Brightest Position on Street
A medium-tier problem at 61% community acceptance, tagged with Array, Sorting, Prefix Sum. Reported in interviews at Hertz and 4 others.
You're given street lamps at specific positions with known brightness values, and you need to find the brightest point on the street. Sounds simple until you realize lamps cast light in both directions and brightness diminishes with distance. This problem appears in assessments from Robinhood, Uber, Visa, Roblox, and Hertz. The naive approach of checking every position will TLE. The trick involves prefix sums and understanding how to efficiently compute overlapping light ranges. If you haven't drilled this pattern and it hits your live assessment, StealthCoder surfaces the working solution in seconds, invisible to the proctor.
Companies that ask "Brightest Position on Street"
Brightest Position on Street 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 pattern is prefix sums combined with coordinate compression or a sweep-line technique. You can't check every position on the street (infinite), so you reduce the problem to candidate points where brightness changes: lamp positions and edges of light ranges. For each candidate point, you compute total brightness as the sum of contributions from all lamps. The gotcha is that brightness typically decays by distance, so you need to calculate each lamp's contribution correctly. Sorting those candidate points and using prefix sums or an ordered set to track active lamps avoids redundant recalculation. Most candidates either ignore the distance decay, attempt to iterate every position, or miss that this is fundamentally a coordinate compression problem. StealthCoder hedges the live OA when the exact decay formula or the ordered set trick isn't fresh.
Pattern tags
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Make sure you actually pass it.
Brightest Position on Street 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.
Brightest Position on Street interview FAQ
Is this really a medium-difficulty problem?+
Yes, by acceptance rate. The core insight is coordinate compression plus prefix sums, both medium-tier topics. The real trap is recognizing you can't brute-force every position and deciding which points matter. Many candidates overthink the brightness decay formula when it's usually just distance-based linear or quadratic falloff.
What's the trick I'm missing if I TLE?+
You're likely iterating every position on the street. Reduce to candidate points: lamp positions and range boundaries. Sort them, then compute brightness at each candidate using prefix sums or an ordered set to track lamp contributions. This drops complexity from O(street length) to O(n log n).
How does an ordered set help here?+
As you sweep left to right through candidate points, an ordered set tracks active lamps (those whose light still reaches the current point). Insert a lamp when you enter its range, erase when you leave. This avoids recalculating contributions for every point. Pair it with prefix sums for O(n log n) total time.
Do I need to know the exact brightness decay formula before the assessment?+
The problem statement will give it to you. Common patterns are brightness = max(0, initial - distance) or brightness = initial / (1 + distance). The formula doesn't change the algorithm. Implement it correctly once and the sorting and prefix logic stays the same.
Why is this asked at Uber, Robinhood, and Visa?+
It tests coordinate compression, understanding when brute-force fails, and comfort with prefix sums under time pressure. These are core skills for backend and infrastructure roles that deal with range queries and large datasets. It's not algorithmic circus; it's practical optimization thinking.
Want the actual problem statement? View "Brightest Position on Street" on LeetCode →