Maximum Product After K Increments
A medium-tier problem at 42% community acceptance, tagged with Array, Greedy, Heap (Priority Queue). Reported in interviews at Infosys and 0 others.
You've got an array and k increments to distribute. The goal is to maximize the product of the final array. This problem tests whether you understand that greedy doesn't always mean obvious, and that a heap lets you always increment the smallest number first. Infosys has asked it. The 42% acceptance rate tells you most people either misunderstand the greedy strategy or implement it inefficiently. If this lands on your assessment and you blank on why incrementing the min repeatedly beats any other approach, StealthCoder surfaces the heap solution in seconds, invisible to the proctor.
Companies that ask "Maximum Product After K Increments"
Maximum Product After K Increments 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 FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know.
Get StealthCoderThe trap is thinking you should distribute increments evenly or focus on large numbers. Wrong. To maximize a product, you want numbers as close to each other as possible, which means always incrementing the smallest element. A min-heap lets you do this in O(k log n) time: pop the min, add 1, push it back, repeat k times. Without the heap, you'd scan the array k times and hit O(k*n), which fails on large k. The other pitfall: not handling negative or zero numbers correctly. If the array contains negatives, you might need to first increment toward zero before maximizing. StealthCoder is the hedge if you freeze on the heap implementation or forget to handle edge cases during the live OA.
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Maximum Product After K Increments 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 FAANG engineer who treats the OA the way companies treat hiring: as a game with rules you should know. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Product After K Increments interview FAQ
Why is greedy (always increment the minimum) the right move here?+
A product is maximized when factors are balanced. Incrementing the smallest element pulls the overall product higher than incrementing an already-large element. Mathematically, if a < b, then (a+1)*b > a*(b+1) when you're trying to maximize the product. The heap ensures you always pick the true minimum in logarithmic time.
What makes this medium difficulty, not easy?+
Most candidates think of a naive O(k*n) scan-and-increment approach first, which times out on large k. The leap to a min-heap is the insight. Additionally, handling edge cases like negative numbers, zeros, or single-element arrays trips people up. Infosys likely uses this to filter out candidates who code without thinking about complexity.
Does Array and Greedy really cover this problem?+
Yes and no. It's greedy in strategy (always pick the minimum), but it's really a Heap problem in execution. You can't do greedy efficiently without a priority queue. The topics list is accurate: you're greedy in your decision, but the data structure is what makes it work at scale.
What if the array has negative numbers or zeros?+
If negatives are present, your first k increments might go toward making them positive or zero, depending on the numbers. Once all are positive, standard greedy applies. If the array is all zeros, it doesn't matter which one you increment first. The heap handles this fine as long as your comparator is correct.
How does this differ from other heap problems?+
Most heap problems ask you to find the k-th largest or merge streams. This one uses the heap as a tool to enforce a greedy invariant across multiple operations. You're not searching for an answer in the heap; you're using it to repeatedly access and modify the minimum in logarithmic time.
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