Maximize the Minimum Game Score
A hard-tier problem at 25% community acceptance, tagged with Array, Binary Search, Greedy. Reported in interviews at Infosys and 0 others.
You're up against a hard problem that only 25% of candidates pass, and Infosys is asking it. This is the type of problem where the greedy instinct fails and the trick isn't obvious on sight. You need to recognize that this is a binary search + greedy verification pattern hidden inside a maximization problem. The problem forces you to think backwards: instead of greedily picking the best move at each step, you binary search on the answer space and then verify if a candidate score is achievable. If you hit this live and freeze on the approach, StealthCoder surfaces a working solution in seconds, invisible to the proctor.
Companies that ask "Maximize the Minimum Game Score"
Maximize the Minimum Game Score 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 trap here is greedy. You want to pick the best move now and maximize immediately, but that leaves you in a weak position for future rounds. The actual solution flips the logic: binary search on the minimum score you can guarantee yourself, then verify each candidate answer by simulating greedily from the constraint backward. The greedy verification step works because once you fix the minimum score floor, the moves that achieve it follow a deterministic pattern. Most failures happen because candidates either stay stuck in greedy-at-each-step thinking or don't see that verification itself needs a greedy pass. The binary search bounds are defined by the array itself, and you're looking for the highest minimum you can lock in. Array manipulation and careful loop logic matter here. StealthCoder hedges the moment you realize the greedy-first instinct was wrong.
Pattern tags
You know the problem.
Make sure you actually pass it.
Maximize the Minimum Game Score 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 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.
Maximize the Minimum Game Score interview FAQ
Is this problem really asked by Infosys?+
Yes. It appears in their technical assessments. Hard difficulty and 25% pass rate indicate it's a real screening tool, not a rarity. Expect it if you're going through their pipeline.
What's the main trick I'm missing?+
Binary search on the answer, not the input. Fix a candidate minimum score, then verify it's achievable by checking if greedy moves from that constraint actually work. Most candidates stay in greedy-at-each-step mode and fail.
How does binary search connect to greedy here?+
Binary search narrows the feasible range of minimum scores. Greedy verification checks whether a specific minimum is actually reachable. They're complementary: binary search picks the answer space, greedy confirms it works.
Will I hit this in my real OA?+
If Infosys is your target and you're taking a technical assessment, yes, or something very similar. The 25% pass rate means most candidates see it and fail. Drill the binary search plus verification pattern now.
What's the failure mode on interview day?+
You'll code a greedy simulation that looks right but doesn't maximize the minimum. Then you'll debug for 20 minutes. The real solution requires flipping your search direction and adding a binary loop. This is where a working hedge matters.
Want the actual problem statement? View "Maximize the Minimum Game Score" on LeetCode →