Serialize and Deserialize Binary Tree

Depth-First Search explores a graph by going as deep as possible along each branch before backtracking, making it ideal for problems involving path finding, connectivity, and exhaustive search. DFS can be implemented recursively using the call stack or iteratively using an explicit stack data structure. For this problem, we apply DFS by starting at a given node, marking it as visited, and recursively exploring all unvisited neighbors. The key advantage of DFS over BFS is its lower memory usage for deep graphs and its natural fit for problems requiring backtracking or path enumeration. When implementing DFS on a grid, we typically define direction arrays for moving up, down, left, and right. At each cell, we check bounds, verify the cell has not been visited, and confirm it meets our criteria before recursing. Common DFS applications include finding connected components, detecting cycles in directed and undirected graphs, topological sorting, and solving constraint satisfaction problems. The time complexity is O(V + E) and space complexity is O(V) for the recursion stack in the worst case. Always consider whether recursive DFS might cause a stack overflow for very deep graphs, and switch to iterative DFS with an explicit stack in those cases.