Approach
Sorting and greedy selection
For Maximum Coins From K Consecutive Bags, the implementation first exposes a useful order, then scans that order while making locally justified choices.
- Choose the key that reveals the greedy or grouping structure.
- Sort the relevant records by that key.
- Scan in order, maintaining the invariant that makes each local choice safe.
Code notes
- 29 lines of Python from the credited upstream file 3413.py.
- The implementation visibly relies on sequence storage.
- No explicit loop blocks detected.
Complexity
Sorting is typically the dominant term unless the subsequent scan uses a more expensive nested operation.
Check the problem constraints before deciding whether this complexity will pass.
Use this to learn the idea, then write your own version.
1class Solution:2 def maximumCoins(self, coins: list[list[int]], k: int) -> int:3 return max(self._slide(coins, k),4 self._slide([[-r, -l, c] for l, r, c in coins], k))5 6 def _slide(self, coins: list[list[int]], k: int) -> int:7 coins.sort()8 res = 09 windowSum = 010 j = 011 for li, ri, ci in coins: 12 rightBoundary = li + k13 14 15 while j + 1 < len(coins) and coins[j + 1][0] < rightBoundary:16 lj, rj, cj = coins[j]17 windowSum += (rj - lj + 1) * cj18 j += 119 20 21 last = 022 if j < len(coins) and coins[j][0] < rightBoundary:23 lj, rj, cj = coins[j]24 last = (min(rightBoundary - 1, rj) - lj + 1) * cj25 26 res = max(res, windowSum + last)27 windowSum -= (ri - li + 1) * ci28 return res29