Breadth-First Search: Difference between revisions
(Created page with "= Breadth-First Search (BFS) = Breadth-First Search (BFS) is an algorithm for searching a tree or graph data structure for a node that meets a set of criteria. It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. <ref name=“geeksforgeeks”>https://www.geeksforgeeks.org/breadth-first-search-or-bfs-for-a-graph/</ref> == How it works == BFS uses a queue data structure to...") |
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Here are some USACO problems that can be solved using BFS: | Here are some USACO problems that can be solved using BFS: | ||
Cow Navigation | - [[http://www.usaco.org/index.php?page=viewproblem2&cpid=713|Cow Navigation]] | ||
Milk Pails | - [[http://www.usaco.org/index.php?page=viewproblem2&cpid=615|Milk Pails]] | ||
MooTube | - [[http://www.usaco.org/index.php?page=viewproblem2&cpid=788|MooTube]] | ||
Moocast | - [[http://www.usaco.org/index.php?page=viewproblem2&cpid=668|Moocast]] | ||
The Bucket List | - [[http://www.usaco.org/index.php?page=viewproblem2&cpid=856|The Bucket List]] |
Revision as of 03:52, 6 May 2023
Breadth-First Search (BFS)
Breadth-First Search (BFS) is an algorithm for searching a tree or graph data structure for a node that meets a set of criteria. It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level. <ref name=“geeksforgeeks”>https://www.geeksforgeeks.org/breadth-first-search-or-bfs-for-a-graph/</ref>
How it works
BFS uses a queue data structure to keep track of the nodes to be visited. It also uses an array or a set to mark the nodes that have been visited, to avoid revisiting them.
The algorithm works as follows:
Choose a starting node, mark it as visited, and add it to the queue. While the queue is not empty: Dequeue a node from the queue and process it (e.g., print its value). Enqueue all its adjacent nodes that have not been visited and mark them as visited. Repeat step 2 until the queue is empty.
Complexity
The time complexity of BFS is O(|V| + |E|), where |V| is the number of nodes and |E| is the number of edges in the graph. This is because every node and every edge will be explored in the worst case.
The space complexity of BFS is O(|V|), where |V| is the number of nodes in the graph. This is because the queue can hold up to |V| nodes in the worst case.
Example C++ code
Here is an example of implementing BFS in C++ using STL containers:
#include <iostream> #include <vector> #include <queue> #include <set> using namespace std; // A simple graph class class Graph { int V; // number of vertices vector<int> *adj; // adjacency list public: // constructor Graph(int V) { this->V = V; adj = new vector<int>[V]; } // add an edge from u to v void addEdge(int u, int v) { adj[u].push_back(v); } // BFS starting from s void BFS(int s) { queue<int> q; // queue for BFS set<int> visited; // set for marking visited nodes // mark s as visited and enqueue it visited.insert(s); q.push(s); while (!q.empty()) { // dequeue a node and print it int v = q.front(); q.pop(); cout << v << " "; // enqueue all adjacent nodes that are not visited for (int u : adj[v]) { if (visited.find(u) == visited.end()) { visited.insert(u); q.push(u); } } } cout << endl; } }; // driver code int main() { // create a graph with 6 vertices Graph g(6); // add some edges g.addEdge(0, 1); g.addEdge(0, 2); g.addEdge(1, 3); g.addEdge(2, 3); g.addEdge(2, 4); g.addEdge(3, 5); g.addEdge(4, 5); // BFS starting from node 0 g.BFS(0); return 0; }
USACO problems for BFS
Here are some USACO problems that can be solved using BFS:
- [Navigation] - [Pails] - [[1]] - [[2]] - [Bucket List]