有权图的最短路径算法

目录

单源最短路径问题

Dijkstra算法

原理 ​

获得最短路径长度的Dijkstra代码实现

时间复杂度

算法优化

优先队列优化后的代码实现

时间复杂度

可以具体获得最短路径的Dijkstra代码实现 

Bellman-Ford算法

原理

代码实现 

Floyed算法

原理

代码实现 

单源最短路径问题

我们的起始点是固定点，从起始点出发到达其他各顶点的最短路径。

Dijkstra算法

此算法不能处理负权边，由于大量的应用不依赖负权边，所以这个算法有非常广泛的应用。

原理 

获得最短路径长度的Dijkstra代码实现

import java.util.Arrays;

public class Dijkstra {

    private WeightedGraph G;
    private int s;//源点s
    private int[] dis;//整型数组表示源点s到某个顶点的距离
    private boolean[] visited;//找到还没确定最短距离的顶点

    public Dijkstra(WeightedGraph G, int s){

        this.G = G;

        G.validateVertex(s);//验证合法性
        this.s = s;

        dis = new int[G.V()];
        Arrays.fill(dis, Integer.MAX_VALUE);//赋初值
        dis[s] = 0;//赋初值为0

        visited = new boolean[G.V()];

        while(true){
            //循环的第一轮找到的必是源点s
            int cur = -1;//最小的dis值对应的顶点是谁 
            int curdis = Integer.MAX_VALUE;//当前找到的最小的dis值
            for(int v = 0; v < G.V(); v ++)
                if(!visited[v] && dis[v] < curdis){
                    curdis = dis[v];
                    cur = v;
                }

            if(cur == -1) break;//代表当前所有的顶点都访问过了，可以退出咯

            visited[cur] = true;//哪些顶点的dis值已经求出来了
            for(int w: G.adj(cur))
                if(!visited[w]){
                    if(dis[cur] + G.getWeight(cur, w) < dis[w])
                        dis[w] = dis[cur] + G.getWeight(cur, w);
                }
        }
    }

    public boolean isConnectedTo(int v){//判断顶点和源点的连通性

        G.validateVertex(v);
        return visited[v];
    }

    public int distTo(int v){//从源点s到顶点v对应的最短路径的长度

        G.validateVertex(v);
        return dis[v];
    }

    static public void main(String[] args){

        WeightedGraph g = new WeightedGraph("g.txt");
        Dijkstra dij = new Dijkstra(g, 0);
        for(int v = 0; v < g.V(); v ++)
            System.out.print(dij.distTo(v) + " ");
        System.out.println();
    }
}

时间复杂度

算法优化

我们可以用优先队列获得v这个顶点对应的dis值，不再是v这个顶点序号的最小值了。我们的优先队列取出来的是顶点的序号，但比较起来是比较的dis值。

优先队列优化后的代码实现

import java.util.Arrays;
import java.util.PriorityQueue;


public class Dijkstra {

    private WeightedGraph G;
    private int s;
    private int[] dis;
    private boolean[] visited;

    private class Node implements Comparable<Node>{

        public int v, dis;

        public Node(int v, int dis){
            this.v = v;
            this.dis = dis;
        }

        @Override
        public int compareTo(Node another){
            return dis - another.dis;
        }
    }

    public Dijkstra(WeightedGraph G, int s){

        this.G = G;

        G.validateVertex(s);
        this.s = s;

        dis = new int[G.V()];
        Arrays.fill(dis, Integer.MAX_VALUE);
        dis[s] = 0;

        visited = new boolean[G.V()];

        PriorityQueue<Node> pq = new PriorityQueue<Node>();
        pq.add(new Node(s, 0));
        while(!pq.isEmpty()){

            int cur = pq.remove().v;

            if(visited[cur]) continue;

            visited[cur] = true;
            for(int w: G.adj(cur))
                if(!visited[w]){
                    if(dis[cur] + G.getWeight(cur, w) < dis[w]){
                        dis[w] = dis[cur] + G.getWeight(cur, w);
                        pq.add(new Node(w, dis[w]));
                    }
                }
        }
    }

    public boolean isConnectedTo(int v){

        G.validateVertex(v);
        return visited[v];
    }

    public int distTo(int v){

        G.validateVertex(v);
        return dis[v];
    }

    static public void main(String[] args){

        WeightedGraph g = new WeightedGraph("g.txt");
        Dijkstra dij = new Dijkstra(g, 0);
        for(int v = 0; v < g.V(); v ++)
            System.out.print(dij.distTo(v) + " ");
        System.out.println();
    }
}

时间复杂度

可以具体获得最短路径的Dijkstra代码实现 

import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.PriorityQueue;


public class Dijkstra {

    private WeightedGraph G;
    private int s;
    private int[] dis;
    private boolean[] visited;
    private int[] pre;

    private class Node implements Comparable<Node>{

        public int v, dis;

        public Node(int v, int dis){
            this.v = v;
            this.dis = dis;
        }

        @Override
        public int compareTo(Node another){
            return dis - another.dis;
        }
    }

    public Dijkstra(WeightedGraph G, int s){

        this.G = G;

        G.validateVertex(s);
        this.s = s;

        dis = new int[G.V()];
        Arrays.fill(dis, Integer.MAX_VALUE);

        pre = new int[G.V()];
        Arrays.fill(pre, -1);

        dis[s] = 0;
        pre[s] = s;
        visited = new boolean[G.V()];

        PriorityQueue<Node> pq = new PriorityQueue<Node>();
        pq.add(new Node(s, 0));
        while(!pq.isEmpty()){

            int cur = pq.remove().v;

            if(visited[cur]) continue;

            visited[cur] = true;
            for(int w: G.adj(cur))
                if(!visited[w]){
                    if(dis[cur] + G.getWeight(cur, w) < dis[w]){
                        dis[w] = dis[cur] + G.getWeight(cur, w);
                        pq.add(new Node(w, dis[w]));
                        pre[w] = cur;
                    }
                }
        }
    }

    public boolean isConnectedTo(int v){

        G.validateVertex(v);
        return visited[v];
    }

    public int distTo(int v){

        G.validateVertex(v);
        return dis[v];
    }

    public Iterable<Integer> path(int t){

        ArrayList<Integer> res = new ArrayList<>();
        if(!isConnectedTo(t)) return res;

        int cur = t;
        while(cur != s){
            res.add(cur);
            cur = pre[cur];
        }
        res.add(s);

        Collections.reverse(res);
        return res;
    }

    static public void main(String[] args){

        WeightedGraph g = new WeightedGraph("g.txt");
        Dijkstra dij = new Dijkstra(g, 0);
        for(int v = 0; v < g.V(); v ++)
            System.out.print(dij.distTo(v) + " ");
        System.out.println();

        System.out.println(dij.path(3));
    }
}

Bellman-Ford算法

原理

松弛操作有方向性，相当于拐个弯到达某个端点是不是比直接到达某个端点更近。此算法在有向图无向图也成立。

代码实现 

import java.util.Arrays;

public class BellmanFord {

    private WeightedGraph G;
    private int s;
    private int[] dis;
    private boolean hasNegCycle = false;

    public BellmanFord(WeightedGraph G, int s){

        this.G = G;

        G.validateVertex(s);
        this.s = s;

        dis = new int[G.V()];
        Arrays.fill(dis, Integer.MAX_VALUE);
        dis[s] = 0;

        for(int pass = 1; pass < G.V(); pass ++){
            for(int v = 0; v < G.V(); v ++)
                for(int w: G.adj(v))
                    if(dis[v] != Integer.MAX_VALUE &amp;&amp;
                       dis[v] + G.getWeight(v, w) < dis[w])
                        dis[w] = dis[v] + G.getWeight(v, w);
        }

        for(int v = 0; v < G.V(); v ++)
            for(int w : G.adj(v))
                if(dis[v] != Integer.MAX_VALUE &amp;&amp;
                   dis[v] + G.getWeight(v, w) < dis[w])
                    hasNegCycle = true;
    }

    public boolean hasNegativeCycle(){
        return hasNegCycle;
    }

    public boolean isConnectedTo(int v){
        G.validateVertex(v);
        return dis[v] != Integer.MAX_VALUE;
    }

    public int distTo(int v){
        G.validateVertex(v);
        if(hasNegCycle) throw new RuntimeException("exist negative cycle.");
        return dis[v];
    }

    static public void main(String[] args){

        WeightedGraph g = new WeightedGraph("g.txt");
        BellmanFord bf = new BellmanFord(g, 0);
        if(!bf.hasNegativeCycle()){
            for(int v = 0; v < g.V(); v ++)
                System.out.print(bf.distTo(v) + " ");
            System.out.println();
        }
        else
            System.out.println("exist negative cycle.");

        WeightedGraph g2 = new WeightedGraph("g2.txt");
        BellmanFord bf2 = new BellmanFord(g2, 0);
        if(!bf2.hasNegativeCycle()){
            for(int v = 0; v < g2.V(); v ++)
                System.out.print(bf2.distTo(v) + " ");
            System.out.println();
        }
        else
            System.out.println("exist negative cycle.");
    }
}

Floyed算法

原理

可以包含负权边，也可以包含负权环。 

代码实现 

import java.util.Arrays;

public class Floyed {

    private WeightedGraph G;
    private int[][] dis;
    private boolean hasNegCycle = false;

    public Floyed(WeightedGraph G){

        this.G = G;

        dis = new int[G.V()][G.V()];
        for(int v = 0; v < G.V(); v ++)
            Arrays.fill(dis[v], Integer.MAX_VALUE);

        for(int v = 0; v < G.V(); v ++){
            dis[v][v] = 0;
            for(int w: G.adj(v))
                dis[v][w] = G.getWeight(v, w);
        }

        for(int t = 0; t < G.V(); t ++)
            for(int v = 0; v < G.V(); v ++)
                for(int w = 0; w < G.V(); w ++)
                    if(dis[v][t] != Integer.MAX_VALUE &amp;&amp; dis[t][w] != Integer.MAX_VALUE
                       &amp;&amp; dis[v][t] + dis[t][w] < dis[v][w])
                        dis[v][w] = dis[v][t] + dis[t][w];

        for(int v = 0; v < G.V(); v ++)
            if(dis[v][v] < 0)
                hasNegCycle = true;
    }

    public boolean hasNegativeCycle(){
        return hasNegCycle;
    }

    public boolean isConnectedTo(int v, int w){
        G.validateVertex(v);
        G.validateVertex(w);
        return dis[v][w] != Integer.MAX_VALUE;
    }

    public int distTo(int v, int w){
        G.validateVertex(v);
        G.validateVertex(w);
        return dis[v][w];
    }

    static public void main(String[] args){

        WeightedGraph g = new WeightedGraph("g.txt");
        Floyed floyed = new Floyed(g);
        if(!floyed.hasNegativeCycle()){
            for(int v = 0; v < g.V(); v ++){
                for(int w = 0; w < g.V(); w ++)
                    System.out.print(floyed.distTo(v, w) + " ");
                System.out.println();
            }
        }
        else
            System.out.println("exist negative cycle.");

        WeightedGraph g2 = new WeightedGraph("g2.txt");
        Floyed floyed2 = new Floyed(g2);
        if(!floyed2.hasNegativeCycle()){
            for(int v = 0; v < g.V(); v ++){
                for(int w = 0; w < g.V(); w ++)
                    System.out.print(floyed2.distTo(v, w) + " ");
                System.out.println();
            }
        }
        else
            System.out.println("exist negative cycle.");
    }
}

代码007普通

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