uva1416 - Warfare And Logistics 最短路树优化

Codex API配置实战:从OpenAI兼容接口到稳定调用的完整指南 大模型API调用是当前AI应用开发的核心环节,其本质是通过标准化接口访问云端AI能力。OpenAI的API设计已成为行业事实标准,其Python SDK提供了简洁的封装。基于此标准,第三方兼容服务如Codex应运而生,它们通过统一接口聚合多个模型,在降低成本、简化开发方面具有显著价值。在实际工程中,配置这类服务常遇到认证失败、参数错误等高频问题,例如模型标识符不匹配导致的400错误和上下文长度超限。本文以Codex为例,系统讲解如何正确配置API密钥、端点URL,并详解模型参数、流式处理、错误重试等关键技术 阅读详情

The army of United Nations launched a new wave of air strikes on terroristforces. The objective of the mission is to reduce enemy's logistical mobility. Each airstrike will destroy a path and therefore increase the shipping cost of the shortest pathbetween two enemy locations. The maximal damage is always desirable.

Let's assume that there are n enemy locations connected by m bidirectional paths,each with specific shipping cost. Enemy's total shipping cost is given as c = $ \sum^{​{n}}_{​{i=1}}$$ \sum^{​{n}}_{​{j=1}}$path(i, j). Here path(i, j) is the shortest path between locations i and j. In case i and j are not connected, path(i, j) = L. Each air strike can only destroy one path. The total shipping cost after the strike is noted as c'. In order to maximizedthe damage to the enemy, UN's air force try to find the maximal c' - c.

Input 

The first line ofeach input case consists ofthree integers: n, m, and L. 1 < n$ \le$100,1$ \le$m$ \le$1000, 1$ \le$L$ \le$10$\scriptstyle \wedge$8. Each ofthe following m lines contains three integers: a,b, s, indicating length of the path between a and b.

Output 

For each case, output the total shipping cost before the air strike and the maximaltotal shipping cost after the strike. Output them in one line separated by a space.

Sample Input 

4  6  1000
1  3  2
1  4  4
2  1  3
2  3  3
3  4  1
4  2  2

Sample Output 

28  38

  N个节点M条边的无向图,每条边上有个正权,c为每对节点的最短路长度之和。要求删除一条边后使得新的c值c'最大。不连通的两点最短路长度视为L。输出c'。

  如果用floyd,每删一条边计算一次,复杂度O(n^3m)。如果用N次dijkstra计算单源最短路,时间复杂度为O(nm^2logn)。

  在源点确定的情况下,只要最短路树不被破坏,起点到所有点的距离都不会改变,也就是只有删除最短路树上的N-1条边中的某条,最短路树才需要重新计算。因此每个源点,最多只要求N次而不是M次单源最短路,时间复杂度变为O(n^2mlohn)。

  注意有坑,可能有重边,这时要用第二短边代替。

  以前写最短路都没有写成结构体这样,并且加边也不像这个结构体里写的这样。这题修改边的时候就发现这样结构化的模版的好处了,有很多功能而且方便调用。

#include<iostream>
#include<queue>
#include<cstring>
#include<cstdio>
#include<cmath>
#include<set>
#include<map>
#include<vector>
#include<stack>
#include<algorithm>
#define INF 0x3f3f3f3f
#define eps 1e-9
#define MAXN 110
#define MAXM 2000010
#define MAXNODE 105
#define MOD 100000
#define SIGMA_SIZE 4
typedef long long LL;
using namespace std;
int N,M,L,sum_single[MAXN],used[MAXN][MAXN][MAXN],idx[MAXN][MAXN];
vector<int> g[MAXN][MAXN];
struct Edge{
    int u,v,dist;
};
struct HeapNode{
    int u,d;
    bool operator < (const HeapNode& rhs) const{
        return d>rhs.d;
    }
};
struct Dijkstra{
    int n,m;
    vector<Edge> edges;
    vector<int> G[MAXN];
    bool done[MAXN];
    int d[MAXN];
    int p[MAXN];

    void init(int n){
        this->n=n;
        for(int i=0;i<n;i++) G[i].clear();
        edges.clear();
    }
    void add_edge(int u,int v,int dist){
        edges.push_back((Edge){u,v,dist});
        m=edges.size();
        G[u].push_back(m-1);
    }
    void dijkstra(int s){
        priority_queue<HeapNode> q;
        for(int i=0;i<n;i++) d[i]=INF;
        d[s]=0;
        q.push((HeapNode){s,0});
        memset(done,0,sizeof(done));
        while(!q.empty()){
            HeapNode x=q.top();
            q.pop();
            int u=x.u;
            if(done[u]) continue;
            done[u]=true;
            int L=G[u].size();
            for(int i=0;i<L;i++){
                Edge& e=edges[G[u][i]];
                if(e.dist>0&&d[e.v]>d[u]+e.dist){
                    d[e.v]=d[u]+e.dist;
                    p[e.v]=G[u][i];
                    q.push((HeapNode){e.v,d[e.v]});
                }
            }
        }
    }
}solver;
int cal(){
    int ret=0;
    memset(used,0,sizeof(used));
    for(int src=0;src<N;src++){
        solver.dijkstra(src);
        sum_single[src]=0;
        for(int i=0;i<N;i++){
            if(i!=src){
                int fa=solver.edges[solver.p[i]].u;
                used[src][fa][i]=used[src][i][fa]=1;    //fa->i是以src为源最短路树上的边
            }
            sum_single[src]+=(solver.d[i]==INF?L:solver.d[i]);
        }
        ret+=sum_single[src];
    }
    return ret;
}
int cal_new(int a,int b){
    int ret=0;
    for(int src=0;src<N;src++){
        if(!used[src][a][b]) ret+=sum_single[src];  //如果不在最短路树上src到各个点的最短路不变
        else{
            solver.dijkstra(src);
            for(int i=0;i<N;i++) ret+=(solver.d[i]==INF?L:solver.d[i]);
        }
    }
    return ret;
}
int main(){
    freopen("in.txt","r",stdin);
    while(scanf("%d%d%d",&N,&M,&L)!=EOF){
        for(int i=0;i<N;i++)
            for(int j=0;j<N;j++) g[i][j].clear();
        solver.init(N);
        int u,v,dist;
        while(M--){
            scanf("%d%d%d",&u,&v,&dist);
            u--;
            v--;
            g[u][v].push_back(dist);
            g[v][u].push_back(dist);
        }
        for(int u=0;u<N;u++)
            for(int v=u+1;v<N;v++) if(!g[u][v].empty()){
                sort(g[u][v].begin(),g[u][v].end());
                solver.add_edge(u,v,g[u][v][0]);
                idx[u][v]=solver.m-1;
                solver.add_edge(v,u,g[u][v][0]);
                idx[v][u]=solver.m-1;
            }
        int ans1=cal(),ans2=-1;
        for(int i=0;i<N;i++)
            for(int j=i+1;j<N;j++) if(!g[i][j].empty()){
                int& e1=solver.edges[idx[i][j]].dist;
                int& e2=solver.edges[idx[j][i]].dist;
                if(g[i][j].size()==1) e1=e2=-1;
                else e1=e2=g[i][j][1];
                ans2=max(ans2,cal_new(i,j));
                e1=e2=g[i][j][0];
            }
        printf("%d %d\n",ans1,ans2);
    }
    return 0;
}



RedHat 9 替换yum源镜像 引言:我们个人通常使用的红帽系统yum源是需要注册付费的,所以在使用yum源安装一些软件时无法正常安装(例如:MySQL),这时我们就需要将yum源替换为CentOS对应的源,以实现在红帽系统中正常安装软件。 阅读详情

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plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

LA4080/UVa1416 Warfare And Logistics 最短路树

题目大意: 求图中两两点对最短距离之和 允许你删除一条边,让你最大化删除这个边之后的图中两两点对最短距离之和。 暴力:每次枚举删除哪条边,以每个点为源点做一次最短路,复杂度\(O(NM^2logN)\)。 值得注意的是,\(Dijkstra\)的复杂度\(O(NlogN)\)是关于边而非点的。 这个复杂度对于\(n=100,m=1000\)的数据难以接受。我们考虑对每个点建出其最短路树。容易想...

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UVA 1416 - Warfare And Logistics(最短路树)

UVA 1416 - Warfare And Logistics 题目链接 题意:给定一个无向图,每个边一个正权,c等于两两点最短路长度之和,现在要求删除一边之后,新图的c值最大的是多少 思路:直接枚举删边,每次做一次dijkstra的话复杂度太高,其实如果建好最短路树,如果删去的边在最短路树上,才需要去做,这样复杂度就优化到(n^2mlog(n)),勉强可以接受 代码:

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UVA - 1416 Warfare And Logistics ( 最短路树)

题目链接:https://cn.vjudge.net/problem/UVA-1416 c比较容易求,cc如果一条边一条边枚举的话,每删除一条边,就要再求一遍任意两点的最短路,无论是跑一遍floyd或n-1遍dij时间复杂度都很大,我们可以用最短路树来优化一下,只有删除最短路树上的边,我们再重新求最短路,删除不是最短路树上的边,任意两点的最短路是不变的,这样时间复杂度就降了下来,因为每一个源点的...

GYH0730的博客 392

UVA 1416 Warfare And Logistics 最短路树

对每个点求最短路,同时求出最短路树。 枚举每条边,如果这条边在最短路树上,那么删掉这条边就需要重新计算最短路,否则不需要。 //#pragma comment(linker, "/STACK:1024000000,1024000000") #include<cstdio> #include<cstring> #include<cstdlib> ...

weixin_34067049的博客 131

uva 1416 - Warfare And Logistics(dijkstra)

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勿忘初衷 1422

UVA1416/LA4080 Warfare And Logistics

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UVA - 1416 Warfare And Logistics (最短路)

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