Breadth First Search (BFS)

Breadth first search (BFS) is a fundamental algorithm used in data structures and algorithms with Python. It is an algorithm for traversing or searching a tree, graph, or any kind of data structure. BFS is one of the most widely used graph algorithms and is used to find the shortest path from one vertex to another in an unweighted graph.

BFS is useful for finding the shortest path from one node to another, or for finding all nodes that are a given distance away from the start node. It is also used to solve many other problems, such as finding the minimum spanning tree, finding the maximum flow in a network, and finding the maximum independent set in a graph.

The algorithm begins at the root node, and explores all of the neighboring nodes at the present depth before moving on to the nodes at the next level. It is also known as level order traversal, as each level of the tree is visited in order.

How the Algorithm Works in Detail

Breadth first search is an algorithm for traversing or searching a graph. It starts at the root node and explores all of the neighboring nodes at the current level before moving on to the nodes at the next level.

The algorithm works by first exploring the root node and then exploring all of its neighbors. Then, it moves on to explore the neighbors of the neighbors, and so on. At each level, the algorithm visits all of the nodes at that level before moving on to the next level.

The algorithm can be implemented using a queue. A queue is a data structure that stores items in a first-in, first-out (FIFO) order. The algorithm adds the root node to the queue and then continues to add all of its neighbors to the queue. It then dequeues the first item in the queue and explores all of its neighbors. This process is repeated until the queue is empty, at which point all of the nodes in the graph have been explored.

Time Complexity

The time complexity of breadth first search is O(V+E), where V is the number of vertices and E is the number of edges in the graph. This means that the algorithm will take O(V+E) time to explore the entire graph.

The best-case time complexity of BFS is O(V), which occurs when the graph is a single connected component, meaning all of the nodes are connected to each other. The worst-case time complexity is O(V+E), which occurs when the graph is composed of many connected components.

Space Complexity

The space complexity of breadth first search is O(V) in the worst-case, as the algorithm will need to store all of the vertices in the graph in order to explore them.

Python Code for Breadth First Search

The following Python code implements the breadth first search algorithm.

# Create a queue to store the nodes to be explored
queue = []

# Add the root node to the queue
queue.append(root)

# Loop while the queue is not empty
while queue:
    # Dequeue the first node in the queue
    node = queue.pop(0)
    
    # Explore the node
    # ...
    
    # Add the node's neighbors to the queue
    for neighbor in node.neighbors:
        queue.append(neighbor)

Conclusion

In conclusion, breadth first search is a fundamental algorithm used in data structures and algorithms with Python. It is an algorithm for traversing or searching a tree, graph, or any kind of data structure. The algorithm begins at the root node, and explores all of the neighboring nodes at the present depth before moving on to the nodes at the next level. The time complexity of BFS is O(V+E), where V is the number of vertices and E is the number of edges in the graph. The space complexity of BFS is O(V) in the worst-case, as the algorithm will need to store all of the vertices in the graph in order to explore them.

Exercises

def bfs(root, target):
    queue = []
    queue.append(root)
    while queue:
        node = queue.pop(0)
        if node.val == target:
            return node
        if node.left:
            queue.append(node.left)
        if node.right:
            queue.append(node.right)
    return None

def bfs(start, end):
    queue = []
    visited = set()
    path = []
    queue.append((start, [start]))
    while queue:
        node, path = queue.pop(0)
        visited.add(node)
        if node == end:
            return path
        for neighbor in node.neighbors:
            if neighbor not in visited:
                queue.append((neighbor, path + [neighbor]))
    return None

def bfs(graph):
    queue = []
    visited = set()
    tree = []
    queue.append((graph.root, []))
    while queue:
        node, path = queue.pop(0)
        visited.add(node)
        for neighbor in node.neighbors:
            if neighbor not in visited:
                queue.append((neighbor, path + [(node, neighbor)]))
    for (u, v) in path:
        if (v, u) not in path:
            tree.append((u, v))
    return tree

def bfs(graph, source, sink):
    queue = []
    visited = set()
    max_flow = 0
    queue.append((source, 0))
    while queue:
        node, flow = queue.pop(0)
        visited.add(node)
        if node == sink:
            max_flow = max(max_flow, flow)
        for neighbor in node.neighbors:
            if neighbor not in visited and graph.capacity[node][neighbor] > 0:
                queue.append((neighbor, min(flow, graph.capacity[node][neighbor])))
    return max_flow

def bfs(graph):
    queue = []
    visited = set()
    max_set = []
    queue.append(graph.root)
    while queue:
        node = queue.pop(0)
        visited.add(node)
        is_independent = True
        for neighbor in node.neighbors:
            if neighbor not in visited:
                is_independent = False
                queue.append(neighbor)
        if is_independent:
            max_set.append(node)
    return max_set