When is depth first search optimal

Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. Asked 7 years, 9 months ago. Active 1 year, 1 month ago. Viewed 20k times. I have this question in my mind for long but never got reasonable answer for that : Usually in artifitial intelligent course when it comes to search it is always said that BFS is optimal but DFS is not but I can come up with many example that shows with DFS we can even get the answer faster.

Improve this question. Hamed Minaee Hamed Minaee 2, 7 7 gold badges 38 38 silver badges 71 71 bronze badges. Optimal in terms of time-complexity execution performance or space-complexity memory usage? Or in the actual quality of the output, when the best possible solution is very hard to calculate such as in the case of a greedy algorithm for example? I guess this is what the OP means. Add a comment.

Active Oldest Votes. The state from where the search begins. A Goal Test. A function that looks at the current state returns whether or not it is the goal state. The Solution to a search problem is a sequence of actions, called the plan that transforms the start state to the goal state. This plan is achieved through search algorithms. Types of search algorithms: There are far too many powerful search algorithms out there to fit in a single article.

Instead, this article will discuss six of the fundamental search algorithms, divided into two categories, as shown below. Attention reader! Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready. Note that there is much more to search algorithms than the chart I have provided above. However, this article will mostly stick to the above chart, exploring the algorithms given there.

Uninformed Search Algorithms: The search algorithms in this section have no additional information on the goal node other than the one provided in the problem definition. Uninformed search is also called Blind search. The following uninformed search algorithms are discussed in this section.

A strategy, describing the manner in which the graph will be traversed to get to G. A fringe, which is a data structure used to store all the possible states nodes that you can go from the current states.

A tree, that results while traversing to the goal node. A solution plan, which the sequence of nodes from S to G. Depth First Search : Depth-first search DFS is an algorithm for traversing or searching tree or graph data structures. It is one of the most commonly preferred algorithms used for traversing or search in tree or graph data structures by using the idea of backtracking.

DFS is also known as Depth First Traversal in case we are using the algorithm in tree data structures Note: A tree is a special kind of graph with no cycles. The algorithm starts at a node of a graph and goes as far as possible in a given path, then backtracks until it finds an unexplored path, and then explores the rest of it.

The process is repeatedly followed until all the nodes in the graph are explored. Most of the concepts in computer science can be visualized and represented in terms of graph data structure.

DFS is one such useful algorithm for analysing these problems easily. The goal here is to find whether the node E is present in the graph. Just by seeing the graph, we can say that node E is not present. Lets see how DFS works to identify this. Step 2: Mark S as Visited. Step 3: While the stack is not empty, repeat the below steps:.

Pop the top element i. Mark A as visited. Mark D as visited. Mark B as visited. Mark C as visited. This is how a depth-first search works, by traversing the nodes depth-wise. We stop DFS and return true when we find the required node key. Angular 7. Machine Learning. Data Structures. Operating System. Computer Network. Compiler Design. Computer Organization.

Discrete Mathematics. Ethical Hacking. Computer Graphics. Software Engineering. Web Technology. Cyber Security. C Programming. Control System. Data Mining. Data Warehouse. Javatpoint Services JavaTpoint offers too many high quality services. Following are the various types of uninformed search algorithms: Breadth-first Search Depth-first Search Depth-limited Search Iterative deepening depth-first search Uniform cost search Bidirectional Search 1. Breadth-first Search: Breadth-first search is the most common search strategy for traversing a tree or graph.

This algorithm searches breadthwise in a tree or graph, so it is called breadth-first search. BFS algorithm starts searching from the root node of the tree and expands all successor node at the current level before moving to nodes of next level. The breadth-first search algorithm is an example of a general-graph search algorithm. Breadth-first search implemented using FIFO queue data structure. Advantages: BFS will provide a solution if any solution exists. If there are more than one solutions for a given problem, then BFS will provide the minimal solution which requires the least number of steps.

Disadvantages: It requires lots of memory since each level of the tree must be saved into memory to expand the next level. BFS needs lots of time if the solution is far away from the root node. Example: In the below tree structure, we have shown the traversing of the tree using BFS algorithm from the root node S to goal node K.