UVAlive 4100 - Sharif Super Computer (2SAT)

介绍SSC超级计算机的设计方案,特别是其独特的网络结构,包括使用不同速度的电缆来确保处理器间高效通信,并讨论如何优化网络布局以减少延迟。

Description

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SSC is a super computer designed in Sharif University having 2 ``master" and n ``slave" processors. It can run softwares in parallel: One of the master processors loads the software on the slave processors such that the memory and CPU usage among them are balanced, while the other master is used for monitoring the system.

Because of the dependencies between different parts of the software, many messages should be exchanged between processors. A very fast network is needed to minimize the message passing overhead. To optimize the network, a clique structure will be constructed in which there is a direct communication cable between each pair of processors.

There are two different cables: blue cables which can transmit up to 100 Megabits per second and red cables which can transmit up to 1 Gigabits per second. Each pair of slave processors will be connected by one blue cable. Due to the higher communication volume on master processors, the two masters are connected by one red cable and also each master is connected to each slave by another red cable.

SSC is thus made of n + 2 motherboards, each containing exactly one processor, the needed memory, and also n + 1 similar network sockets installed as a horizontal array. The motherboards are put in a vertical rack box, each in one horizontal shelf. So, each motherboard is uniquely identified by its height in the rack.

The cooling system has forced us to put the two master motherboards in the lowest and highest shelves of the rack. We assume that the master in the bottom has height 0, and the heights of the other motherboards are integers higher than 0. You, as a computer engineer, are asked to do the final assembly of SSC. You are given the empty rack box, the ready motherboards, and your job is to determine whether you can put the boards in the rack that satisfy the constraints and cable lengths.

There are exactly 2n + 1 red cables available with the given sizes. However the blue cables are available in m different sizes, and we have unlimited number of cables in each size. You are so careful to keep the cabling between processors tidy and tight, so you want to install the motherboards in the heights such that the size of cable used between each pair of motherboards is exactly equal to the difference between the heights of two boards.

Input

There are multiple test cases in the input. The first line of each test case contains two numbers n(1$ \le$n$ \le$100) and m(1$ \le$m$ \le$1000) . The second line contains 2n + 1 integers, which are the sizes of Gigabit Ethernet cables. The third line contains m integers which are the sizes of Megabit Ethernet cable groups. The last line of the input contains two zero numbers.

Output

For each data set you should write n + 1 integers as the heights of the motherboards in SSC rack box. The first number represents the height of the top master processor, and the remaining n integers are the positions of the slaves in an increasing order. In the case of having multiple solutions write the one with the minimum alphabetical order. If there is no solution write `` Impossible".

Sample Input

3 3 
3 7 7 10 10 14 17 
3 4 7 
3 3 
3 7 7 10 10 14 17 
3 4 8 
0 0

Sample Output

17 3 7 10 
Impossible
 
     

直接按照书上的思路写的。要注意的细节是,最开始2*n条边不能两两配对则false。

输出答案前记得先排序。

#include<cstdio>
#include<map>
#include<queue>
#include<cstring>
#include<iostream>
#include<algorithm>
#include<vector>
#include<list>
#include<set>
#include<cmath>
using namespace std;
const int maxn = 1e3 + 5;
const int INF = 1e9;
const double eps = 1e-6;
typedef unsigned long long ULL;
typedef long long LL;
typedef pair<int, int> P;
#define fi first
#define se second

int blue[maxn];
map<int, int> M;

struct TwoSAT{
    int n;
    vector<int> G[maxn*2];
    bool mark[maxn*2];
    int S[maxn*2], c;

    bool dfs(int x){
        if(mark[x^1]) return false;
        if(mark[x]) return true;
        mark[x] = true;
        S[c++] = x;
        for(int i = 0;i < G[x].size();i++){
            if(!dfs(G[x][i])) return false;
        }
        return true;
    }

    void init(int n){
        this -> n = n;
        for(int i = 0;i < n*2;i++) G[i].clear();
        memset(mark, 0, sizeof mark);
    }

    bool solve(){
        for(int i = 0;i < n*2;i += 2)
        if(!mark[i] && !mark[i+1]){
            c = 0;
            if(!dfs(i)){
                while(c > 0) mark[S[--c]] = false;
                if(!dfs(i+1)) return false;
            }
        }
        return true;
    }
};

TwoSAT solver;

P pos[maxn];

int abs(int x){
    return x<0?-x:x;
}

vector<int> ans;

int main(){
    int n, m;
    while(cin >> n >> m){
        if(n == 0 && m == 0)
            break;
        for(int i = 0;i < 2*n+1;i++){
            cin >> blue[i];
        }
        sort(blue, blue+2*n+1);
        bool ok = true;
        for(int i = 0;i < n;i++){
            pos[i] = P(blue[i], blue[2*n-i-1]);
            if(blue[i]+blue[2*n-i-1] != blue[2*n])
                ok = false;
        }
        M.clear();
        for(int i = 0;i < m;i++){
            int x;
            cin >> x;
            M[x] = 1;
        }

        solver.init(n);
        for(int i = 0;i < n;i++){
            int x1 = pos[i].fi;
            int y1 = pos[i].se;
            for(int j = i+1;j < n;j++){
                int x2 = pos[j].fi;
                int y2 = pos[j].se;
                if(M.count(abs(x1-x2))==0 && M.count(abs(x1-y2))==0){
                    solver.G[2*i].push_back(2*i+1);
                    solver.G[2*i+1].push_back(2*i);
                }
                else if(M.count(abs(x1-x2))==0)
                    solver.G[2*i].push_back(2*j+1);
                else if(M.count(abs(x1-y2))==0)
                    solver.G[2*i].push_back(2*j);

                if(M.count(abs(y1-x2))==0 && M.count(abs(y1-y2))==0){
                    solver.G[2*i].push_back(2*i+1);
                    solver.G[2*i+1].push_back(2*i);
                }
                else if(M.count(abs(y1-x2))==0)
                    solver.G[2*i+1].push_back(2*j+1);
                else if(M.count(abs(y1-y2))==0)
                    solver.G[2*i+1].push_back(2*j);
            }
        }
        if(ok && solver.solve()){
            cout << blue[2*n] << ' ';
            ans.clear();
            for(int i = 0;i < n;i++){
                if(solver.mark[2*i])
                    ans.push_back(pos[i].fi);
                else
                    ans.push_back(pos[i].se);
            }
            sort(ans.begin(), ans.end());
            for(int i = 0;i < ans.size();i++)
                cout << ans[i] << ' ';
            puts("");
        }
        else
            puts("Impossible");
    }
    return 0;
}


【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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