T1 [JZOJ6314] Balancing Inversions

题目描述

  Bessie 和 Elsie 在一个长为 2N 的布尔数组 A 上玩游戏。

  Bessie 的分数为 A 的前一半的逆序对数量,Elsie 的分数为 A 的后一半的逆序对数量。

  逆序对指的是满足 A[i]=1 以及 A[j]=0 的一对元素,其中i<j。例如,一段 0 之后接着一段 1 的数组没有逆序对,一段 X 个 1 之后接着一段 Y 个 0 的数组有 XY 个逆序对。

  Farmer John 偶然看见了这一棋盘,他好奇于可以使得游戏看起来成为平局所需要交换相邻元素的最小次数。请帮助 Farmer John 求出这个问题的答案。

数据范围

  前 $4$ 组数据 $N$ 分别为 $5,20,100,1000$

  对于 $100\%$ 的数据,$1 \leq N \leq 10^5$

分析

  考场上一直在想这题,绕了好多弯子,到最后十分钟差不多想清楚,但是没码完

  显然,当不交换中间两元素时,最小交换次数为前后两半的逆序对数差的绝对值

  然后可以先枚举将左边 $k$ 个 $1$ 移到右边时的所有情况,同理再枚举移 $0$ 的情况

  以移 $1$ 为例,我们可以用两个指针分别指向前半段的尾部和后半段的首部,然后依次向前查找 $1$ 和向后查找 $0$,这样总时间复杂度就是 $O(n)$ 的了

#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
#include <cmath>
#include <vector>
#include <queue>
using namespace std;
#define ll long long
#define inf 0x3f3f3f3f
#define N 100005 int n, cnt1, cnt2, pos1, pos2;
ll an, bn, ans, len1, len2;
int a[N], b[N]; int main() {
scanf("%d", &n);
for (int i = ; i <= n; i++) {
scanf("%d", a + i);
if (a[i]) cnt1++; else an += cnt1;
}
for (int i = ; i <= n; i++) {
scanf("%d", b + i);
if (b[i]) cnt2++; else bn += cnt2;
}
ans = abs(an - bn);
pos1 = n; pos2 = ; len1 = len2 = ;
for (int i = ; i <= cnt1 && i <= n - cnt2; i++) {
while (!a[pos1]) pos1--; len1 += n - pos1; pos1--;
while (b[pos2]) pos2++; len2 += pos2 - ; pos2++;
ll sum1 = an - len1 + i * (cnt1 - i);
ll sum2 = bn - len2 + i * (n - cnt2 - i);
ll now = len1 + len2 + i + abs(sum1 - sum2);
ans = min(ans, now);
}
pos1 = n; pos2 = ; len1 = len2 = ;
for (int i = ; i <= n - cnt1 && i <= cnt2; i++) {
while (a[pos1]) pos1--; len1 += n - pos1; pos1--;
while (!b[pos2]) pos2++; len2 += pos2 - ; pos2++;
ll sum1 = an + len1 - i * cnt1;
ll sum2 = bn + len2 - i * (n - cnt2);
ll now = len1 + len2 + i + abs(sum1 - sum2);
ans = min(ans, now);
}
printf("%lld", ans); return ;
}

T2 [JZOJ6317] 树

题目描述

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" 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数据范围

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" alt="" width="341" height="148" />

分析

  如果只考虑对一个点求方案数,那么就是一个很显然的树形 $dp$

  设 $f[x]$ 表示点 $x$ 的子树能组成的方案总数

  若点 $x$ 与其子节点之间的边染黑,则该子节点的子树不能存在边被染黑;若该边不被染黑,则可以算上子节点的所有方案

  于是得到 $f[x]=\prod (f[son]+1)$

  但对于每个点都求一遍的话,当然会超时

  实际上经过一条边的一个方向,得到子树的方案数是一定的,所以可以记忆化搜索

#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
#include <cmath>
#include <vector>
#include <queue>
using namespace std;
#define ll long long
#define inf 0x3f3f3f3f
#define N 100005 int n, from, tot, p = 1e9 + ;
int to[N << ], nxt[N << ], head[N];
int book[N << ]; void add(int u, int v) {
to[++tot] = v; nxt[tot] = head[u]; head[u] = tot;
} int dp(int x, int fa) {
int now = ;
for (int i = head[x]; i; i = nxt[i]) {
if (to[i] == fa) continue;
if (!book[i]) book[i] = dp(to[i], x);
now = (ll)now * (book[i] + ) % p;
}
return now % p;
} int main() {
scanf("%d", &n);
for (int i = ; i <= n; i++) {
scanf("%d", &from);
add(from, i); add(i, from);
}
for (int i = ; i <= n; i++)
printf("%d ", dp(i, ) % p); return ;
}

T3 [JZOJ6292] 序列

题目描述

数据范围

  

分析

  记旋转 $i$ 次后的答案为 $ans_i$,我们再单独考虑序列中每一个元素

  对于所有 $i \in [1,n]$,若 $s_i < i$,则:

  (1) 对 $ans_1,ans_2,···,ans_{i-s_i-1},ans_{i-s_i}$ 分别产生 $i-s_i-1,i-s_i-2,···,1,0$ 的贡献

  (2) 对 $ans_{i-s_i+1},ans_{i-s_i+2},···,ans_{i-2},ans_{i-1}$ 分别产生 $1,2,···,s_i-2,s_i-1$ 的贡献

  (3) 对 $ans_i,ans_{i+1},···,ans_{n-1},ans_n$ 分别产生 $n-s_i,n-s_i-1,···,i-s_i+1,i-s_i$ 的贡献

  $i \leq s_i < n$ 和 $s_i \geq n$ 的情况类似处理

  所以只需要支持区间递增/减加和最后查询每个点的值的操作即可

  这里用差分就可以实现,不过感觉我的写法很麻烦

#include <iostream>
#include <cstdio>
#include <cstring>
#include <algorithm>
#include <vector>
#include <queue>
using namespace std;
#define ll long long
#define inf 0x3f3f3f3f
#define N 2000005 inline int read() {
int x = , f = ; char c = getchar();
while (!isdigit(c)) {if (c == '-') f = -; c = getchar();}
while ( isdigit(c)) {x = x * + c - ''; c = getchar();}
return x *= f;
} int n, x;
ll minans = 4e12;
ll ans[N], pre[N], suf[N], p[N], s[N]; int main() {
freopen("a.in", "r", stdin);
freopen("a.out", "w", stdout);
n = read();
for (int i = ; i <= n; i++) {
x = read();
if (x < i) {
suf[i - x - ]++;
pre[i - x + ]++; pre[i]--; p[i] -= x - ;
suf[i - ]--; suf[n - ]++; s[i - ] -= n - i;
ans[i] += i - x;
}
else if (x < n) {
pre[]++; pre[i]--; p[i] -= i - ;
ans[] += x - i; ans[i] -= x - i;
suf[i - ]--; suf[n + i - x - ]++; s[i - ] -= n - x;
pre[n + i - x + ]++;
}
else {
pre[]++; pre[i]--; p[i] -= i - ;
ans[] += x - i; ans[i] -= x - i;
pre[i + ]++;
ans[i] += x - n;
}
}
for (int i = ; i <= n; i++) pre[i] += pre[i - ];
for (int i = ; i <= n; i++) pre[i] += pre[i - ] + p[i];
for (int i = n; i >= ; i--) suf[i] += suf[i + ];
for (int i = n; i >= ; i--) suf[i] += suf[i + ] + s[i];
for (int i = ; i <= n; i++)
minans = min(minans, (ans[i] += ans[i - ]) + pre[i] + suf[i]);
printf("%lld\n", minans); return ;
}

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