Description

Input

Output

Sample Input

3
8 7 6
3 9 4
1 10 5

Sample Output

18

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" 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Data Constraint

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" alt="" border="0" hspace="0" vspace="0" />
 
做法:几乎一看数据范围就可以猜出状态压缩dp,然而不知道为什么我打炸了。。于是该打记忆化搜索。
 #include <cstdio>
#include <iostream>
#include <cstring>
#define rep(i,a,b) for (int i=1;i<=n;i++)
#define M 65537
#define N 17
using namespace std;
int a[N],b[N],c[N],n,ans;
int f[M][N][]; void Init(){
scanf("%d",&n);
rep(i,,n) scanf("%d%d%d",&a[i],&b[i],&c[i]);
} void Dfs(int dep,int x,int zt,int situ,int high){
if (f[situ][x][zt]>=high) return;
else f[situ][x][zt]=high,ans=max(ans,high);
if (dep>n){
ans=max(ans,high);
return;
}
int cmpx,cmpy;
if (zt==||zt==) cmpx=a[x]; else cmpx=b[x];
if (zt==||zt==) cmpy=c[x]; else cmpy=b[x];
if (cmpx<cmpy) swap(cmpx,cmpy);
rep(i,,n){
if (<<i&situ) continue;
int xx=a[i],yy=b[i],next=;
if (xx<yy) swap(xx,yy);
if (xx<=cmpx&&yy<=cmpy) Dfs(dep+,i,next,situ|(<<i),high+c[i]); xx=a[i],yy=c[i],next=;
if (xx<yy) swap(xx,yy);
if (xx<=cmpx&&yy<=cmpy) Dfs(dep+,i,next,situ|(<<i),high+b[i]); xx=b[i],yy=c[i],next=;
if (xx<yy) swap(xx,yy);
if (xx<=cmpx&&yy<=cmpy) Dfs(dep+,i,next,situ|(<<i),high+a[i]);
}
} void Work(){
rep(i,,n){
Dfs(,i,,<<i,c[i]);
Dfs(,i,,<<i,b[i]);
Dfs(,i,,<<i,a[i]);
}
printf("%d",ans);
} int main(){
Init();
Work();
}

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