Maximum Value at a Given Index in a Bounded Array
A medium-tier problem at 39% community acceptance, tagged with Math, Binary Search, Greedy. Reported in interviews at ByteDance and 1 others.
ByteDance and TikTok have both asked this medium-level problem, and it trips up candidates who don't spot the math angle fast. You're given a bounded array sum and an index, then asked to find the maximum value at that index such that the array remains valid. The obvious greedy approach fails because you can't just maximize and check, you have to calculate whether a configuration is even possible. That's where binary search meets math, and most candidates waste minutes on the wrong approach. If this hits your live OA and you blank on the constraint-checking math, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "Maximum Value at a Given Index in a Bounded Array"
Maximum Value at a Given Index in a Bounded 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 trap is treating this as a pure greedy or simulation problem. The real pattern: binary search on the answer (the value at the target index), then use math to check if that value is achievable. To validate a candidate value, you calculate the minimum array sum needed, which involves arithmetic progression logic on both sides of the index. The greedy fill is capped by the value itself (can't exceed it) and by the bounded sum constraint. Once you see that validation is O(1) math rather than O(n) iteration, binary search becomes obvious. Most candidates either try greedy simulation or miss that the constraint-checking is purely mathematical. The mental shift from simulation to binary search on feasibility is where you lose time. StealthCoder handles the validation math and binary search bounds for you, so even if you freeze on the mathematical formula for summing an arithmetic sequence, you get a working solution in seconds.
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Maximum Value at a Given Index in a Bounded 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.
Maximum Value at a Given Index in a Bounded Array interview FAQ
Is this really asked by ByteDance and TikTok?+
Yes. Both companies are in the confirmed asker list. It's a medium-difficulty problem that tests whether you can combine binary search with mathematical constraint validation, which is a real production skill.
What's the main trick everyone misses?+
Treating it as simulation or greedy when it's actually a binary search problem. You search on the answer value, then use math (arithmetic progressions) to check feasibility in O(1), not iteration. Most candidates waste time simulating instead of mathematically validating.
How does this relate to the Math and Greedy topics?+
Math is the validation layer: calculating minimum sum for a given value using arithmetic progressions. Greedy is the fill strategy: spread the value as far as possible while capping neighbors at the value itself. Binary search ties them together.
What's the acceptance rate say about difficulty?+
At 39%, it's a real medium-difficulty problem. Not quite easy, not quite hard. Most people who solve it know the binary search plus math pattern upfront. Cold solving it is a time leak.
How do I prepare for this if I haven't seen it?+
Know arithmetic progression sum formulas cold. Understand how to validate a greedy configuration in O(1) using math. Practice binary search on feasibility, not just binary search on arrays. The pattern shows up in other constraint-satisfaction problems too.
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