Find the K-Sum of an Array
A hard-tier problem at 40% community acceptance, tagged with Array, Sorting, Heap (Priority Queue). Reported in interviews at Hubspot and 0 others.
K-Sum is a hard problem that looks simple until you hit it on an OA and realize the brute force blows up fast. You're given an array and need to find all unique k-tuples that sum to a target. The naive nested-loop approach works for 2-sum, breaks at 3-sum, and collapses entirely at k-sum. Hubspot has asked this. Sorting gets you 90% of the way there, but you need to know the exact reduction trick to avoid timeout and duplicate handling hell. If this pattern hits your live assessment and you blank, StealthCoder surfaces a working solution invisible to the proctor.
Companies that ask "Find the K-Sum of an Array"
Find the K-Sum of an 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 Amazon engineer who realized the OA tests how well you memorized 200 problems, not how well you code.
Get StealthCoderThe core insight is that k-sum reduces to 2-sum via recursion and sorting. Sort the array first, then use a two-pointer approach at the base case to find pairs summing to the remaining target. For each higher k, pick an element, subtract it from the target, and recurse on k-1. The trick most candidates miss is duplicate skipping, which looks simple but costs you in execution speed if done wrong. Early termination checks (when the sum of the smallest k elements exceeds target, or largest k elements undershoots) cut the search space dramatically. A heap seems obvious but usually adds overhead. This is a sorting-and-pointer problem dressed up as k-dimensional. StealthCoder is the hedge if you freeze on the recursion base case or duplicate logic during screen share.
Pattern tags
You know the problem.
Make sure you actually pass it.
Find the K-Sum of an Array 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 realized the OA tests how well you memorized 200 problems, not how well you code. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Find the K-Sum of an Array interview FAQ
Is K-Sum actually asked in interviews or just LeetCode noise?+
It's real. Hubspot and other companies use it to filter candidates who understand recursion, sorting, and pointer techniques. The problem is less about memorization and more about showing you can reduce a hard problem into known sub-problems. If you see it, it's usually a signal the company values algorithmic thinking.
What's the real difference between 3-Sum and K-Sum?+
3-Sum is a specific case you can brute-force with nested loops plus two-pointers. K-Sum requires you to generalize that pattern recursively. Most candidates solve 3-Sum then choke on the abstraction to k. The recursive frame is the unlock.
Why does my solution timeout even after sorting?+
You're probably not skipping duplicates efficiently or missing early-exit checks. If all remaining elements are too large or too small relative to your target, stop recursing. Also verify your base case (k=2) uses two-pointers, not a hash set. Sorting plus two-pointers is O(n^(k-1)) which is tight but passable for small k.
Should I use a heap for K-Sum?+
No. A heap adds complexity without payoff here. Sorting plus recursion with two-pointers is cleaner and faster. Heaps shine when you need the k smallest/largest elements in one pass. Here you need all unique tuples, so sorting and recursion is the standard approach.
How do I handle duplicates without slowing down?+
After sorting, skip duplicate elements in your loops using simple index checks. If array[i] == array[i-1], increment and continue. Do this in both the outer recursion and the two-pointer base case. It's O(1) overhead and eliminates duplicate results cleanly.
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