Maximum Product Subarray
A medium-tier problem at 35% community acceptance, tagged with Array, Dynamic Programming. Reported in interviews at Hiver and 18 others.
Maximum Product Subarray is a medium-difficulty array problem that shows up across 19 companies, including Google, TikTok, LinkedIn, and Nvidia. You're asked to find the contiguous subarray with the largest product. The 35% acceptance rate signals a trick most candidates miss on first attempt. The trap is obvious: you think you just need to track a running max product, the way you'd solve maximum sum subarray. That doesn't work here. Negatives flip the sign, and a small negative number multiplied by a large negative can suddenly become your answer. You need dynamic programming, but not the version you expect. If this problem hits your live assessment and you blank on how to handle the negative-flipping mechanic, StealthCoder solves it in seconds, invisible to the proctor.
Companies that ask "Maximum Product Subarray"
Maximum Product Subarray 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 core insight is that you must track both the maximum and minimum product ending at each position. When you see a negative number, the minimum product (most negative) can flip and become the maximum product on the next iteration. A naive greedy approach that only tracks the max will fail when the optimal subarray contains negative multipliers. The algorithm is straightforward once you see it: iterate through the array, update both max and min products at each index by considering the current number alone, the current number times the previous max, or the current number times the previous min. This is pure Dynamic Programming on Array manipulation. The acceptance rate reflects that most candidates code the sum subarray solution first, then get confused when products behave differently. StealthCoder is your hedge if the negative-handling pattern isn't in your muscle memory when you sit down.
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Maximum Product Subarray 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.
Maximum Product Subarray interview FAQ
Why doesn't the greedy approach work for product like it does for sum?+
Multiplication by negative numbers reverses order. A small negative times a large negative becomes large positive. Sum never flips like that. You must track both max and min products simultaneously, because today's minimum might become tomorrow's maximum after hitting a negative number.
Is this still asked at Google, TikTok, and Nvidia?+
Yes. It appears across 10 top-tier and mid-market companies in the data, with Google and TikTok explicitly listed. The problem tests whether you understand dynamic programming beyond the textbook sum variant, not just array iteration.
What's the time and space complexity I should target?+
Single pass through the array with constant space for tracking max and min products. O(n) time, O(1) space. If you're considering nested loops or extra arrays, you're overcomplicating it.
How does this differ from the maximum sum subarray problem?+
Sum subarray tracks one running maximum. Product subarray must track maximum and minimum because negatives can flip the polarity. The DP state needs to store both values, not just one. Same array structure, fundamentally different state transition logic.
What's the one mistake that tanks most submissions?+
Forgetting to update the minimum product or not considering the current element as a starting point. When you hit a negative, the previous minimum (most negative) times that negative becomes a candidate for the new maximum. Missing that transition is how 65% of submissions fail.
Want the actual problem statement? View "Maximum Product Subarray" on LeetCode →