[codeforces848D] Shake It!

这篇博客详细介绍了Codeforces 848D问题的解决方案,涉及无向图的构建和s-t最小割的计算。通过动态规划方法,枚举关键参数并利用状态压缩优化复杂度,最终得出O(n^4*logn)的时间复杂度算法。

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题目大意

给定n,m,最开始一个无向图中只有两个点s,t和连接它们的一条边。你需要进行n次操作,每次选择图中一条边(u,v),加入一个点i,并且添加两条边(u,i),(i,v)。
问最终有多少种不同构的图,满足其s-t最小割为m。模10^9+7输出

n,m≤50

分析

设f[i][j]表示i次操作,s-t最小割为j的方案数。
接下来你需要枚举五个数a,b,c,d,x(其中四元组(a,b,c,d)之前没有被枚举过),意义如下:
加入了x个这样的点:设这个点为v,其中s-v的最小割为b,在s-v为基础的子图上进行了a次操作;v-t最小割为d,在v-t为基础的子图上进行了c次操作。那么得到转移:
f[i][j]Cxf[a][b]f[c][d]+x1>f[i+x(a+c+1)][j+xmin(b,d)]
其中组合数表示f[a][b]*f[c][d]种方案选了x次,一种方案可重复选。

考虑优化这个dp。我们发现a,b,c,d,x对状态的转移有一个特点:dp两维的增量相同。所以可以设g[i][j]=[a+c==i][min(b,d)==j]f[a][b]f[c][d]。然后上面的转移变成:
f[i][j]Cxg[p][q]+x1>f[i+xp][j+xq]
g[p][q]是由a,c < p转移而来,所以可以顺序枚举p,q,并且更新f数组
枚举x由调和级数可知 总复杂度是log的
那么时间复杂度为O(n4logn)

#include <cstdio>
#include <cstring>
#include <algorithm>

using namespace std;

const int N=52,mo=1e9+7;

typedef long long LL;

int n,m,f[N][N],g[N][N],h[N][N],Inv[N],ans;

int main()
{
    scanf("%d%d",&n,&m);
    Inv[1]=1;
    for (int i=2;i<=n+1;i++) Inv[i]=(LL)Inv[mo%i]*(mo-mo/i)%mo;
    f[0][1]=1;
    for (int i=1;i<=n;i++) for (int j=1;j<=i+1;j++)
    {
        for (int p=0;p<i;p++)
        {
            for (int q=j;q<=p+1;q++)
            {
                g[i][j]=(g[i][j]+(LL)f[p][q]*f[i-p-1][j])%mo;
            }
            for (int q=j+1;q<=i-p;q++)
            {
                g[i][j]=(g[i][j]+(LL)f[p][j]*f[i-p-1][q])%mo;
            }
        }
        memset(h,0,sizeof(h));
        for (int p=0;p+i<=n;p++)
        {
            for (int q=1;q+j<=n+1;q++)
            {
                int C=1;
                for (int t=1;p+i*t<=n && q+i*t<=n+1;t++)
                {
                    C=(LL)C*(g[i][j]+t-1)%mo*Inv[t]%mo;
                    h[p+i*t][q+j*t]=(h[p+i*t][q+j*t]+(LL)C*f[p][q])%mo;
                }
            }
        }
        for (int p=i;p<=n;p++) for (int q=j;q<=p+1;q++) f[p][q]=(f[p][q]+h[p][q])%mo;
    }
    printf("%d\n",f[n][m]);
    return 0;
}
### Codeforces 1487D Problem Solution The problem described involves determining the maximum amount of a product that can be created from given quantities of ingredients under an idealized production process. For this specific case on Codeforces with problem number 1487D, while direct details about this exact question are not provided here, similar problems often involve resource allocation or limiting reagent type calculations. For instance, when faced with such constraints-based questions where multiple resources contribute to producing one unit of output but at different ratios, finding the bottleneck becomes crucial. In another context related to crafting items using various materials, it was determined that the formula `min(a[0],a[1],a[2]/2,a[3]/7,a[4]/4)` could represent how these limits interact[^1]. However, applying this directly without knowing specifics like what each array element represents in relation to the actual requirements for creating "philosophical stones" as mentioned would require adjustments based upon the precise conditions outlined within 1487D itself. To solve or discuss solutions effectively regarding Codeforces' challenge numbered 1487D: - Carefully read through all aspects presented by the contest organizers. - Identify which ingredient or component acts as the primary constraint towards achieving full capacity utilization. - Implement logic reflecting those relationships accurately; typically involving loops, conditionals, and possibly dynamic programming depending on complexity level required beyond simple minimum value determination across adjusted inputs. ```cpp #include <iostream> #include <vector> using namespace std; int main() { int n; cin >> n; vector<long long> a(n); for(int i=0;i<n;++i){ cin>>a[i]; } // Assuming indices correspond appropriately per problem statement's ratio requirement cout << min({a[0], a[1], a[2]/2LL, a[3]/7LL, a[4]/4LL}) << endl; } ``` --related questions-- 1. How does identifying bottlenecks help optimize algorithms solving constrained optimization problems? 2. What strategies should contestants adopt when translating mathematical formulas into code during competitive coding events? 3. Can you explain why understanding input-output relations is critical before implementing any algorithmic approach? 4. In what ways do prefix-suffix-middle frameworks enhance model training efficiency outside of just tokenization improvements? 5. Why might adjusting sample proportions specifically benefit models designed for tasks requiring both strong linguistic comprehension alongside logical reasoning skills?
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