[algorithm] Non-recursive depth first search algorithm

I am looking for a non-recursive depth first search algorithm for a non-binary tree. Any help is very much appreciated.

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);
    }
}

DFS iterative in Java:

//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;
}

If you have pointers to parent nodes, you can do it without additional memory.

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

Note that if the child nodes are stored as an array rather than through sibling pointers, the next sibling can be found as:

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

FULL example WORKING code, without stack:

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);
}}

output: Breadth First Traversal- starting from vertex 2: 2 0 3 1 4 Depth First Traversal- starting from vertex 2: 2 3 4 1 0

You can use a stack. I implemented graphs with Adjacency Matrix:

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();
        }
    }
}

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

Just watched this video and came out with implementation. It looks easy for me to understand. Please critique this.

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()
}

Just wanted to add my python implementation to the long list of solutions. This non-recursive algorithm has discovery and finished events.

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)

Use a stack to track your nodes

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)

}

An ES6 implementation based on biziclops great answer:

root = {_x000D_
  text: "root",_x000D_
  children: [{_x000D_
    text: "c1",_x000D_
    children: [{_x000D_
      text: "c11"_x000D_
    }, {_x000D_
      text: "c12"_x000D_
    }]_x000D_
  }, {_x000D_
    text: "c2",_x000D_
    children: [{_x000D_
      text: "c21"_x000D_
    }, {_x000D_
      text: "c22"_x000D_
    }]_x000D_
  }, ]_x000D_
}_x000D_
_x000D_
console.log("DFS:")_x000D_
DFS(root, node => node.children, node => console.log(node.text));_x000D_
_x000D_
console.log("BFS:")_x000D_
BFS(root, node => node.children, node => console.log(node.text));_x000D_
_x000D_
function BFS(root, getChildren, visit) {_x000D_
  let nodesToVisit = [root];_x000D_
  while (nodesToVisit.length > 0) {_x000D_
    const currentNode = nodesToVisit.shift();_x000D_
    nodesToVisit = [_x000D_
      ...nodesToVisit,_x000D_
      ...(getChildren(currentNode) || []),_x000D_
    ];_x000D_
    visit(currentNode);_x000D_
  }_x000D_
}_x000D_
_x000D_
function DFS(root, getChildren, visit) {_x000D_
  let nodesToVisit = [root];_x000D_
  while (nodesToVisit.length > 0) {_x000D_
    const currentNode = nodesToVisit.shift();_x000D_
    nodesToVisit = [_x000D_
      ...(getChildren(currentNode) || []),_x000D_
      ...nodesToVisit,_x000D_
    ];_x000D_
    visit(currentNode);_x000D_
  }_x000D_
}

You would use a stack that holds the nodes that were not visited yet:

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

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);

                }
            }

The general logic is, push a node(starting from root) into the Stack, Pop() it and Print() value. Then if it has children( left and right) push them into the stack - push Right first so that you will visit Left child first(after visiting node itself). When stack is empty() you will have visited all nodes in Pre-Order.

Suppose you want to execute a notification when each node in a graph is visited. The simple recursive implementation is:

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
}

Ok, now you want a stack-based implementation because your example doesn't work. Complex graphs might for instance cause this to blow the stack of your program and you need to implement a non-recursive version. The biggest issue is to know when to issue a notification.

The following pseudo-code works (mix of Java and C++ for readability):

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);
  }
}

It looks complicated but the extra logic needed for issuing notifications exists because you need to notify in reverse order of visit - DFS starts at root but notifies it last, unlike BFS which is very simple to implement.

For kicks, try following graph: nodes are s, t, v and w. directed edges are: s->t, s->v, t->w, v->w, and v->t. Run your own implementation of DFS and the order in which nodes should be visited must be: w, t, v, s A clumsy implementation of DFS would maybe notify t first and that indicates a bug. A recursive implementation of DFS would always reach w last.

Pseudo-code based on @biziclop's answer:

• Using only basic constructs: variables, arrays, if, while and for
• Functions getNode(id) and getChildren(id)
• Assuming known number of nodes N

NOTE: I use array-indexing from 1, not 0.

Breadth-first

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

Depth-first

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

Here is a link to a java program showing DFS following both reccursive and non-reccursive methods and also calculating discovery and finish time, but no edge laleling.

    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);
                }
            }
        }
    }
}

Full source here.

Non-recursive DFS using ES6 generators

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 
  }    
}

It deviates from typical non-recursive DFS to easily detect when all reachable descendants of given node were processed and to maintain the current path in the list/stack.

Using Stack, here are the steps to follow: Push the first vertex on the stack then,

1. If possible, visit an adjacent unvisited vertex, mark it, and push it on the stack.
2. If you can’t follow step 1, then, if possible, pop a vertex off the stack.
3. If you can’t follow step 1 or step 2, you’re done.

Here's the Java program following the above steps:

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;
}

While "use a stack" might work as the answer to contrived interview question, in reality, it's just doing explicitly what a recursive program does behind the scenes.

Recursion uses the programs built-in stack. When you call a function, it pushes the arguments to the function onto the stack and when the function returns it does so by popping the program stack.