codeforces 493D Vasya and Chess(博弈?)

本文探讨了在一个特定的N*N棋盘上,白队的Queen与黑队的Queen之间的策略博弈。通过理解棋子走法和游戏规则,揭示了如何利用棋子位置的奇偶性来预测胜者。

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题目大意:

给一个N*N的棋盘。然后一开始 白队的Queen在(1,1),黑队的Queen在(1,n)。

其余的点都是绿棋子。

Queen可以走横竖和斜线。(就是国际象棋里面Queen的走法,但是必须要吃子)。

问白方先走。谁能嬴(没得走或者被吃了就死了)


解题思路:

博弈题。。习惯性乱猜 就过了。写的比A还快。

猜法如下:每个人各走一步,可以使得总奇偶性不变。然后只要知道一开始的距离的奇偶性。就知道谁先死了。(勿喷)

#include <cstdio>
#include <vector>
#include <iostream>
#include <algorithm>
#include <cstring>
using namespace std;
int main()
{
    int n;
    cin >> n;
    if(n&1)
        printf("black\n");
    else
        printf("white\n1 2\n");
    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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