結果

問題 No.2604 Initial Motion
ユーザー rlangevin
提出日時 2024-01-19 12:14:02
言語 PyPy3
(7.3.15)
結果
TLE  
実行時間 -
コード長 4,583 bytes
コンパイル時間 319 ms
コンパイル使用メモリ 82,296 KB
実行使用メモリ 115,156 KB
最終ジャッジ日時 2024-09-28 03:24:10
合計ジャッジ時間 66,185 ms
ジャッジサーバーID
(参考情報)
judge5 / judge2
このコードへのチャレンジ
(要ログイン)
ファイルパターン 結果
sample AC * 3
other AC * 38 TLE * 1
権限があれば一括ダウンロードができます

ソースコード

diff #
プレゼンテーションモードにする

import sys
input = sys.stdin.readline
#https://github.com/not522/ac-library-python/blob/master/atcoder/mincostflow.py
from typing import NamedTuple, Optional, List, Tuple, cast
from heapq import heappush, heappop
class MCFGraph:
class Edge(NamedTuple):
src: int
dst: int
cap: int
flow: int
cost: int
class _Edge:
def __init__(self, dst: int, cap: int, cost: int) -> None:
self.dst = dst
self.cap = cap
self.cost = cost
self.rev: Optional[MCFGraph._Edge] = None
def __init__(self, n: int) -> None:
self._n = n
self._g: List[List[MCFGraph._Edge]] = [[] for _ in range(n)]
self._edges: List[MCFGraph._Edge] = []
def add_edge(self, src: int, dst: int, cap: int, cost: int) -> int:
assert 0 <= src < self._n
assert 0 <= dst < self._n
assert 0 <= cap
m = len(self._edges)
e = MCFGraph._Edge(dst, cap, cost)
re = MCFGraph._Edge(src, 0, -cost)
e.rev = re
re.rev = e
self._g[src].append(e)
self._g[dst].append(re)
self._edges.append(e)
return m
def get_edge(self, i: int) -> Edge:
assert 0 <= i < len(self._edges)
e = self._edges[i]
re = cast(MCFGraph._Edge, e.rev)
return MCFGraph.Edge(
re.dst,
e.dst,
e.cap + re.cap,
re.cap,
e.cost
)
def edges(self) -> List[Edge]:
return [self.get_edge(i) for i in range(len(self._edges))]
def flow(self, s: int, t: int,
flow_limit: Optional[int] = None) -> Tuple[int, int]:
return self.slope(s, t, flow_limit)[-1]
def slope(self, s: int, t: int,
flow_limit: Optional[int] = None) -> List[Tuple[int, int]]:
assert 0 <= s < self._n
assert 0 <= t < self._n
assert s != t
if flow_limit is None:
flow_limit = cast(int, sum(e.cap for e in self._g[s]))
dual = [0] * self._n
prev: List[Optional[Tuple[int, MCFGraph._Edge]]] = [None] * self._n
def refine_dual() -> bool:
pq = [(0, s)]
visited = [False] * self._n
dist: List[Optional[int]] = [None] * self._n
dist[s] = 0
while pq:
dist_v, v = heappop(pq)
if visited[v]:
continue
visited[v] = True
if v == t:
break
dual_v = dual[v]
for e in self._g[v]:
w = e.dst
if visited[w] or e.cap == 0:
continue
reduced_cost = e.cost - dual[w] + dual_v
new_dist = dist_v + reduced_cost
dist_w = dist[w]
if dist_w is None or new_dist < dist_w:
dist[w] = new_dist
prev[w] = v, e
heappush(pq, (new_dist, w))
else:
return False
dist_t = dist[t]
for v in range(self._n):
if visited[v]:
dual[v] -= cast(int, dist_t) - cast(int, dist[v])
return True
flow = 0
cost = 0
prev_cost_per_flow: Optional[int] = None
result = [(flow, cost)]
while flow < flow_limit:
if not refine_dual():
break
f = flow_limit - flow
v = t
while prev[v] is not None:
u, e = cast(Tuple[int, MCFGraph._Edge], prev[v])
f = min(f, e.cap)
v = u
v = t
while prev[v] is not None:
u, e = cast(Tuple[int, MCFGraph._Edge], prev[v])
e.cap -= f
assert e.rev is not None
e.rev.cap += f
v = u
c = -dual[s]
flow += f
cost += f * c
if c == prev_cost_per_flow:
result.pop()
result.append((flow, cost))
prev_cost_per_flow = c
return result
K, N, M = map(int, input().split())
A = list(map(int, input().split()))
B = list(map(int, input().split()))
G = MCFGraph(N + 2)
s, g = 0, N + 1
for i in range(M):
u, v, d = map(int, input().split())
G.add_edge(u, v, K, d)
G.add_edge(v, u, K, d)
for i in range(K):
G.add_edge(s, A[i], 1, 0)
for i in range(N):
G.add_edge(i + 1, g, B[i], 0)
print(G.flow(s, g, K)[1])
הההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההההה
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
0