使用堆栈来跟踪节点

Stack<Node> s;

s.prepend(tree.head);

while(!s.empty) {
    Node n = s.poll_front // gets first node

    // do something with q?

    for each child of n: s.prepend(child)

}

DFS:

list nodes_to_visit = {root};
while( nodes_to_visit isn't empty ) {
  currentnode = nodes_to_visit.take_first();
  nodes_to_visit.prepend( currentnode.children );
  //do something
}

BFS:

list nodes_to_visit = {root};
while( nodes_to_visit isn't empty ) {
  currentnode = nodes_to_visit.take_first();
  nodes_to_visit.append( currentnode.children );
  //do something
}

两者的对称相当酷。

更新:如前所述，take_first()删除并返回列表中的第一个元素。

你可以使用一个堆栈来保存尚未访问的节点:

stack.push(root)
while !stack.isEmpty() do
    node = stack.pop()
    for each node.childNodes do
        stack.push(stack)
    endfor
    // …
endwhile

如果你有指向父节点的指针，你可以在没有额外内存的情况下完成。

def dfs(root):
    node = root
    while True:
        visit(node)
        if node.first_child:
            node = node.first_child      # walk down
        else:
            while not node.next_sibling:
                if node is root:
                    return
                node = node.parent       # walk up ...
            node = node.next_sibling     # ... and right

注意，如果子节点存储为数组而不是通过兄弟指针，那么下一个兄弟节点可以通过以下方式找到:

def next_sibling(node):
    try:
        i =    node.parent.child_nodes.index(node)
        return node.parent.child_nodes[i+1]
    except (IndexError, AttributeError):
        return None

http://www.youtube.com/watch?v=zLZhSSXAwxI

刚刚看了这个视频，并提出了实施方案。这对我来说似乎很容易理解。请评论一下。

visited_node={root}
stack.push(root)
while(!stack.empty){
  unvisited_node = get_unvisited_adj_nodes(stack.top());
  If (unvisited_node!=null){
     stack.push(unvisited_node);  
     visited_node+=unvisited_node;
  }
  else
     stack.pop()
}

PreOrderTraversal is same as DFS in binary tree. You can do the same recursion 
taking care of Stack as below.

    public void IterativePreOrder(Tree root)
            {
                if (root == null)
                    return;
                Stack s<Tree> = new Stack<Tree>();
                s.Push(root);
                while (s.Count != 0)
                {
                    Tree b = s.Pop();
                    Console.Write(b.Data + " ");
                    if (b.Right != null)
                        s.Push(b.Right);
                    if (b.Left != null)
                        s.Push(b.Left);

                }
            }

一般的逻辑是，将一个节点(从根开始)推入Stack, Pop()它和Print()值。然后，如果它有子节点(左和右)，将它们推入堆栈-先推右，这样你就会先访问左子节点(在访问节点本身之后)。当stack为空()时，您将访问Pre-Order中的所有节点。

你可以使用堆栈。我用邻接矩阵实现了图:

void DFS(int current){
    for(int i=1; i<N; i++) visit_table[i]=false;
    myStack.push(current);
    cout << current << "  ";
    while(!myStack.empty()){
        current = myStack.top();
        for(int i=0; i<N; i++){
            if(AdjMatrix[current][i] == 1){
                if(visit_table[i] == false){ 
                    myStack.push(i);
                    visit_table[i] = true;
                    cout << i << "  ";
                }
                break;
            }
            else if(!myStack.empty())
                myStack.pop();
        }
    }
}

虽然“使用堆栈”可能是人为的面试问题的答案，但实际上，它只是显式地做递归程序在幕后所做的事情。

递归使用程序内置堆栈。当你调用一个函数时，它将函数的参数推入堆栈，当函数返回时，它通过弹出程序堆栈来执行。

假设您希望在访问图中的每个节点时执行通知。简单的递归实现是:

void DFSRecursive(Node n, Set<Node> visited) {
  visited.add(n);
  for (Node x : neighbors_of(n)) {  // iterate over all neighbors
    if (!visited.contains(x)) {
      DFSRecursive(x, visited);
    }
  }
  OnVisit(n);  // callback to say node is finally visited, after all its non-visited neighbors
}

好的，现在你需要一个基于堆栈的实现，因为你的例子不起作用。例如，复杂的图形可能会导致程序的堆栈崩溃，您需要实现一个非递归版本。最大的问题是知道何时发出通知。

下面的伪代码可以工作(为了可读性，Java和c++混合使用):

void DFS(Node root) {
  Set<Node> visited;
  Set<Node> toNotify;  // nodes we want to notify

  Stack<Node> stack;
  stack.add(root);
  toNotify.add(root);  // we won't pop nodes from this until DFS is done
  while (!stack.empty()) {
    Node current = stack.pop();
    visited.add(current);
    for (Node x : neighbors_of(current)) {
      if (!visited.contains(x)) {
        stack.add(x);
        toNotify.add(x);
      }
    }
  }
  // Now issue notifications. toNotifyStack might contain duplicates (will never
  // happen in a tree but easily happens in a graph)
  Set<Node> notified;
  while (!toNotify.empty()) {
  Node n = toNotify.pop();
  if (!toNotify.contains(n)) {
    OnVisit(n);  // issue callback
    toNotify.add(n);
  }
}

它看起来很复杂，但发出通知所需的额外逻辑存在，因为您需要以相反的访问顺序通知- DFS从根开始，但在最后通知它，不像BFS实现非常简单。

看看下面的图表: 节点是s t v w。 有向边为: S ->t, S ->v, t->w, v->w, v->t。 运行你自己的DFS实现，访问节点的顺序必须是: W t v s 一个笨拙的DFS实现可能会首先通知t，这表明存在错误。DFS的递归实现总是最后到达w。

Java中的DFS迭代:

//DFS: Iterative
private Boolean DFSIterative(Node root, int target) {
    if (root == null)
        return false;
    Stack<Node> _stack = new Stack<Node>();
    _stack.push(root);
    while (_stack.size() > 0) {
        Node temp = _stack.peek();
        if (temp.data == target)
            return true;
        if (temp.left != null)
            _stack.push(temp.left);
        else if (temp.right != null)
            _stack.push(temp.right);
        else
            _stack.pop();
    }
    return false;
}

使用Stack，以下是要遵循的步骤:

如果可能，访问一个相邻的未访问顶点，标记它， 然后把它推到堆栈上。 如果您不能遵循第1步，那么，如果可能的话，弹出一个顶点 堆栈。 如果你不能遵循第1步或第2步，你就完了。

下面是执行上述步骤的Java程序:

public void searchDepthFirst() {
    // begin at vertex 0
    vertexList[0].wasVisited = true;
    displayVertex(0);
    stack.push(0);
    while (!stack.isEmpty()) {
        int adjacentVertex = getAdjacentUnvisitedVertex(stack.peek());
        // if no such vertex
        if (adjacentVertex == -1) {
            stack.pop();
        } else {
            vertexList[adjacentVertex].wasVisited = true;
            // Do something
            stack.push(adjacentVertex);
        }
    }
    // stack is empty, so we're done, reset flags
    for (int j = 0; j < nVerts; j++)
            vertexList[j].wasVisited = false;
}

使用ES6生成器的非递归DFS

class Node {
  constructor(name, childNodes) {
    this.name = name;
    this.childNodes = childNodes;
    this.visited = false;
  }
}

function *dfs(s) {
  let stack = [];
  stack.push(s);
  stackLoop: while (stack.length) {
    let u = stack[stack.length - 1]; // peek
    if (!u.visited) {
      u.visited = true; // grey - visited
      yield u;
    }

    for (let v of u.childNodes) {
      if (!v.visited) {
        stack.push(v);
        continue stackLoop;
      }
    }

    stack.pop(); // black - all reachable descendants were processed 
  }    
}

它与典型的非递归DFS不同，可以很容易地检测给定节点的所有可达后代何时被处理，并维护列表/堆栈中的当前路径。

基于biziclops的ES6实现很棒的答案:

root = { text: "root", children: [{ text: "c1", children: [{ text: "c11" }, { text: "c12" }] }, { text: "c2", children: [{ text: "c21" }, { text: "c22" }] }, ] } console.log("DFS:") DFS(root, node => node.children, node => console.log(node.text)); console.log("BFS:") BFS(root, node => node.children, node => console.log(node.text)); function BFS(root, getChildren, visit) { let nodesToVisit = [root]; while (nodesToVisit.length > 0) { const currentNode = nodesToVisit.shift(); nodesToVisit = [ ...nodesToVisit, ...(getChildren(currentNode) || []), ]; visit(currentNode); } } function DFS(root, getChildren, visit) { let nodesToVisit = [root]; while (nodesToVisit.length > 0) { const currentNode = nodesToVisit.shift(); nodesToVisit = [ ...(getChildren(currentNode) || []), ...nodesToVisit, ]; visit(currentNode); } }

Stack<Node> stack = new Stack<>();
stack.add(root);
while (!stack.isEmpty()) {
    Node node = stack.pop();
    System.out.print(node.getData() + " ");

    Node right = node.getRight();
    if (right != null) {
        stack.push(right);
    }

    Node left = node.getLeft();
    if (left != null) {
        stack.push(left);
    }
}

伪代码基于@biziclop的答案:

只使用基本结构:变量、数组、if、while和for 函数getNode(id)和getChildren(id) 假设已知节点数N

注意:我从1开始使用数组索引，而不是0。

广度优先

S = Array(N)
S[1] = 1; // root id
cur = 1;
last = 1
while cur <= last
    id = S[cur]
    node = getNode(id)
    children = getChildren(id)

    n = length(children)
    for i = 1..n
        S[ last+i ] = children[i]
    end
    last = last+n
    cur = cur+1

    visit(node)
end

深度优先

S = Array(N)
S[1] = 1; // root id
cur = 1;
while cur > 0
    id = S[cur]
    node = getNode(id)
    children = getChildren(id)

    n = length(children)
    for i = 1..n
        // assuming children are given left-to-right
        S[ cur+i-1 ] = children[ n-i+1 ] 

        // otherwise
        // S[ cur+i-1 ] = children[i] 
    end
    cur = cur+n-1

    visit(node)
end

完整的示例工作代码，没有堆栈:

import java.util.*;

class Graph {
private List<List<Integer>> adj;

Graph(int numOfVertices) {
    this.adj = new ArrayList<>();
    for (int i = 0; i < numOfVertices; ++i)
        adj.add(i, new ArrayList<>());
}

void addEdge(int v, int w) {
    adj.get(v).add(w); // Add w to v's list.
}

void DFS(int v) {
    int nodesToVisitIndex = 0;
    List<Integer> nodesToVisit = new ArrayList<>();
    nodesToVisit.add(v);
    while (nodesToVisitIndex < nodesToVisit.size()) {
        Integer nextChild= nodesToVisit.get(nodesToVisitIndex++);// get the node and mark it as visited node by inc the index over the element.
        for (Integer s : adj.get(nextChild)) {
            if (!nodesToVisit.contains(s)) {
                nodesToVisit.add(nodesToVisitIndex, s);// add the node to the HEAD of the unvisited nodes list.
            }
        }
        System.out.println(nextChild);
    }
}

void BFS(int v) {
    int nodesToVisitIndex = 0;
    List<Integer> nodesToVisit = new ArrayList<>();
    nodesToVisit.add(v);
    while (nodesToVisitIndex < nodesToVisit.size()) {
        Integer nextChild= nodesToVisit.get(nodesToVisitIndex++);// get the node and mark it as visited node by inc the index over the element.
        for (Integer s : adj.get(nextChild)) {
            if (!nodesToVisit.contains(s)) {
                nodesToVisit.add(s);// add the node to the END of the unvisited node list.
            }
        }
        System.out.println(nextChild);
    }
}

public static void main(String args[]) {
    Graph g = new Graph(5);

    g.addEdge(0, 1);
    g.addEdge(0, 2);
    g.addEdge(1, 2);
    g.addEdge(2, 0);
    g.addEdge(2, 3);
    g.addEdge(3, 3);
    g.addEdge(3, 1);
    g.addEdge(3, 4);

    System.out.println("Breadth First Traversal- starting from vertex 2:");
    g.BFS(2);
    System.out.println("Depth First Traversal- starting from vertex 2:");
    g.DFS(2);
}}

输出: 宽度优先遍历-从顶点2开始: 2 0 3. 1 4 深度优先遍历-从顶点2开始: 2 3. 4 1 0

这是一个java程序的链接，显示DFS同时遵循递归和非递归方法，还计算发现和完成时间，但没有边对齐。

    public void DFSIterative() {
    Reset();
    Stack<Vertex> s = new Stack<>();
    for (Vertex v : vertices.values()) {
        if (!v.visited) {
            v.d = ++time;
            v.visited = true;
            s.push(v);
            while (!s.isEmpty()) {
                Vertex u = s.peek();
                s.pop();
                boolean bFinished = true;
                for (Vertex w : u.adj) {
                    if (!w.visited) {
                        w.visited = true;
                        w.d = ++time;
                        w.p = u;
                        s.push(w);
                        bFinished = false;
                        break;
                    }
                }
                if (bFinished) {
                    u.f = ++time;
                    if (u.p != null)
                        s.push(u.p);
                }
            }
        }
    }
}

这里是完整的源代码。

只是想把我的python实现添加到长长的解决方案列表中。这种非递归算法具有发现和完成事件。

worklist = [root_node]
visited = set()
while worklist:
    node = worklist[-1]
    if node in visited:
        # Node is finished
        worklist.pop()
    else:
        # Node is discovered
        visited.add(node)
        for child in node.children:
            worklist.append(child)