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Problem solution · Python

ABC317 D — President

ABC317 D — President: a Python solution using dynamic programming. Learn the idea, check the complexity, and read the full code, with credit to KATO-Hiro AtCoder Solutions.

Technique
Dynamic programming
Source
KATO-Hiro AtCoder Solutions
Length
39 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 ABC317 D — President, 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

  • 39 lines of Python from the credited upstream file abc317_d.py.
  • The implementation visibly relies on ordered lookup, 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 KATO-Hiro AtCoder Solutions by KATO-Hiro and is used under the CC0-1.0 licence.

Open problem ↗Original source ↗
Full codeABC317 D — President · PythonPython
Use this to learn the idea, then write your own version.
# -*- coding: utf-8 -*-  def main():    import sys     input = sys.stdin.readline     n = int(input())    costs = [0] * n    seats = [0] * n     for i in range(n):        xi, yi, zi = map(int, input().split())        need = (xi + yi) // 2 + 1        cost = max(0, need - xi)        costs[i] = cost         seats[i] = zi     # dp[i][j]: i番目の選挙区でj議席を獲得するのに必要な鞍替えの人数    # j: 議席数とすべきところを選挙区としていたのが敗因    inf = 10**18    seat_sum = sum(seats) // 2 + 1    size = seat_sum + 1    dp = [inf] * size    dp[0] = 0     for i, (cost, seat) in enumerate(zip(costs, seats)):        for j in range(size)[::-1]:            nj = min(seat_sum, j + seat)            dp[nj] = min(dp[nj], dp[j] + cost)     print(dp[seat_sum])  if __name__ == "__main__":    main()

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