Codeforces 906D Power Tower 拓展欧拉定理

本文介绍了一种处理区间价值查询的高效算法,通过预处理和递归应用欧拉定理来解决大规模序列上的复杂查询问题。适用于序列长度较大且模数范围广泛的应用场景。

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题意

定义一个序列w[1..n]的价值为这里写图片描述
给出一个长度为n的序列w,每次询问一个区间[l,r]的价值模m。
n200000,1m109n≤200000,1≤m≤109

分析

首先注意到,若序列中某个数为1,那么从这一位开始后面的元素全部是没用的。那么我们可以先把这部分去掉,这样序列中的全部元素都大于1。
定义函数solve(l,r,mo)solve(l,r,mo)表示询问[l,r][l,r]的权值模momo的值。
根据拓展欧拉定理,可以先把φ(mo)φ(mo)求出来,然后递归solve(l+1,r,φ(mo))solve(l+1,r,φ(mo))
在回溯的时候,需要知道递归求出的值是否小于φ(mo)φ(mo),这时可以在运算的时候记录一个布尔变量,表示运算得出的值是否大于一个常数inf,若大于则取模,否则就先不取模。
注意到一个数最多取O(log)O(log)φφ后就会变为1,那么我们可以先把每次递归的模数求出来,那么最多递归O(log)O(log)层模数就会变为1。
这时,如果还剩下超过4层需要递归,不难发现这一定是一个很大很大的值,这时就可以直接退出,否则的话就递归下去。

代码

#include<iostream>
#include<cstdio>
#include<cstdlib>
#include<cstring>
#include<algorithm>
using namespace std;

typedef long long LL;

const int N=100005;
const LL inf=1000000000;

int n,m,w[N],tot,a[105],nx[N];
bool flag;

int ksm(int x,int y,int mo)
{
    int ans=1;bool f1=0;
    while (y)
    {
        if (y&1)
        {
            flag|=((LL)ans*x>inf)|f1;
            if (flag) ans=(LL)ans*x%mo;
            else ans*=x;
        }
        f1|=((LL)x*x>inf);
        if (f1||flag) x=(LL)x*x%mo;
        else x*=x;
        y>>=1;
    }
    return ans;
}

int get_phi(int n)
{
    int ans=n;
    for (int i=2;i*i<=n;i++)
        if (n%i==0)
        {
            ans=ans/i*(i-1);
            while (n%i==0) n/=i;
        }
    if (n>1) ans=ans/n*(n-1);
    return ans;
}

int solve(int d,int l,int r)
{
    int mo=a[d];
    if (l==r)
    {
        if (w[l]>inf) {flag=1;return w[l]%mo;}
        else return w[l];
    }
    if (d==tot&&r-l+1>10) {flag=1;return 0;}
    int phi=a[d+1],x=solve(min(tot,d+1),l+1,r);
    if (flag) x+=phi;
    else if (x>=phi) x=x%phi+phi;
    return ksm(w[l],x,mo);
}

int main()
{
    scanf("%d%d",&n,&m);
    a[0]=m;
    while (a[tot]>1) tot++,a[tot]=get_phi(a[tot-1]);
    a[tot+1]=1;
    for (int i=1;i<=n;i++) scanf("%d",&w[i]);
    int q;scanf("%d",&q);
    nx[n+1]=n+1;
    for (int i=n;i>=1;i--) nx[i]=(w[i]==1)?i:nx[i+1];
    while (q--)
    {
        int l,r;scanf("%d%d",&l,&r);
        int tmp=nx[l];
        if (tmp<=r) r=tmp-1;
        if (l>r) puts(m>1?"1":"0");
        else flag=0,printf("%d\n",solve(0,l,r)%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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