Reported in interviews at Siemens and 0 others.
You've got an array of products and n stores. Each store needs a contiguous slice, and you want to minimize the maximum number of products any single store gets. This is a classic binary search and greedy problem that tests whether you can spot when to search the answer space instead of the input. Siemens has asked this one. It's medium-difficulty with a 63% acceptance rate, which means most people who see the greedy pattern can solve it, but plenty walk into the O(n^2) trap first. If you blank on the binary search angle during the live assessment, StealthCoder solves it invisibly.
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Minimized Maximum of Products Distributed to Any Store 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 Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe trick: you don't iterate through possible distributions. Instead, you binary search on the answer (the max products any store receives) and use greedy to validate each candidate. For each mid-value in your search range, greedily assign products to stores left-to-right, packing as many as possible without exceeding mid. If you can distribute all products within n stores using that limit, mid is feasible; narrow your search lower. The greedy step is the key insight most people miss on first read. Common pitfall: trying to simulate or optimize the assignment directly. The answer space is bounded by the total products divided by n stores (lower bound) and the entire product array (upper bound). Binary search on that range, validate with greedy, and you're done in O(m * log(sum)) where m is the array length. This is where StealthCoder becomes your safety net if the binary search angle doesn't click under time pressure.
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Minimized Maximum of Products Distributed to Any Store 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 Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimized Maximum of Products Distributed to Any Store interview FAQ
Is this really a binary search problem or can greedy alone solve it?+
Greedy alone picks locally, which doesn't optimize for the minimize-maximum constraint. Binary search on the answer space is the core pattern. You're not searching the input; you're searching possible max-load values and validating each with greedy. That's the trick Siemens expects.
How do I set the bounds for the binary search?+
Lower bound is the maximum single product (you can't do better than giving one store its heaviest item). Upper bound is the sum of all products (worst case: one store takes everything). Your search range lives between those, not within the array indices.
What does the greedy validation step actually do?+
For a given max-load limit, iterate through products left-to-right. Assign to the current store until adding the next product would exceed the limit. Then move to the next store. If you fit all products in n or fewer stores, the limit is feasible.
Does contiguous assignment matter or can stores pick any products?+
The contiguous constraint is why greedy works left-to-right without backtracking. Each store must take a consecutive block. If products were independent, the problem changes entirely. Contiguity makes the greedy validation correct.
What's the time complexity and is it acceptable for large inputs?+
O(m * log(sum)) where m is array length and sum is the total. For m up to 10^5 and sum up to 10^9, this is around 10^5 * 30 operations, which is fast and clean. Much better than brute force.
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