Codeforces671D Roads in Yusland

本文介绍了一种使用线性规划解决特定覆盖问题的方法,并通过转化为对偶问题简化求解过程。文章提供了一个具体实例,详细解释了如何利用贪心算法进行高效计算。

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Problem

Codeforces

Solution

被神仙同学安利的一道题目,然后想了好久都没想出来= =翻翻题解发现是道神仙题


可以把覆盖所有边的限制用矩阵的运算来描述,那么不难发现这其实是一道线性规划问题。然后这个问题不太好解决,我们可以考虑它的对偶问题。

\[\max\{c^Tx|Ax\leq b\}=\min\{b^Ty|A^Ty\geq c\}\]

正确性不会证明,只会感性地理解一下。右式的含义大概就是如果制作一个产品的成本大于等于它的收益,那么我们要最大化收益就不如把原材料直接卖了。那么这个式子的含义其实不考虑成本的最大收益等于无法获利时的最小成本,即一个临界状态,既可以做产品也可以直接卖。

由此,问题就转化成了:要求给每条边定一个权值,使得所有链覆盖的边权和小于等于它的权值,要最大化总边权。直接从深的开始贪心即可,在每个点上处理一下它的父边的选择。

时间复杂度 \(O(m\log m)\)

Code

#include <algorithm>
#include <cstdlib>
#include <cstdio>
#define pd(x) if(t[x].add)pushdown(x)
using namespace std;
typedef long long ll;
const int maxn=300010;
template <typename Tp> inline int getmin(Tp &x,Tp y){return y<x?x=y,1:0;}
template <typename Tp> inline int getmax(Tp &x,Tp y){return y>x?x=y,1:0;}
template <typename Tp> inline void read(Tp &x)
{
    x=0;int f=0;char ch=getchar();
    while(ch!='-'&&(ch<'0'||ch>'9')) ch=getchar();
    if(ch=='-') f=1,ch=getchar();
    while(ch>='0'&&ch<='9') x=x*10+ch-'0',ch=getchar();
    if(f) x=-x;
}
struct data{int v,nxt;}edge[maxn<<1];
struct leftist{
    int val,add,lc,rc,dis,del;
}t[maxn];
int n,m,p,tot,head[maxn],rt[maxn],dep[maxn];
ll ans;
void insert(int u,int v)
{
    edge[++p]=(data){v,head[u]};head[u]=p;
    edge[++p]=(data){u,head[v]};head[v]=p;
}
int new_node(int val,int del){t[++tot].val=val;t[tot].del=del;return tot;}
void pushdown(int x)
{
    int lc=t[x].lc,rc=t[x].rc;
    if(lc){t[lc].val+=t[x].add;t[lc].add+=t[x].add;}
    if(rc){t[rc].val+=t[x].add;t[rc].add+=t[x].add;}
    t[x].add=0;
}
int merge(int x,int y)
{
    if(!x||!y) return x|y;
    pd(x);pd(y);
    if(t[x].val>t[y].val) swap(x,y);
    t[x].rc=merge(t[x].rc,y);
    if(t[t[x].rc].dis>t[t[x].lc].dis) swap(t[x].lc,t[x].rc);
    t[x].dis=t[t[x].rc].dis+1;
    return x;
}
void input()
{
    int u,v,w;
    read(n);read(m);t[0].dis=-1;
    for(int i=1;i<n;i++){read(u);read(v);insert(u,v);}
    for(int i=1;i<=m;i++)
    {
        read(u);read(v);read(w);
        rt[u]=merge(rt[u],new_node(w,v));
    }
}
void dfs(int x,int pre)
{
    dep[x]=dep[pre]+1;
    for(int i=head[x];i;i=edge[i].nxt)
      if(edge[i].v^pre)
      {
        dfs(edge[i].v,x);
        rt[x]=merge(rt[x],rt[edge[i].v]);
      }
    if(x==1) return ;
    while(rt[x]&&dep[t[rt[x]].del]>=dep[x]) rt[x]=merge(t[rt[x]].lc,t[rt[x]].rc);
    if(!rt[x]){puts("-1");exit(0);}
    int tmp=t[rt[x]].val;
    t[rt[x]].val-=tmp;t[rt[x]].add-=tmp;
    ans+=tmp;
}
int main()
{
    input();
    dfs(1,1);
    printf("%lld\n",ans);
    return 0;
}

转载于:https://www.cnblogs.com/totorato/p/10597086.html

### 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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