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Problem solution · Python

Best Team With No Conflicts

Best Team With No Conflicts: a Python solution using dynamic programming. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Dynamic programming
Source
walkccc LeetCode Solutions
Length
31 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Dynamic programming

For Best Team With No Conflicts, the implementation records answers for smaller states and reuses them to build the requested result without repeating work.

  1. Define precisely what one DP state represents.
  2. Establish the base cases before transitions are evaluated.
  3. Process states in dependency order and combine only already-known values.

Code notes

  • 31 lines of Python from the credited upstream file 1626.py.
  • The implementation visibly relies on sequence storage, cached states.
  • No explicit loop blocks detected.

Complexity

Multiply the number of reachable states by the work performed for each transition, then include the stored state table in memory usage.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeBest Team With No Conflicts · PythonPython
Use this to learn the idea, then write your own version.
from dataclasses import dataclass  @dataclassclass Player:  age: int  score: int  class Solution:  def bestTeamScore(self, scores: list[int], ages: list[int]) -> int:    n = len(scores)    players = [Player(age, score) for age, score in zip(ages, scores)]    # dp[i] := the maximum score of choosing the players[0..i] with the    # players[i] being selected    dp = [0] * n     # Sort by age descending, then by score descending    players.sort(key=lambda x: (-x.age, -x.score))     for i in range(n):      # For each player, choose it first      dp[i] = players[i].score      # players[j].age >= players[i].age since we sort in descending order.      # So, we only have to check that players[j].score >= players[i].score.      for j in range(i):        if players[j].score >= players[i].score:          dp[i] = max(dp[i], dp[j] + players[i].score)     return max(dp)

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