Reduce Array Size to The Half
A medium-tier problem at 69% community acceptance, tagged with Array, Hash Table, Greedy. Reported in interviews at Akuna Capital and 0 others.
Reduce Array Size to The Half is a medium-difficulty problem that looks straightforward until you hit the greedy twist. You have an array and need to remove the fewest elements such that the array length is reduced to half or less. Most candidates start with a brute-force or sorting approach and waste time optimizing the wrong thing. Akuna Capital has asked this one. The real solution lives in Heap or Hash Table plus Greedy, and if you blank on the frequency-first ordering during your live assessment, StealthCoder surfaces the working approach in seconds without the proctor knowing.
Companies that ask "Reduce Array Size to The Half"
Reduce Array Size to The Half 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. Made by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE.
Get StealthCoderThe trap is thinking you need to find some clever structural property of the array itself. You don't. The insight is purely greedy: remove the most frequent elements first. Count element frequencies using a Hash Table, then sort or heap-extract by frequency in descending order. Keep removing the highest-frequency elements until the array size hits your target. Most candidates either skip the frequency count and try direct array manipulation, or they sort by value instead of count. The Heap approach is cleaner for large datasets because you only extract half the unique elements, not the whole array. When you're mid-assessment and the obvious path doesn't click, StealthCoder hands you the frequency-first pattern and eliminates the time sink.
Pattern tags
You know the problem.
Make sure you actually pass it.
Reduce Array Size to The Half 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. Made by a working Amazon engineer who got tired of watching qualified friends bomb OAs they'd solve cold in an IDE. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Reduce Array Size to The Half interview FAQ
Why does sorting by frequency work here?+
Removing high-frequency elements eliminates more total elements per removal. If element X appears 10 times and Y appears 2 times, deleting X first gets you closer to the target faster. It's a greedy proof: always pick the locally optimal choice (highest frequency) and you get the global optimum (fewest deletions total).
Is Hash Table plus Sorting fast enough or do I need Heap?+
Both pass. Sorting is O(k log k) where k is unique elements; Heap is O(n + k log k). For most inputs, sorting is simpler and fast enough. Heap shines when k is huge but you only need to extract n/2 elements. Either approach works for the problem's constraints.
Do I need to actually return the removed elements?+
No. The problem asks for the minimum number of elements to remove, not which ones. Just count and stop once array length <= original length divided by 2. This matters because candidates sometimes overcomplicate the output.
How often is this asked at top companies?+
Lower frequency relative to classic array problems, but Akuna Capital has confirmed it. It's more common in hedge fund and quant firms than FAANG, so if you see it in an OA, the company likely values greedy reasoning and optimization thinking.
What's the most common mistake candidates make?+
Sorting by element value instead of frequency, or trying to find a pattern in the array structure itself. Some also miscount the target (forgetting integer division). Hash Table counts plus descending frequency sort catches all three mistakes immediately.
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