CodeForces 906D (欧拉降幂)

本文探讨了在给定范围内求解幂塔表达式并对其取模的算法问题。通过递归方法和预处理m及其欧拉函数序列,实现高效求解。与上帝与集合、super_log题目相似,但关注于范围变化和效率提升。

Power Tower

•题意

求$w_{l}^{w_{l+1}^{w_{l+2}^{w_{l+3}^{w_{l+4}^{w_{l+5}^{...^{w_{r}}}}}}}}$ 对m取模的值

•思路

 跟这两个题差不多上帝与集合正确用法  super_log

区别在于

①个数变成范围,不过也是一层一层递归,直到最后只有一层返回$w_{r}\ or\ \varphi(m)=1$

②对于一组数据 m是固定的,m的所有欧拉函数 $\varphi(m),\varphi(\varphi(m))...$可以预处理出来

  省去了一次次的计算,提高效率 

•代码

CodeForces 906D.cpp

### 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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