結果
| 問題 |
No.2337 Equidistant
|
| コンテスト | |
| ユーザー |
gew1fw
|
| 提出日時 | 2025-06-12 19:45:58 |
| 言語 | PyPy3 (7.3.15) |
| 結果 |
WA
|
| 実行時間 | - |
| コード長 | 2,363 bytes |
| コンパイル時間 | 166 ms |
| コンパイル使用メモリ | 82,184 KB |
| 実行使用メモリ | 183,564 KB |
| 最終ジャッジ日時 | 2025-06-12 19:46:20 |
| 合計ジャッジ時間 | 15,709 ms |
|
ジャッジサーバーID (参考情報) |
judge3 / judge2 |
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| ファイルパターン | 結果 |
|---|---|
| sample | AC * 1 |
| other | AC * 3 WA * 25 |
ソースコード
import sys
from collections import deque
def main():
sys.setrecursionlimit(1 << 25)
input = sys.stdin.read
data = input().split()
idx = 0
N = int(data[idx])
idx += 1
Q = int(data[idx])
idx += 1
edges = [[] for _ in range(N+1)]
for _ in range(N-1):
a = int(data[idx])
idx += 1
b = int(data[idx])
idx += 1
edges[a].append(b)
edges[b].append(a)
# Precompute distance from all nodes to all others is O(N^2), which is impossible for N=2e5.
# Instead, for each query, compute distance between S and T using BFS.
# But for 2e5 queries, each O(N), it's 4e10 operations, which is way too slow.
# So, we need a better way: for each query, compute distance between S and T using BFS, but optimized.
# Wait, perhaps we can compute distance using LCA with binary lifting.
# So, first, compute parent, depth, and binary lifting table for LCA.
# Compute parent and depth for each node using BFS
parent = [0]*(N+1)
depth = [0]*(N+1)
visited = [False]*(N+1)
q = deque()
q.append(1)
visited[1] = True
while q:
u = q.popleft()
for v in edges[u]:
if not visited[v]:
visited[v] = True
parent[v] = u
depth[v] = depth[u] + 1
q.append(v)
# Now, compute binary lifting table for LCA
LOG = 20
up = [[0]*(N+1) for _ in range(LOG)]
up[0] = parent
for k in range(1, LOG):
for v in range(1, N+1):
up[k][v] = up[k-1][up[k-1][v]]
def lca(u, v):
if depth[u] < depth[v]:
u, v = v, u
# Bring u to the same depth as v
for k in range(LOG-1, -1, -1):
if depth[u] - (1 << k) >= depth[v]:
u = up[k][u]
if u == v:
return u
for k in range(LOG-1, -1, -1):
if up[k][u] != up[k][v]:
u = up[k][u]
v = up[k][v]
return parent[u]
def distance(u, v):
a = lca(u, v)
return depth[u] + depth[v] - 2 * depth[a]
for _ in range(Q):
S = int(data[idx])
idx +=1
T = int(data[idx])
idx +=1
d = distance(S, T)
if d % 2 == 0:
print(1)
else:
print(0)
if __name__ == "__main__":
main()
gew1fw