Maximum Profitable Triplets With Increasing Prices I
A medium-tier problem at 55% community acceptance, tagged with Array, Binary Indexed Tree, Segment Tree. Reported in interviews at IBM and 0 others.
Maximum Profitable Triplets With Increasing Prices I is a medium-difficulty problem that combines array traversal with tree-based data structures. IBM has asked this in their assessment rounds. The problem forces you to track three elements across an array where both the values and their indices must satisfy ordering constraints. Most candidates either brute-force all triplets (too slow) or try to optimize without the right data structure. If this hits your live OA and you freeze on the tree-structure optimization, StealthCoder solves it invisibly during screen share and surfaces a working solution in seconds.
Companies that ask "Maximum Profitable Triplets With Increasing Prices I"
Maximum Profitable Triplets With Increasing Prices I 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 engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share.
Get StealthCoderThe trap here is the triplet constraint: you need elements at positions i < j < k where the prices follow a specific profitable pattern. A naive triple-nested loop works but times out on larger inputs, which is where most candidates get stuck. The real solution uses either a Binary Indexed Tree or Segment Tree to track minimum or maximum values to the left and right of your current position, collapsing the problem from O(n^3) to O(n log n). The indexing is tricky; off-by-one errors on tree updates are common. Many candidates know these data structures exist but haven't coded them under interview pressure. That's the exact scenario where StealthCoder removes the risk. You get a correct, tested implementation that you can paste and adapt in seconds.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximum Profitable Triplets With Increasing Prices I 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. Built by an engineer at a top-10 tech company who can solve these problems cold but didn't want to trust himself in a 90-minute screen share. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Maximum Profitable Triplets With Increasing Prices I interview FAQ
Is this actually asked by FAANG companies or just IBM?+
IBM has confirmed ask reports. The structure (tree-based array optimization) is common across finance and backend roles, so similar patterns appear elsewhere, but this exact problem is rare. It's a hedge problem, not a staple.
Can you solve this without a segment tree or BIT?+
Technically yes with nested loops, but you'll hit time limits on medium-to-large test cases. The assessment almost always requires the tree optimization. That's the entire point of the problem.
What's the biggest pitfall candidates hit?+
Off-by-one errors when querying or updating the tree structure. Also confusing whether you track minimums or maximums at each position. Under pressure, these bugs are silent and hard to spot.
Does this relate to other tree-based array problems?+
Yes. If you've solved range-sum or range-max queries with trees, the mechanics are identical. The twist here is combining it with a triplet constraint and profit logic.
How much time should I spend on this in an OA?+
If the pattern doesn't click in 8-10 minutes, move on and return if time allows. Tree construction alone eats 15-20 minutes if you're not fluent. That's why having a fallback solution matters.
Want the actual problem statement? View "Maximum Profitable Triplets With Increasing Prices I" on LeetCode →