算法系列笔记8(有关图的算法二—最短路径问题)

柔光的暖阳◎ 2022-08-07 03:56 70阅读 0赞

图的最短路径问题主要分为两类，单源最短路径问题和全对最短路径问题。单源最短路径问题指给点单个源点，求其到所有其它顶点之间的最短距离。而全对最短路径问题指所有顶点之间的最短路劲问题。此外对于单对最短路径问题，从渐进意义上来看，目前还没有比最好的单元算法更快的算法来解决这一问题。

**一：单源最短路径问题**

单源最短路劲问题根据其权重分为四类，当图G=(V,E)为无权图，直接使用广度优先遍历(这里不做介绍)；当权值为非负值，则使用Dijkstra算法；存在负权值及负权环，可以使用Bellman-Ford算法。最后一类是有向无回图(DAG)的单源最短路径问题。

**Dijkstra算法**

Dijkstra算法为贪心算法，如何证明贪心策略确实有效才是关键。

基本思想：给每个顶点一个估算距离，初始化源点的估算距离为0，其它点为无穷大；优先队列Q=V，从Q中选择最小的估算距离的顶点加入集合S中，然后对从该顶点出发的每条边进行松弛，从而更新每个顶点的估算距离。

需要证明的就是从Q中选择最小估算距离的顶点加入S中，此时的估算距离就等于该顶点的最短路径。

代码如下：

// dijkstra 算法
    
    string Graph::intToStr(int i){
    	string s;
    	stringstream ss;
    	ss << i;
    	ss >>s;
    	ss.clear();
    	return s;
    }
    void Graph::dijkstra(){
    	mPriceQueue.clear();
    	mPriceQueue[0] = 0;
    	path[0] = intToStr(0);
    	for(int i = 1; i < vertexNum; i++){
    		mPriceQueue[i] = MAXPRICE; 
    		path[i] = ""; 
    	}
    	while(!mPriceQueue.empty()){
    		pair<int, int> pMinNode = getMinPriceVet();      // dijkstra的贪心策略就是该点就是此时的pMinNode.second就是最短路径
    		int minPriceVet = pMinNode.first;
    		minDistance[minPriceVet] = pMinNode.second;   //  保存最短路径到minDistance中
    		GNode *p = edges[minPriceVet].next;
    		while(p != NULL){
    			if(mPriceQueue.count(p->val) && (p->weight + pMinNode.second) < mPriceQueue[p->val]){  // 松弛操作
    				mPriceQueue[p->val] = p->weight + pMinNode.second;
    				path[p->val] = path[minPriceVet] + intToStr(p->val);
    			}
    			p = p->next;
    		}
    	}
    	// 输出路径
    	for(int i = 0; i < vertexNum; i++){
    		cout << "从0到" << i << "的最短路径为: " <<path[i] << "; 距离为: " << minDistance[i] << endl;
    	}
    }

结果：

![Center][]

时间复杂度分析：时间复杂度为V\*Textract min \+ E\*Trelax的时间。当Q为数组时，时间复杂的为O(V^2);当Q为二叉堆时，时间复杂度为O(Vlg(V+E));当Q为fibonacci堆时，时间复杂度为O(VlgV+E).

**Bellman-Ford算法**

Bellman-Ford的思想是将边按照一定的顺序，每条边都松弛V-1次。每条边都松弛V-1次，一定能使估算距离等于最短路径。

代码如下：

// BellmanFord 算法 
    // 按照边的顺序松弛每一条边 V-1次
    void Graph::bellmanFord(){
    	minDistance[0] = 0;
    	path[0] = intToStr(0);
    	for(int i = 1; i < vertexNum; i++){
    		minDistance[i] = MAXPRICE;
    		path[i] = "";
    	}
    	for(int m = 0; m < vertexNum-1; m++){
    		for(int i = 0; i < vertexNum; i++){
    			GNode *p = edges[i].next;
    			while(p != NULL){
    				if(minDistance[p->val] > p->weight + minDistance[i]){     // 松弛每一条边
    					minDistance[p->val] = minDistance[i] + p->weight;
    					path[p->val] = path[i] + intToStr(p->val);
    				}
    				p = p->next;
    			}
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){            // 检查是否存在负权回路
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			if(minDistance[p->val] > p->weight + minDistance[i]){
    				cout << "该图存在负权环" << endl;
    				return;
    			}
    			p = p->next;
    		}
    	}
    	// 输出路径
    	for(int i = 0; i < vertexNum; i++){
    		cout << "从0到" << i << "的最短路径为: " <<path[i] << "; 距离为: " << minDistance[i] << endl;
    	}
    }

结果如下：

![Center 1][]

时间复杂度为O(VE).

注意：这里有个应用就是差分约束系统。其实线性规划的一种特殊情况，即A矩阵每行只有一个1和-1，其它的都为0，这样可以构造约束图，求得最短路径就是差分约束系统的解。这里不详细介绍了。

**DAG图的单源最短路径**

算法的基本思想：求DAG图的拓扑排序，然后按照拓扑排序顶点的顺序对其每条边进行松弛操作即如果d\[v\] > d\[u\]+w\[u,v\]，则d\[v\] = d\[u\]+w\[u,v\]。其时间复杂度为O(V+E).

**二：全对最短路径问题**

求所有的两两顶点之间的最短路径，最基本的思想就是对所有的顶点都运用单源最短路径问题。这样当无权值时，时间复杂度为O(V^2+VE)<即V\*BFS>;权值非负为O(V^2lgV+VE)<即V\*Dijkstra>;一般情况为O(V^2\*E),此时对于稠密图E=O(V^2)，要达到O(V^4)了，效率会非常差，因此需要其他算法来改进时间复杂度。主要有三种方法，第一种为动态规划方法(结合矩阵乘法)；第二种为Floyd-Warshall算法；第三种为Johnson算法，对于稀疏图要优于Floy-Warshall算法。

动态规划方法

![Center 2][]

其中dijm表示从i到j的最短路径，其中最多经过m条边。此时当m=V-1即为我们要求的最短路径(对于无负权环)。D(1)=W为权重矩阵。即求D(V-1).

此外将(1)式看做某种新运算\*，那么每次都是求D(m) = D(m-1) \* W。

代码如下：

//  用动态规划求全最短路径
    void Graph::dpAllDistance(){
    	int Adj[maxSize][maxSize];
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			minAllDistance[i][j] = MAXPRICE; 
    			if(i == j) minAllDistance[i][j] = 0;         
    			AllPath[i][j] = intToStr(i);
    			Adj[i][j] = minAllDistance[i][j];
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			minAllDistance[i][p->val] = p->weight;     //  经过了1条边 即为权重
    			Adj[i][p->val] = p->weight;
    			AllPath[i][p->val] = AllPath[i][p->val] + intToStr(p->val);
    			p = p->next;
    		}
    	}
    
    	for(int m = 2; m < vertexNum; m++){     // 至多经过m条边
    		for(int i = 0; i < vertexNum; i++){
    			for(int j = 0; j < vertexNum; j++){
    				for(int k = 0; k < vertexNum; k++){
    					if(minAllDistance[i][j] > minAllDistance[i][k] + Adj[k][j]){
    						minAllDistance[i][j] = minAllDistance[i][k] + Adj[k][j];
    						AllPath[i][j] = AllPath[i][k] + intToStr(j);
    					}
    				}
    			}
    		}
    	}
    	// 检测是否存在负权环
    	for(int i = 0; i < vertexNum; i++){
    		if(minAllDistance[i][i] < 0){
    			cout << "该图存在负权环" << endl;
    			return;
    		}
    	}
    
    	// 将全对最短路径输出
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			cout << "从" <<i << "到" << j <<"的最短路径为: " << AllPath[i][j] << " 距离为: " << minAllDistance[i][j] << endl;
    		} 
    	}
    }

时间复杂度：此时时间复杂度仍然为O(V^4)，但是采用D(m) = D(m/2)\*D(m/2)来计算，时间复杂度即为O(V^3\*lgV).

![Center 3][]

Floyd-Warshall算法

定义dijk表示从i到j考虑了从中间节点\{1,2,3…k\}经过的所有情况的最小值。当k=n时，即考虑了所有顶点，此时即为最短路径。

代码如下：

// floyd-warshall算法求最短路径问题
    
    void Graph::floydWarshall(){
    	int Adj[maxSize][maxSize];
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			minAllDistance[i][j] = MAXPRICE; 
    			if(i == j) minAllDistance[i][j] = 0;         
    			AllPath[i][j] = intToStr(i);
    			Adj[i][j] = minAllDistance[i][j];
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			minAllDistance[i][p->val] = p->weight;     //  经过了1条边 即为权重
    			Adj[i][p->val] = p->weight;
    			AllPath[i][p->val] = AllPath[i][p->val] + intToStr(p->val);
    			p = p->next;
    		}
    	}
    	for(int k = 0; k < vertexNum; k++){
    		for(int i = 0; i < vertexNum; i++){
    			for(int j = 0; j < vertexNum; j++){
    				if(minAllDistance[i][j] > minAllDistance[i][k] + minAllDistance[k][j]){
    					minAllDistance[i][j] = minAllDistance[i][k] + minAllDistance[k][j];
    					AllPath[i][j] = AllPath[i][k] + AllPath[k][j];
    				}
    			}
    		}
    	}
    
    	// 检测是否存在负权环
    	for(int i = 0; i < vertexNum; i++){
    		if(minAllDistance[i][i] < 0){
    			cout << "该图存在负权环" << endl;
    			return;
    		}
    	}
    
    	// 将全对最短路径输出
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			cout << "从" <<i << "到" << j <<"的最短路径为: " << AllPath[i][j] << " 距离为: " << minAllDistance[i][j] << endl;
    		} 
    	}

结果如下：

![Center 4][]

时间复杂度为O(V^3).

Johnson算法

Johnson算法采用了重赋权重方法。重赋权重方法不会改变最短路径，此外通过重赋权重产生的都是非负权重。

伪代码：

![Center 5][]

时间复杂度为O(V)\*Dijkstra算法。

算法全部代码：

graph.h

#ifndef GRAPH_H
    #define GRAPH_H
    #include <iostream>
    #include <vector>
    #include <map>
    #include <utility>
    #include <string>
    using namespace std;
    #define maxSize 10
    #define MAXPRICE 0x10000000  // 将此值设为权值的最大值
    
    
    struct GNode{
    	int val;
    	int weight;
    	GNode *next;
    };
    class Graph{
    public:
    	void createGraph(int n, int e);
    	void destroyGraph(GNode *p);
    	~Graph(){
    		for(int i = 0; i < vertexNum; i++){
    			destroyGraph(edges[i].next);
    			//cout << "析构:" << i << endl;
    		}
    	}
    	void showGraph();
    	void bfsTravel();      // 广度遍历
    	void dfsTravel();      // 深度遍历
    	void showTopSort();   //  输出拓扑序列
    	void componentSC();      // 建立图g的强连通分量
    
    	void prim();
    	void dijkstra();
    	void bellmanFord();
    
    	// 动态规划求全最短路径
    	void dpAllDistance();
    
    	void floydWarshall();
    
    
    private:
    	int vertex[maxSize];      // 存放顶点
    	GNode edges[maxSize];    //  存放邻接表
    	int vertexNum;        //顶点个数
    	int edgesNum;          //边条数
    
    	//bfs and dfs 遍历
    	int visited[maxSize];
    	void bfs(int s);
    	void dfs(int s);
    	int beginTime[maxSize];       // 深度开始访问x的时间
    	int endTime[maxSize];          // 结束访问x的时间
    	static int time;
    	vector<int> topSort;      // topSort的逆序为有向无回路的拓扑排序  
    	void buildTransGraph(Graph &g);   // 建立图g的转置
    
    	// prim
    	map<int, int> mPriceQueue;    // 保存最小生成树和dijkstra算法的造价
    	pair<int, int> getMinPriceVet();
    	int preNode[maxSize];      // 用于保存最小生成树的边 值为该结点的前驱
    
    	// 保存dijkstra 路径
    	string path[maxSize];        // 保存最终的最短路径
    	int minDistance[maxSize];   // 保存最终的最小距离
    	string intToStr(int i);
    
    	// 保存所有路劲 
    	int minAllDistance[maxSize][maxSize];
    	string AllPath[maxSize][maxSize];	
    };
    
    #endif

graph.cpp

#include <iostream>
    #include "graph.h"
    #include <queue>
    #include <algorithm>
    #include <sstream>
    using namespace std;
    
    
    int Graph::time = 0;
    void Graph::createGraph(int n, int e){
    	vertexNum = n;
    	edgesNum = e;
    	for(int i = 0; i < n; i++){
    		vertex[i] = i;
    		edges[i].val = i;
    		edges[i].weight = 0;
    		edges[i].next = NULL;
    	}
    
    	for(int i = 0; i < e; i++){
    		int source, dest, wei;
    		cin >> source >> dest >> wei;
    		GNode *newNode = new GNode();
    		newNode->val = dest;
    		newNode->weight = wei;
    		newNode ->next = NULL;
    		GNode *p = &edges[source];
    		while(p->next != NULL) p = p->next;
    		p->next = newNode;
    
    		//  无向图     有向图就将这段删除掉
    		/*GNode *newNode2 = new GNode();
    		newNode2->val = source;
    		newNode2->weight = wei;
    		newNode2 ->next = NULL;
    		GNode *p2 = &edges[dest];
    		while(p2->next != NULL) p2 = p2->next;
    		p2->next = newNode2;*/
    
    
    	}
    }
    
    void Graph::destroyGraph(GNode *p){
    	if(p == NULL) return;
    	else{
    		destroyGraph(p->next);
    		delete p;
    	}
    }
    
    void Graph::showGraph(){
    	for(int i = 0; i < vertexNum; i++){
    		int j = i;
    		cout << i << "->";
    		GNode *p = edges[j].next;
    		while( p != NULL) {
    			cout << "(" << p->val <<"," << p->weight << ")" ;
    			p = p->next;
    		}
    		cout << endl;
    	}
    }
    
    // 广度遍历图
    void Graph::bfs(int s){
    	queue<int> q;
    	q.push(s);
    	visited[s] = 1;
    	while(!q.empty()){
    		int u = q.front();
    		q.pop();
    		cout << u <<" ";
    		GNode *p = edges[u].next;
    		while(p != NULL){
    			if(!visited[p->val]){    // 未被访问，则将其加入队列中并标志为访问过
    				q.push(p->val);
    				visited[p->val] = 1;
    			}
    			p = p->next;
    		}
    	}
    
    }
    
    void Graph::bfsTravel(){
    	memset(visited, 0, sizeof(int)*vertexNum);
    	for(int i = 0; i < vertexNum; i++){
    		if(!visited[i]){
    			bfs(i);
    			cout << endl;
    		}
    	}
    }
    
    // 深度优先遍历
    void Graph::dfs(int s){
    	visited[s] = 1;
    	time += 1;
    	beginTime[s] = time;
    	cout << s << "(" << beginTime[s] << " ";              // shen
    	GNode *p = edges[s].next;
    	while(p != NULL){
    		if(!visited[p->val])
    			dfs(p->val);
    		p = p->next;
    	}
    	time += 1;
    	endTime[s] = time;
    	topSort.push_back(s);
    	cout << endTime[s] << ")" <<" ";
    }
    
    void Graph::dfsTravel(){
    	memset(visited, 0, sizeof(int)*vertexNum);
    	memset(beginTime, 0, sizeof(int)*vertexNum);  // 结点开始访问的时间
    	memset(endTime, 0, sizeof(int)*vertexNum);    // 结点结束访问的时间
    	for(int i = 0; i < vertexNum; i++){
    		if(!visited[i]){
    			dfs(i);
    			cout << endl;
    		}
    	}
    }
    
    //  输出拓扑排序
    void Graph::showTopSort(){
    	for(vector<int>::reverse_iterator iter = topSort.rbegin(); iter != topSort.rend(); iter ++)
    		cout << *iter << " ";
    	cout << endl;
    }
    
    // 创建图g的转置
    void Graph::buildTransGraph(Graph &g){
    	this->vertexNum = g.vertexNum;
    	this->edgesNum = g.edgesNum;
    	for(int i = 0; i < vertexNum; i++){
    		this->vertex[i] = g.vertex[i];
    		this->edges[i].val = g.edges[i].val;
    		this->edges[i].weight = g.edges[i].weight;
    		this->edges[i].next = NULL;
    	}
    
    	for(int i = 0; i < vertexNum; i++){
    		GNode *p = g.edges[i].next;
    		while(p != NULL){
    			GNode *newNode = new GNode();
    			newNode->val = i;
    			newNode->next = NULL;
    			newNode->weight = p->weight;
    			GNode *q = &edges[p->val];
    			while(q->next != NULL) q = q->next;
    			q->next = newNode;
    			p = p->next;
    		} 
    	}
    }
    
    //强连通分量
    void Graph::componentSC(){
    	//time = 0;
    	//dfsTravel();              // 对图g进行深度搜索得到完成x访问所需要的时间 并由此得到其拓扑排序
    	Graph g2;
    	g2.buildTransGraph(*this);        // 得到图G的转置
    	time = 0;
    	memset(g2.visited, 0, sizeof(int)*vertexNum);
    	cout << "强连通分量: " << endl;
    	for(vector<int>::reverse_iterator iter = topSort.rbegin(); iter != topSort.rend(); iter++){  // 对转置图g2进行深度搜索得到强连通分量
    		if(!g2.visited[*iter])
    			g2.dfs(*iter);
    	}
    	cout << endl;
    }
    
    // 最小生成树
    /*最小生成树采用的是贪心算法：设T是图G的MST(minimum spanning tree), 
    A属于V， (u,v)是连接A与V-A之间的一条边且权值最小，则(u,v)边属于最小生成树
    lowcost存放代价*/
    
    pair<int, int> Graph::getMinPriceVet(){
    	pair<int, int> p;
    	int minPrice = MAXPRICE;
    	int minPriceVet = 0;
    	for(map<int, int>::iterator iter = mPriceQueue.begin(); iter != mPriceQueue.end(); iter++){
    		if((*iter).second < minPrice){
    			minPrice = (*iter).second;
    			minPriceVet = (*iter).first;
    			p = *iter;
    		}
    	}
    	mPriceQueue.erase(minPriceVet);         // 或者采用数组 此时就需要设置visited标记数组来判断该顶点是否已经加入S中了
    	return p;
    }
    
    void Graph::prim(){
    	mPriceQueue.clear();
    	mPriceQueue[0] = 0;
    	for(int i = 1; i < vertexNum; i++){
    		mPriceQueue[i] = MAXPRICE; 
    	}
    	while(!mPriceQueue.empty()){
    		pair<int, int> pMinNode = getMinPriceVet();
    		int minPriceVet = pMinNode.first;
    		GNode *p = edges[minPriceVet].next;
    		while(p != NULL){
    			if(mPriceQueue.count(p->val) && p->weight < mPriceQueue[p->val]){
    				mPriceQueue[p->val] = p->weight;
    				preNode[p->val] = minPriceVet;
    			}
    			p = p->next;
    		}
    	}
    	// 展示最小生成树的边
    	for(int i = 1; i < vertexNum; i++)
    	{
    		cout << "(" << preNode[i] << "," << i << ") ";
    	}
    	cout << endl;
    }
    
    
    
    
    // dijkstra 算法
    
    string Graph::intToStr(int i){
    	string s;
    	stringstream ss;
    	ss << i;
    	ss >>s;
    	ss.clear();
    	return s;
    }
    void Graph::dijkstra(){
    	mPriceQueue.clear();
    	mPriceQueue[0] = 0;
    	path[0] = intToStr(0);
    	for(int i = 1; i < vertexNum; i++){
    		mPriceQueue[i] = MAXPRICE; 
    		path[i] = ""; 
    	}
    	while(!mPriceQueue.empty()){
    		pair<int, int> pMinNode = getMinPriceVet();      // dijkstra的贪心策略就是该点就是此时的pMinNode.second就是最短路径
    		int minPriceVet = pMinNode.first;
    		minDistance[minPriceVet] = pMinNode.second;   //  保存最短路径到minDistance中
    		GNode *p = edges[minPriceVet].next;
    		while(p != NULL){
    			if(mPriceQueue.count(p->val) && (p->weight + pMinNode.second) < mPriceQueue[p->val]){  // 松弛操作
    				mPriceQueue[p->val] = p->weight + pMinNode.second;
    				path[p->val] = path[minPriceVet] + intToStr(p->val);
    			}
    			p = p->next;
    		}
    	}
    	// 输出路径
    	for(int i = 0; i < vertexNum; i++){
    		cout << "从0到" << i << "的最短路径为: " <<path[i] << "; 距离为: " << minDistance[i] << endl;
    	}
    }
    
    
    // BellmanFord 算法 
    // 按照边的顺序松弛每一条边 V-1次
    void Graph::bellmanFord(){
    	minDistance[0] = 0;
    	path[0] = intToStr(0);
    	for(int i = 1; i < vertexNum; i++){
    		minDistance[i] = MAXPRICE;
    		path[i] = "";
    	}
    	for(int m = 0; m < vertexNum-1; m++){
    		for(int i = 0; i < vertexNum; i++){
    			GNode *p = edges[i].next;
    			while(p != NULL){
    				if(minDistance[p->val] > p->weight + minDistance[i]){     // 松弛每一条边
    					minDistance[p->val] = minDistance[i] + p->weight;
    					path[p->val] = path[i] + intToStr(p->val);
    				}
    				p = p->next;
    			}
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){            // 检查是否存在负权回路
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			if(minDistance[p->val] > p->weight + minDistance[i]){
    				cout << "该图存在负权环" << endl;
    				return;
    			}
    			p = p->next;
    		}
    	}
    	// 输出路径
    	for(int i = 0; i < vertexNum; i++){
    		cout << "从0到" << i << "的最短路径为: " <<path[i] << "; 距离为: " << minDistance[i] << endl;
    	}
    }
    
    //  用动态规划求全最短路径
    void Graph::dpAllDistance(){
    	int Adj[maxSize][maxSize];
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			minAllDistance[i][j] = MAXPRICE; 
    			if(i == j) minAllDistance[i][j] = 0;         
    			AllPath[i][j] = intToStr(i);
    			Adj[i][j] = minAllDistance[i][j];
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			minAllDistance[i][p->val] = p->weight;     //  经过了1条边 即为权重
    			Adj[i][p->val] = p->weight;
    			AllPath[i][p->val] = AllPath[i][p->val] + intToStr(p->val);
    			p = p->next;
    		}
    	}
    
    	for(int m = 2; m < vertexNum; m++){     // 至多经过m条边
    		for(int i = 0; i < vertexNum; i++){
    			for(int j = 0; j < vertexNum; j++){
    				for(int k = 0; k < vertexNum; k++){
    					if(minAllDistance[i][j] > minAllDistance[i][k] + Adj[k][j]){
    						minAllDistance[i][j] = minAllDistance[i][k] + Adj[k][j];
    						AllPath[i][j] = AllPath[i][k] + intToStr(j);
    					}
    				}
    			}
    		}
    	}
    	// 检测是否存在负权环
    	for(int i = 0; i < vertexNum; i++){
    		if(minAllDistance[i][i] < 0){
    			cout << "该图存在负权环" << endl;
    			return;
    		}
    	}
    
    	// 将全对最短路径输出
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			cout << "从" <<i << "到" << j <<"的最短路径为: " << AllPath[i][j] << " 距离为: " << minAllDistance[i][j] << endl;
    		} 
    	}
    }
    
    // floyd-warshall算法求最短路径问题
    
    void Graph::floydWarshall(){
    	int Adj[maxSize][maxSize];
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			minAllDistance[i][j] = MAXPRICE; 
    			if(i == j) minAllDistance[i][j] = 0;         
    			AllPath[i][j] = intToStr(i);
    			Adj[i][j] = minAllDistance[i][j];
    		}
    	}
    	for(int i = 0; i < vertexNum; i++){
    		GNode *p = edges[i].next;
    		while(p != NULL){
    			minAllDistance[i][p->val] = p->weight;     //  经过了1条边 即为权重
    			Adj[i][p->val] = p->weight;
    			AllPath[i][p->val] = AllPath[i][p->val] + intToStr(p->val);
    			p = p->next;
    		}
    	}
    	for(int k = 0; k < vertexNum; k++){
    		for(int i = 0; i < vertexNum; i++){
    			for(int j = 0; j < vertexNum; j++){
    				if(minAllDistance[i][j] > minAllDistance[i][k] + minAllDistance[k][j]){
    					minAllDistance[i][j] = minAllDistance[i][k] + minAllDistance[k][j];
    					AllPath[i][j] = AllPath[i][k] + AllPath[k][j];
    				}
    			}
    		}
    	}
    
    	// 检测是否存在负权环
    	for(int i = 0; i < vertexNum; i++){
    		if(minAllDistance[i][i] < 0){
    			cout << "该图存在负权环" << endl;
    			return;
    		}
    	}
    
    	// 将全对最短路径输出
    	for(int i = 0; i < vertexNum; i++){
    		for(int j = 0; j < vertexNum; j++){
    			cout << "从" <<i << "到" << j <<"的最短路径为: " << AllPath[i][j] << " 距离为: " << minAllDistance[i][j] << endl;
    		} 
    	}
    
    }

[Center]: /images/20220805/f60de03e65254317b793da9f9f09f052.png
[Center 1]: /images/20220805/93b7703cf1ca421686006cb0ca68a145.png
[Center 2]: /images/20220805/516833ce4e02498fa5b67d4363450a40.png
[Center 3]: /images/20220805/4a62621b1042464a980548f94075918f.png
[Center 4]: /images/20220805/0d608b1979954221abbad10c628a36fc.png
[Center 5]: /images/20220805/5531645076014ccabe7d57973744a969.png