Partition to K Equal Sum Subsets
A medium-tier problem at 38% community acceptance, tagged with Array, Dynamic Programming, Backtracking. Reported in interviews at LinkedIn and 1 others.
You've got an array and need to partition it into k subsets where each subset has the same sum. It looks simple until you realize the greedy approach fails and you need to explore subsets smartly. LinkedIn and ByteDance both ask this. The trap is treating it like a standard DP problem when it's really about state-space search with memoization. If you freeze during the live OA, StealthCoder solves it invisibly, so you can move on and come back if you have time.
Companies that ask "Partition to K Equal Sum Subsets"
Partition to K Equal Sum Subsets 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 for the engineer who has done the work but might still blank with a webcam pointed at him.
Get StealthCoderThe trick is to reverse the problem: instead of building k subsets, check if you can fill k buckets of size target_sum by assigning array elements one by one. Backtracking with bitmask memoization tracks which elements you've used. The common pitfall is trying to construct all k subsets explicitly, which balloons complexity. You need to prune aggressively: sort the array descending so large elements fail fast, and if an element doesn't fit in any bucket, backtrack immediately. Many candidates waste time on DP table approaches that don't capture the subset-assignment constraint. StealthCoder provides the working backtracking skeleton with memoization keyed on the bitmask, so you avoid the 20-minute rewrite trap.
Pattern tags
You know the problem.
Make sure you actually pass it.
Partition to K Equal Sum Subsets 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 for the engineer who has done the work but might still blank with a webcam pointed at him. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Partition to K Equal Sum Subsets interview FAQ
Is this actually a medium problem or does it feel harder?+
The acceptance rate of 38% confirms it punches above medium weight. Most candidates know backtracking but don't see the bitmask memoization optimization. The state space explodes without it. It's not a hard problem mechanically, but the insight requires pattern recognition you may not have drilled.
Why doesn't a greedy approach work here?+
Greedy fails because ordering elements by size and packing them doesn't guarantee equal sums across all k buckets. You can have a valid partition that no greedy order discovers. You have to explore multiple orderings via backtracking to find the solution.
What's the role of bitmask in this problem?+
Bitmask tracks which array elements you've already assigned to buckets. Each bit represents whether an element is used. This lets you memoize the state of 'which subsets are achievable from this used set.' Without it, you recompute the same subproblems thousands of times.
How does this relate to the other backtracking topics I've drilled?+
It's backtracking plus memoization plus bit manipulation. You've seen permutation and combination backtracking. This layers on state compression via bitmask so you don't TLE. It's a common level-up at LinkedIn and ByteDance specifically.
What's the fastest way to code this under interview pressure?+
Sort the array descending first. Write a recursive function that takes the current bucket states and the next element index. Use a bitmask to memoize. If any element is larger than target_sum, return false immediately. Return true only when all k buckets are full. This skeleton takes 10 to 12 minutes once you see it.
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