Codeforces 37D Lesson Timetable

文章讨论了一种问题,涉及在限制条件下确定教室间的课程分配方案,使用动态规划方法计算f[i][j],并提出利用组合数递推公式优化高精度计算。

题意

有1到M编号的教室,第一节课教室i有Xi个人上课,每个人第一节课的教室编号小于等于第二节课的教室编号,教室i最大容纳数量Yi,Yi>=Xi,求不同的方案数。

题解

先不管第一节课的方案数量,最后再乘就行了,先假设已经安排好了第一节课求第二节课的方案数。

f[i][j]表示安排好了前i个教室,已经安排了j个人的方案数。

假设Xi的前缀和为Si,那么:

f[i][j]=sum({Comb(Si-j+k,k)*f[i-1][j-k] | k>=0&&k<=Yi&&k<=j})

即前i-1个教室已经选好j-k了个人,再从Si-j+k的备选池里选出来k个人放到教室i。

Trick

可以提前用组合数递推公式计算Comb矩阵避免写高精度。

### 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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