Minimize OR of Remaining Elements Using Operations
A hard-tier problem at 29% community acceptance, tagged with Array, Greedy, Bit Manipulation. Reported in interviews at Aon and 0 others.
Minimize OR of Remaining Elements Using Operations is a hard array problem that shows up in real OAs despite a sub-30% acceptance rate. You're given an array and operations that let you reduce it, with the goal of minimizing the bitwise OR of what's left. The trick isn't obvious: most candidates start greedy and miss the bit-level pattern. If this lands in your assessment and you blank on the strategy, StealthCoder surfaces a working solution invisible to the proctor, so you're not stuck debugging on the clock.
Companies that ask "Minimize OR of Remaining Elements Using Operations"
Minimize OR of Remaining Elements Using Operations 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 used it to pass JPMorgan's OA and system design loop.
Get StealthCoderThe problem lives at the intersection of Greedy and Bit Manipulation. The naive approach is to just pick operations greedily, but the OR result depends on which bits survive across all remaining elements. The real insight is that you need to think backwards from the bit representation: which bits can you force to zero, and in what order do operations matter? Most candidates waste time trying every operation sequence. The pattern is to model which bits are controllable and construct the minimal OR by eliminating high-value bits first. Common pitfall: assuming a local greedy choice (remove the element with the highest bit set) leads to the global optimum. It doesn't. You need to simulate or use dynamic reasoning about bit states. If you hit this live and the greedy intuition fails, StealthCoder runs the correct bit-focused approach in seconds.
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Minimize OR of Remaining Elements Using Operations recycles across companies for a reason. It's hard-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 used it to pass JPMorgan's OA and system design loop. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimize OR of Remaining Elements Using Operations interview FAQ
Is this problem still being asked in real interviews?+
Yes, though at low frequency. Aon has reported it. The 28% acceptance rate suggests it's genuinely hard and not heavily drilled, which means fewer candidates see the pattern. That's both a risk and an edge if you study it.
What's the difference between this and a standard array greedy problem?+
Standard greedy problems reward you for picking the locally optimal element each step. This one requires understanding bit-level mechanics and how OR accumulates across remaining elements. The greedy choice that feels right often locks you into a suboptimal bit state later.
How much does bit manipulation matter here?+
It's critical. You need to think about which bits are set in remaining elements and how operations affect the OR result at the bit level, not just array-level. Without that lens, you'll chase dead ends.
What's the trick I'm missing if my greedy approach fails?+
The trick is that operations can be sequenced to eliminate high-value bits from the OR result, but the order and choice depend on the bit structure of all remaining elements, not just one. You may need to accept a locally worse choice to unlock a globally better OR.
Should I memorize a solution or understand the pattern?+
Understand the pattern. The problem is hard enough that variants exist. Focus on why greedy fails, how to reason about bit states, and when an operation is actually worth executing. That transfers to similar problems.
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