Swaps in Permutation

You are given a permutation of the numbers 1, 2, ..., n and m pairs of positions (aj, bj).

At each step you can choose a pair from the given positions and swap the numbers in that positions. What is the lexicographically maximal permutation one can get?

Let p and q be two permutations of the numbers 1, 2, ..., np is lexicographically smaller than the q if a number 1 ≤ i ≤ n exists, sopk = qk for 1 ≤ k < i and pi < qi.

Input

The first line contains two integers n and m (1 ≤ n, m ≤ 106) — the length of the permutation p and the number of pairs of positions.

The second line contains n distinct integers pi (1 ≤ pi ≤ n) — the elements of the permutation p.

Each of the last m lines contains two integers (aj, bj) (1 ≤ aj, bj ≤ n) — the pairs of positions to swap. Note that you are given apositions, not the values to swap.

Output

Print the only line with n distinct integers p'i (1 ≤ p'i ≤ n) — the lexicographically maximal permutation one can get.

Example
input
9 6
1 2 3 4 5 6 7 8 9
1 4
4 7
2 5
5 8
3 6
6 9
output
7 8 9 4 5 6 1 2 3

给你n个数
下m行,每行两个数,这两个位置的数可以无限次的交换
让你输出一个字典序最大的排序


因为1,4可以无限次的交换,4,7也可以无限次的交换,所以1,7也可以交换,这样可以想到用并查集把他们划为一类,

然后再用优先队列把同一个集合的存进去,这样输出的时候就能保证大的在前面。


#include<queue>
#include<cstdio>
#include<cstring>
#include<iostream>
#include<algorithm>

using namespace std;

int f[1000010];
int a[1000010];
int find(int x)
{
    if(x == f[x])
        return f[x];
    else
        return f[x] = find(f[x]);
}
priority_queue<int> q[1000010];

int main(void)
{
    int n,m,i;
    while(scanf("%d%d",&n,&m)==2)
    {
        for(i=0;i<=n;i++)
            f[i] = i;
        for(i=1;i<=n;i++)
            scanf("%d",&a[i]);
        for(i=1;i<=m;i++)
        {
            int t1,t2;
            scanf("%d%d",&t1,&t2);
            int x = find(t1);
            int y = find(t2);
            if(x != y)
                f[x] = y;
        }
        for(i=1;i<=n;i++)
            q[f[find(i)]].push(a[i]);
        for(i=1;i<=n;i++)
        {
            printf("%d ",q[f[i]].top());
            q[f[i]].pop();
        }
    }
}



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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