D. Swaps in Permutation

本文介绍了一种算法,用于求解给定一系列数字及它们之间的可交换规则后,能够通过合法交换操作得到的字典序最大序列。该算法利用广度优先搜索(BFS)找出所有可交换位置,并按数值大小重新排列以获得最终结果。

这个题目的题意是,给一个长度为n的数字序列,其中各个元素均不相同且在1到n之间。然后给出一些位置的交换规则,即给出某些位置上的数是可以互相交换的。求出最终能交换得到的字典序最大的序列。

题目的解决方法并不难,举个例子:如果位置1能和位置2山的数交换,位置1又可以和位置3上的数交换,那么,位置1,2,3对应的数实际上是可以互相交换了,那么该字典序最大的序列就是位置1,2,3上的元素从到小的序列了。那么基于此,可以采用bfs,将从某个位置出发,能过交换的到的位置全部找出来,然后从大到小依次填到相应的位置上。最终得到的序列就是答案。具体请看代码。

#include<cstdio>
#include<cstring>
#include<queue>
#include<vector>
#include<set>
#include<algorithm>

using namespace std;

const int MAX = 1000010;

int a[MAX], c[MAX], vis[MAX];
vector<int>G[MAX];

int main(){
    int n,m;

    while (scanf("%d%d",&n,&m) != EOF){
        for (int i = 1; i<=n; i++){
            scanf("%d", &a[i]);
            G[i].clear();
        }


        int x, y;
        for (int i = 0; i<m; i++){
            scanf("%d%d",&x,&y);
            G[x].push_back(y);
            G[y].push_back(x);
        }

        memset(vis, 0, sizeof(vis));
        int cnt = 0;

        for (int i = 1; i<=n; i++){
            if (!vis[i]){

                set<int>S, T;

                cnt++;
                queue<int> q;
                q.push(i);
                vis[i] = cnt;

                while (!q.empty()){
                    int u = q.front();
                    S.insert(u);
                    T.insert(a[u]);
                    q.pop();

                    for (int j = 0; j<G[u].size(); j++){
                        int v = G[u][j];

                        if (!vis[v]){
                            vis[v] = cnt;
                            q.push(v);
                        }
                    }
                }

                set<int>::iterator it;
                set<int>::reverse_iterator rit;
                for (it = S.begin(),rit=T.rbegin(); it!=S.end(); it++,rit++){
                    c[*it] = *rit;
                }


            }
        }
        printf("%d", c[1]);
        for (int i = 2; i<=n; i++) printf(" %d", c[i]);
        printf("\n");
    }

    return 0;
}









帮我看一下我的代码对吗 def swap(permutation, i, j): """ Return a new permutation where the elements at indices i and j are swapped. Parameters: permutation (list or np.ndarray): Current assignment. i, j (int): Indices to swap. Returns: (list or np.ndarray): New permutation with i and j exchanged. """ # create a copy or modify in place to swap positions i and j new_permutation=permutation new_permutation[i]=permutation[j] new_permutation[j]=permutation[i] return new_permutation def delta_i_j(permutation, i, j, weights, distance): """ Compute the change in objective value if we swap facilities i and j. Parameters: permutation (list or np.ndarray): Current assignment. i, j (int): Indices of facilities to swap. weights (np.ndarray): Flow matrix. distance (np.ndarray): Distance matrix. Returns: float: Change in cost after swapping i and j. """ # compute the fitness difference directly or with the O(n) delta formula old_cost=fitness(permutation,weights,distance) new_permutation=swap(permutation,i,j) new_cost=fitness(new_permutation,weights,distance) return new_cost-old_cost def best_swap(weights, distance, permutation, current_fitness, best_fitness, tabu_matrix, itr): """ Identify the best neighbor of the current solution while respecting Tabu Search rules. Parameters: weights (np.ndarray) : Flow matrix of the QAP. distance (np.ndarray) : Distance matrix of the QAP. permutation (list or np.ndarray) : Current assignment of facilities to locations. current_fitness (float) : Cost of the current solution. best_fitness (float) : Best cost found so far (global best). tabu_matrix (np.ndarray) : Matrix that stores, for each possible swap, the iteration number until which it is tabu. itr (int) : Current iteration index. Returns: i, j (int): Indices of the selected swap. delta (float): Change in cost for the swap. is_best (bool): True if this move improves the global best solution. """ # iterate over all pairs (i, j) # compute delta for each swap # skip tabu moves unless aspiration criterion is met # keep track of the best candidate n=len(permutation) best_delta=float('inf') best_i, best_j = 0, 0 is_best=False for i in range(n): for j in range(i+1,n): if tabu_matrix[i][j]<itr: if current_fitness+delta_i_j(permutation,i,j,weights,distance)<best_fitness: new_permutation=swap(permutation,i,j) delta=delta_i_j(permutation,i,j,weights,distance) return i,j,delta,True else: delta=delta_i_j(permutation,i,j,weights,distance) if delta<best_delta: best_delta=delta best_i, best_j=i,j return best_i,best_j,best_delta,is_best def tabu_search(weights, distance, tabu_tenure, tmax, diversification, u): """ Main Tabu Search routine. Parameters: weights (np.ndarray): Flow between locations. distance (np.ndarray): Distance between locations. tabu_tenure (int): Number of iterations a move remains tabu. tmax (int): Total number of iterations. diversification (bool): Enable diversification strategy. u (int): Number of iterations after which diversification triggers. Returns: best_fitness (float): Best objective value found. best_permutation (list or np.ndarray): Best assignment found. fitness_history (list): Cost of each visited solution. best_history (list): Best cost at each iteration. """ # initialize permutation and tabu matrix # initialize diversification structures if enabled # repeat for tmax iterations: # choose move (best swap or diversification) # apply move and update current fitness # update best solution if improved # update tabu matrix # update diversification bookkeeping if enabled # initialize permutation and tabu matrix n = len(weights) permutation = np.random.permutation(n) # 计算当前适应度 current_fitness = fitness(permutation,weights,distance) # 初始化最佳适应度 best_fitness = current_fitness # 初始化最佳排列 best_permutation = permutation.copy() # 初始化禁忌矩阵 tabu_matrix = np.zeros((n, n), dtype=int) for itr in range(tmax): # 找到最佳交换 i, j, delta, is_best = best_swap(weights, distance, permutation, current_fitness, best_fitness, tabu_matrix, itr) # 更新排列 permutation = swap(permutation, i, j) # 更新当前适应度 current_fitness += delta # 更新禁忌矩阵 tabu_matrix[i][j] = itr + tabu_tenure tabu_matrix[j][i] = itr + tabu_tenure # 更新最佳适应度和最佳排列 if is_best: best_fitness = current_fitness best_permutation = permutation.copy() # 多样化策略 if diversification and itr % u == 0: np.random.shuffle(permutation) current_fitness = fitness(permutation,weights, distance ) return best_permutation, best_fitness, # return final best solution and histories # ------------------------------------------------ # Diversification: # implement a mechanism that forces rarely-used # swaps to be tried after u iterations # ------------------------------------------------
10-07
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