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This approach relies on sorting both the seats and students arrays. After sorting them, we pair the i-th seat with the i-th student. The sum of absolute differences between corresponding elements gives us the minimum number of moves required.
Time Complexity: O(n log n) due to sorting.
Space Complexity: O(1) since sorting is in-place.
1def min_moves_to_seat(seats, students):
2 seats.sort()
3 students.sort()
4 return sum(abs(s - st) for s, st in zip(seats, students))
5
6seats = [3, 1, 5]
7students = [2, 7, 4]
8print(min_moves_to_seat(seats, students))Using sort() for both lists, we zip them to calculate the sum of absolute differences between paired elements, which gives the minimum moves.
This approach involves creating frequency arrays or maps for both seats and students positions. Align students to seats by finding the closest available seat iteratively, simulating a greedy approach.
Time Complexity: O(n + m), where m is the range of possible positions (maximum 100).
Space Complexity: O(m) due to frequency arrays.
1
Initialize frequency arrays for both seats and students. Then, use two pointers to traverse and match the closest available seat to each student, updating movements accordingly.