← Hudson River Trading Interview Insights
The core idea clicked pretty fast: partition the timeline at each direction-change timestamp so you get segments where the watcher's direction is constant, then solve each segment analytically.
Model the problem as a sweep over time, tracking the watcher's direction and each person's frozen status. Use a priority queue or sorted events to efficiently determine when each person enters or exits the watcher's line of sight, updating positions accordingly. Finally, compute the total frozen time for each person and subtract from T to get their final position.
Pro tip: Clarify the watcher's line of sight: it likely extends infinitely in the current direction, so only people ahead of the watcher are affected. Also, consider edge cases like simultaneous events and people exactly at the watcher's position.
Clarify that the watcher moves at unit speed and changes direction at given timestamps. Each person moves at unit speed unless frozen. Define the watcher's line of sight as a ray in the current direction.
Determine when a person enters or exits the watcher's line of sight. This depends on relative positions and the watcher's direction. Compute these event times.
Process events in chronological order, updating the watcher's direction at given timestamps and toggling each person's frozen status. Maintain each person's position and frozen time.
For each person, final position = initial position + (T - total_frozen_time) * speed, where speed is 1. Ensure all events up to time T are processed.
Discuss time and space complexity. Use efficient data structures (e.g., priority queue) to handle up to N people and M direction changes. Consider if O((N+M) log N) is achievable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.