codeforces 894D

本文介绍了一种针对二叉树节点距离查询的高效算法。通过预处理每个节点到其子节点的距离并采用类似线段树的方法进行合并,结合二分查找技术,实现了对大量查询的快速响应。此算法的时间复杂度为预处理加查询的O(nlog(n)+mlog(n)^2),空间复杂度为O(nlog(n))。

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(对二叉树有感觉的话,思路还是出得蛮快的)
题意:给定一个n节点二叉树(n<106),每条边上有一个权值,然后给出mm<105)个询问,求Ai节点在Hi距离内能到达的树上哪些节点,求到达这些节点经过的距离之和。

思路:如果每次处理询问都要对树上节点遍历的话,肯定超时,于是想到预处理,由于这个树是很标准的二叉树,那么我们可以试试对于每个树节点,求出每个子节点距离它的值,并保存起来(就像线段树中的push_up操作一样)。然后对于每一个询问Ai,我们可以利用二分查找迅速求出这个节点所能到达它下面子树节点的距离和,然后想到达剩下的节点无非要经过Ai/2或者Ai1这两个节点。然后我们也可以迅速得到Ai1下面子树的结果。对于Ai/2上面的点,我们依次遍历这些店直到树根,然后每次处理这些点对应的Ai1子树即可。

时间复杂度:预处理+查询:O(nlog(n)+mlog(n)2)
空间复杂度:预处理的结果:O(nlog(n))

(开始以为可能会MLE,然后发现题目中内存比较大。好久没写代码,写的时候调了半天(居然以为break语句能跳出它上面的if-else语句…)。)

#include <cstdio>
#include <algorithm>
#include <vector>
#define LL long long

using namespace std;
const int maxn = 1000050;
const int inf = 0x3f3f3f3f;

int len[maxn][2];
vector<int> vec[maxn];
vector<LL> sum[maxn];

void push_up(int p, int n) {
    // merge sort
    int l = p<<1, r = p<<1|1, i = 0, j = 0;
    int l_len = len[p][0], r_len = r<=n? len[p][1]:0;
    while(i < (int)vec[l].size()-1 && j < (int)vec[r].size()-1) {
        if(vec[l][i]+l_len < vec[r][j]+r_len)
            vec[p].push_back(vec[l][i++]+l_len);
        else
            vec[p].push_back(vec[r][j++]+r_len);
        if(vec[p].back() >= inf) break ;
    }
    while(i < (int)vec[l].size()-1 && vec[p].back() < inf)
        vec[p].push_back(vec[l][i++]+l_len);
    while(j < (int)vec[r].size()-1 && vec[p].back() < inf)
        vec[p].push_back(vec[r][j++]+r_len);
    if(vec[p].back() >= inf) vec[p].pop_back();
    // calculate prefix sum
    sum[p].push_back(vec[p][0]);
    for(int k=1; k<(int)vec[p].size(); k++) {
        sum[p].push_back(vec[p][k]);
        sum[p][k] += sum[p][k-1];
    }
    return ;
}

void build(int p, int n) {
    int lson = p<<1, rson = p<<1|1;
    if(lson <= n) build(lson, n);
    if(rson <= n) build(rson, n);
    vec[p].clear();
    vec[p].push_back(0);
    if(lson <= n || rson <= n)
        push_up(p, n);
    vec[p].push_back(inf);
    return ;
}

LL get_sum(int n, int id, int h) {
    LL ret = h;
    int pos = upper_bound(vec[id].begin(), vec[id].end(), h) - vec[id].begin() - 1;
    if(pos >= 1) ret += (LL)h*pos - sum[id][pos];
    //printf("ret1 : %I64d\n",ret);
    while(id != 1) {
        h -= len[id/2][id&1];
        if(h > 0) ret += h;
        else break ;
        //printf("ret2 : %I64d\n",ret);
        int id_2 = id ^ 1, branch = len[id/2][id_2&1];
        if(id_2 <= n && h-branch > 0) {
            ret += h-branch;
            int pos = upper_bound(vec[id_2].begin(), vec[id_2].end(), h-branch) - vec[id_2].begin() - 1;
            if(pos >= 1) ret += (LL)(h-branch)*pos - sum[id_2][pos];
            //printf("ret3 : %I64d\n",ret);
        }
        id /= 2;
    }
    return ret;
}

int main() {
    int n, m;
    scanf("%d%d",&n,&m);
    for(int i=1; i<n; i++) {
        int t, st = (i+1)/2;
        scanf("%d",&t);
        len[st][(i+1)&1] = t;
    }
    build(1, n);
    while(m --) {
        int id, h;
        scanf("%d%d",&id,&h);
        LL ans = get_sum(n, id, h);
        printf("%I64d\n",ans);
    }
    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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