【codeforces 348D】Turtles

本文介绍LGV定理在二维空间中计算从起点集到终点集不相交路径方案数的应用,通过矩阵的行列式计算路径方案数,并提出使用暴力dp方法求解。

题目描述

社论

LGV定理告诉我们,在二维空间下,有起点集 $S$,以及终点集 $T$,从 $S$ 到 $T$ 的每一个点走到对应点且不相交路径的方案数为下列矩阵的行列式:

$$
\begin{pmatrix}
f(S_1,T_1) & f(S_1,T_2) & f(S_1,T_3) & \cdots & f(S_1,T_n) \\
f(S_2,T_1) & f(S_2,T_2) & f(S_2,T_3) & \cdots & f(S_2,T_n) \\
\vdots & \vdots & \vdots & \ddots & \vdots \\
f(S_n,T_1) & f(S_n,T_2) & f(S_n,T_3) & \cdots & f(S_n,T_n)
\end{pmatrix}
$$

其中 $f(S,T)$ 表示从 $S$ 到 $T$ 的方案数

然后就每次暴力 $dp$ 一下即可

转载于:https://www.cnblogs.com/KingSann/articles/11128552.html

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### 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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