\n Learning various algorithms while preparing for coding tests is very important. Among them,
\n DFS (Depth-First Search) and BFS (Breadth-First Search) algorithms are effective for solving many problems.
\n In this article, we will understand the concepts of DFS and BFS and learn how to prepare for actual coding tests through examples using these algorithms.\n <\/p>\n
\n DFS and BFS are algorithms for graph traversal, and the approaches of the two algorithms are different from each other.\n <\/p>\n
\n DFS is a depth-first search that explores as deeply as possible from one node before returning to the previous node
\n to explore other nodes when no further exploration is possible. It is generally implemented using a stack structure.\n <\/p>\n
\n BFS is a breadth-first search that explores all neighboring nodes of one node before moving on to explore nodes at the next depth.
\n It is typically implemented using a queue structure.\n <\/p>\n
\n Here is an example problem that can be solved using DFS and BFS. \n For each query, print “YES” if X and Y are connected, otherwise print “NO”.\n <\/p>\n \n The graph can be represented in the form of an adjacency list. Connected nodes are added to the list to determine the existence or lack thereof.\n <\/p>\n \n We will implement DFS through recursive calls. A boolean array is used to check visited nodes, and we will explore \n BFS is implemented using a queue. We add the starting node to the queue, and for each node, we take it out of the queue one by one \n Now we will write the complete code that takes the input of the graph and determines connectivity using DFS and BFS.\n <\/p>\n \n DFS and BFS each have their strengths and weaknesses, and it is important to choose the right algorithm depending on the specific problem. <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\n Problem:<\/strong> Determine whether two nodes in a given graph are connected.\n <\/p>\nInput<\/h3>\n
\n
Output<\/h3>\n
Problem Solving Process<\/h2>\n
1. Graph Representation<\/h3>\n
2. Implementing DFS<\/h3>\n
\n starting from the given node until we reach the target node.\n <\/p>\n\nfun dfs(graph: List3. Implementing BFS<\/h3>\n
\n to visit all neighboring nodes and check if we reach the target node.\n <\/p>\n\nfun bfs(graph: List4. Complete Code<\/h3>\n
\nimport java.util.*\n\nfun main() {\n val scanner = Scanner(System.`in`)\n val n = scanner.nextInt()\n val e = scanner.nextInt()\n\n val graph = List(n) { mutableListOfConclusion<\/h2>\n
\n I hope this tutorial helped you understand the two algorithms and will aid you in preparing for actual coding tests.\n <\/p>\n