codeforces 706D(01字典树)

本文介绍了一种使用01字典树的数据结构来高效处理元素的增删操作,并支持查询集合中与指定值的最大异或值的问题。通过节点计数优化查询过程,确保了算法的有效性和快速响应。

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给出很多操作,能向一个集合里增加元素,也能减少元素,查询某一个值在这个集合里的最大异或值

01字典树,在节点上新开一个记录节点出现的次数,查询的时候用次数做判断,没有出现的话就相当与没有这个节点就好了。

#include <stdio.h>
#include <algorithm>
using namespace std;
const int N=200005;
struct node
{
    int val[2];
    int k;
}tree[N*40];
int tot;
long long int ans=0;

int newnode()
{
    tot++;
    tree[tot].val[0]=tree[tot].val[1]=0;
    tree[tot].k=0;
    return tot;
}
void ins(int x)
{
    int root=0;
    for(int i=31;i>=0;i--)
    {
        int temp=!!(x&(1<<i));
        if(tree[root].val[temp]==0)
            tree[root].val[temp]=newnode();
        tree[tree[root].val[temp]].k++;
        root=tree[root].val[temp];
    }
}
void query(int x)
{
    int root=0;
    for(int i=31;i>=0;i--)
    {
        int temp=!!!(x&(1<<i));
        if(tree[root].val[temp]!=0&&tree[tree[root].val[temp]].k>=1)
        {
            ans+=temp*(1<<i);
            root=tree[root].val[temp];
        }
        else
        {
            ans+=(!temp)*(1<<i);
            root=tree[root].val[!temp];
        }
    }
}
void decc(int x)
{
    int root=0;
    for(int i=31;i>=0;i--)
    {
        int temp=!!(x&(1<<i));
        tree[tree[root].val[temp]].k--;
        root=tree[root].val[temp];
    }
}

int main()
{
    int n;
    scanf("%d",&n);
    ins(0);
    while(n--)
    {
        char sym;
        int x;
        ans=0;
        scanf(" %c %d",&sym,&x);
        if(sym=='+')
        {
            ins(x);
        }
        if(sym=='?')
        {
            query(x);
            printf("%lld\n",ans^x);
        }
        if(sym=='-')
        {
            decc(x);
        }
    }
    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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