Codeforces 1009D Relatively Prime Graph 【贪心】【复杂度分析】

本文探讨了一个看似为n^2*logn复杂度的问题如何通过合理的数据限制和算法优化达到实际可行的解决方案。通过枚举和利用特定条件下的贪心策略,确保了在限定条件下程序能够高效运行。

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我的代码看起来复杂度是n^2*logn【枚举i,j==n^2 , 判断gcd==logn】 (n=1e5)但为什么过了呢?

因为数据里限定了m也是1e5级别的数

如果impossible 【及贪心完造的边还没有m这么多】,那n一定很小,小到n^2 * logn可以过;

如果possible,那一定n^2还没有枚举完就造完m条边,直接break了。【因为n^2枚举完造的边与m不是一个数量级的数】

可能还担心造完的图不是connected的,但实际上我们先从1向所有其他点连边,所以保证了连通性。

 1 #include<iostream>
 2 using namespace std;
 3 
 4 int u[100005],v[100005],pool;
 5 
 6 int gcd(int a,int b){//a大于b 
 7     if(b==0) return a;
 8     return gcd(b,a%b);
 9 }
10 //connected
11 int main(){
12     int n,m; cin>>n>>m;
13     
14     if(m<n-1) { cout<<"Impossible"; return 0; }
15     
16     for(int i=1;i<=n;i++){//u向v建边不会影响其余的边,因为只和gcd有关 
17         for(int j=i+1;j<=n;j++){
18             if(pool==m) break;
19             if( gcd(j,i)==1 ) { u[++pool]=i; v[pool]=j; }
20         }
21         if(pool==m) break;
22     }
23     
24     if(pool==m){
25         cout<<"Possible"<<endl;
26         for(int i=1;i<=pool;i++) cout<<u[i]<<" "<<v[i]<<endl;    
27     }
28     else cout<<"Impossible";
29     
30     return 0;
31 }

 

转载于:https://www.cnblogs.com/ZhenghangHu/p/9313362.html

### Codeforces Problem 1009 Details and Solution The referenced content discusses a set of problems related to number theory on the Codeforces platform with difficulties ranging from 2000 to 3000+. The focus is primarily on optimizing algorithms due to large input sizes where complexities such as \(O(n \sqrt{n})\) are not feasible without additional optimizations[^1]. For **Problem 1009**, while specific details about this exact problem may vary depending on updates or changes within the Codeforces database, it generally involves handling operations over tree structures efficiently given constraints like those described: - Input consists of two integers \(n\) (number of nodes) and \(m\) (number of queries), followed by detailed descriptions of each node's children. - Queries include three types: adding an edge between two vertices ("1 v u"), removing edges based on height conditions ("2 v h"), and finding paths satisfying certain properties ("3 k"). Given these parameters, efficient data structure usage becomes critical. Below outlines how one might approach solving similar structured questions effectively. #### Efficient Data Structures Utilization To handle up to \(10^5\) elements per dimension (\(n,m\)), advanced techniques must be employed: - Preprocessing using Depth First Search (DFS) traversal can help compute auxiliary information quickly during runtime. - Segment trees or Binary Indexed Trees could assist managing dynamic range updates/queries required under different operation modes mentioned above. Here’s some sample pseudo-code demonstrating initialization phase alongside query processing logic utilizing DFS precomputation technique combined with segment tree implementation for fast querying capabilities: ```python from collections import defaultdict class Node: def __init__(self): self.children = [] def dfs(node_id, parent=-1): global depth, subtree_size start_time[node_id] = time_counter[0] time_increases() subtree_size[node_id]=1 for child in adj_list[node_id]: if(child !=parent): depth[child]=depth[node_id]+1 dfs(child,node_id) subtree_size[node_id]+=subtree_size[child] # Initialize variables & read inputs here... adj_list=defaultdict(list) for _ in range(m): op,*params=input().split() params=list(map(int,params)) if(op=='1'): # Add Edge Logic Here Using Params[v,u] elif(op=='2'): pass else: # Operation Type '3' result=process_query_k(params[k]) print(result) ``` This code snippet provides foundational steps towards constructing solutions tailored specifically toward addressing challenges posed through complex graph manipulations involving numerous sequential actions against hierarchical datasets represented via adjacency lists. §§Related Questions§§ 1. What other common optimization strategies exist when dealing with computational limits exceeding standard algorithmic approaches? 2. How does implementing lazy propagation affect performance improvements inside segment-tree-based systems used across competitive programming platforms? 3. Can you provide examples illustrating alternative methods besides DFS preorder/postorder traversals that achieve equivalent results more suitably suited under particular circumstances? 4. In what ways do persistent data structures contribute differently compared traditional mutable ones concerning memory consumption versus speed tradeoffs seen regularly throughout high-level contests including CF rounds?
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