Codeforces 55D Beautiful numbers

美丽数问题解析

题意

一个十进制数能被自身非零数整除则是一个美丽数,求[l,r]区间的美丽数个数。

题解

数位DP+记忆化搜索,用dp[k][lcm][rem]保存前面各位数字的最小公倍数为lcm,MOD 252==rem的情况下,往后填k位0~9可以有多少种合法方案。

第一维的规模为18。

1~9只能组合出48个不同的lcm,所以第二维可以离散化将规模缩减到48。

第三维的规模为252。为什么是MOD 252?假设高位数字为A,填的低位数字为B,则(A*10^k+B) %2520=((10A)%2520*10^(k-1)+B)%2520=((A%252)*10^k+B)%2520,即高位数字对最终结果 MOD 2520的影响仅与其MOD 252的结果有关。

接下来说明一个数是否为美丽数只与其MOD 2520的结果有关。假设一个数C的各位数字最小公倍数为lcm,则C%lcm=(2520M+P)%lcm=(2520%lcm*M+P)%lcm=P%lcm,而P=C%2520。

### 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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