codeforces----193A Cutting Figure

本文讨论了如何通过最小割点集的概念解决特定问题,重点在于理解问题的复杂性和解决方案的有效性。作者详细解释了三种可能的情况,并提供了一个算法来解决这些问题。

道广说这个题可以用最小割点集来做,但是点太多,肯定得超时。一开始有点儿没想明白,第二天早晨起来仔细一考虑,稍微分析一下就能得出,只有三种情况,一种是‘#’数量少于3,这种情况不可分割,第二种情况是只需要割一个点就可以的,最后一种情况是只需要割两个点就可以,所以果断上来判断‘#’个数,如果小于3,直接输出-1,否则枚举割一个点的位置,用dfs搜一下看‘#’被分成几部分,发现大于1的话直接返回,输出1,再则直接输出2.

#include <iostream>
#include <cstring>

#define N 55

using namespace std;

int n, m;
int map[N][N], vis[N][N];

void dfs(int x, int y){
	vis[x][y] = 1;
	if(0 <= x - 1 && map[x - 1][y] && !vis[x - 1][y])
		dfs(x - 1, y);
	if(x + 1 < n && map[x + 1][y] && !vis[x + 1][y])
		dfs(x + 1, y);
	if(0 <= y - 1 && map[x][y - 1] && !vis[x][y - 1])
		dfs(x, y - 1);
	if(y + 1 < m && map[x][y + 1] && !vis[x][y + 1])
		dfs(x, y + 1);
}

int work(int x, int y){
	int i, j,cnt=0;
	memset(vis, 0, sizeof(vis));
	vis[x][y] = 1;
	for(i = 0; i < n; i ++)
		for(j = 0; j < m; j ++)
			if(map[i][j] && !vis[i][j]){
				dfs(i, j);
				cnt ++;
			}
	return cnt;
}

int solve(){
	int i, j;
	for(i = 0; i < n; i ++)
		for(j = 0; j < m; j ++){
			if(work(i, j) > 1)
				return 1;
		}
	return 0;
}

int main(){
	int i, j, cnt = 0;
	char buf;
	cin >> n >> m;
	for(i = 0; i < n; i ++)
		for(j = 0; j < m; j ++){
			cin >> buf;
			map[i][j] = buf == '#';
			cnt += map[i][j];
		}
	if(cnt < 3)
		cout << -1 << endl;
	else if(solve())
		cout << 1 << endl;
	else
		cout << 2 << endl;
	return 0;
}


Matlab基于粒子群优化算法及鲁棒MPPT控制器提高光伏并网的效率内容概要:本文围绕Matlab在电力系统优化与控制领域的应用展开,重点介绍了基于粒子群优化算法(PSO)和鲁棒MPPT控制器提升光伏并网效率的技术方案。通过Matlab代码实现,结合智能优化算法与先进控制策略,对光伏发电系统的最大功率点跟踪进行优化,有效提高了系统在不同光照条件下的能量转换效率和并网稳定性。同时,文档还涵盖了多种电力系统应用场景,如微电网调度、储能配置、鲁棒控制等,展示了Matlab在科研复现与工程仿真中的强大能力。; 适合人群:具备一定电力系统基础知识和Matlab编程能力的高校研究生、科研人员及从事新能源系统开发的工程师;尤其适合关注光伏并网技术、智能优化算法应用与MPPT控制策略研究的专业人士。; 使用场景及目标:①利用粒子群算法优化光伏系统MPPT控制器参数,提升动态响应速度与稳态精度;②研究鲁棒控制策略在光伏并网系统中的抗干扰能力;③复现已发表的高水平论文(如EI、SCI)中的仿真案例,支撑科研项目与学术写作。; 阅读建议:建议结合文中提供的Matlab代码与Simulink模型进行实践操作,重点关注算法实现细节与系统参数设置,同时参考链接中的完整资源下载以获取更多复现实例,加深对优化算法与控制系统设计的理解。
### Codeforces Problem 976C Solution in Python For solving problem 976C on Codeforces using Python, efficiency becomes a critical factor due to strict time limits aimed at distinguishing between efficient and less efficient solutions[^1]. Given these constraints, it is advisable to focus on optimizing algorithms and choosing appropriate data structures. The provided code snippet offers insight into handling string manipulation problems efficiently by customizing comparison logic for sorting elements based on specific criteria[^2]. However, for addressing problem 976C specifically, which involves determining the winner ('A' or 'B') based on frequency counts within given inputs, one can adapt similar principles of optimization but tailored towards counting occurrences directly as shown below: ```python from collections import Counter def determine_winner(): for _ in range(int(input())): count_map = Counter(input().strip()) result = "A" if count_map['A'] > count_map['B'] else "B" print(result) determine_winner() ``` This approach leverages `Counter` from Python’s built-in `collections` module to quickly tally up instances of 'A' versus 'B'. By iterating over multiple test cases through a loop defined by user input, this method ensures that comparisons are made accurately while maintaining performance standards required under tight computational resources[^3]. To further enhance execution speed when working with Python, consider submitting codes via platforms like PyPy instead of traditional interpreters whenever possible since they offer better runtime efficiencies especially important during competitive programming contests where milliseconds matter significantly.
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