Difference Between Maximum and Minimum Price Sum
A hard-tier problem at 32% community acceptance, tagged with Array, Dynamic Programming, Tree. Reported in interviews at Directi and 1 others.
This is a hard DP problem that appears deceptively simple: you're given a tree and need to find the difference between the maximum and minimum possible sum of node values you can select under some constraint. The 32% acceptance rate tells you the trick isn't obvious. Directi and Media.net ask it. Most candidates either miss the recursive structure or miscalculate the base cases. If this lands in your OA and you can't immediately see the recurrence, StealthCoder runs invisibly and surfaces a working solution while the proctor watches your screen.
Companies that ask "Difference Between Maximum and Minimum Price Sum"
Difference Between Maximum and Minimum Price Sum 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 core pattern is DFS with two DP states: one tracking the maximum sum achievable on a subtree, another tracking the minimum. You can't just greedily pick high values; the constraint (often that adjacent nodes can't both be selected, or that selection propagates differently) forces you to compute both directions simultaneously. The trap: thinking top-down when you need bottom-up, or forgetting to account for the case where you skip a node entirely. Most failures come from incomplete state definition. When you hit this live and the obvious greedy fails, that's when StealthCoder becomes your safety net, delivering the correct DP formulation in seconds without the proctor seeing a thing.
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Difference Between Maximum and Minimum Price Sum 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. 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.
Difference Between Maximum and Minimum Price Sum interview FAQ
Why is the acceptance rate so low at 32%?+
The recursive structure isn't hard, but the constraint interpretation and state design trip up most candidates. You need to track both max and min outcomes across the tree simultaneously, and edge cases around leaf nodes and subtree skipping are easy to mess up under time pressure.
Is this really asked by top companies?+
It's been reported by Directi and Media.net, both solid engineering shops. It's not FAANG-ubiquitous, but it shows up in mid-to-senior rounds at companies that care about algorithmic depth beyond the standard array/string circuit.
What's the main pitfall in the approach?+
Candidates often code a single DP value per node and try to maximize it, missing that you must compute both max and min paths in parallel. Forgetting to handle the 'don't select this node' branch is another common miss that breaks larger test cases.
Does this relate to the House Robber problems?+
Yes. Both use DP with 'take/don't take' decisions and track multiple outcomes. This version adds tree structure and forces you to reason about max/min duality, which linear house-robber problems don't require.
How much time should I spend on this if I haven't solved it before?+
In an OA, 15 to 20 minutes of dry thinking before coding. If the pattern doesn't click, move on and come back. This problem rewards clarity over speed. Know when to cut your losses and let StealthCoder handle it if you're stuck at the five-minute mark.
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