Topological sorting is an important concept in data structures. It is primarily used in directed acyclic graphs (DAGs) to find a linear ordering of the vertices such that for every directed edge (u, v), vertex u comes before vertex v in the ordering. In other words, it arranges the vertices in a way that all dependencies are satisfied.

**What is a Directed Acyclic Graph?**

A directed acyclic graph is a finite graph with directed edges between vertices that does not contain any cycles. This means that there are no loops or circular dependencies within the graph. Each edge has a direction associated with it, indicating the flow from one vertex to another.

**Why do we need Topological Sorting?**

Topological sorting has various applications, especially in scheduling and task planning scenarios where certain tasks or activities need to be completed before others can begin. It helps identify the order in which these tasks should be executed to ensure smooth progress.

__Algorithm for Topological Sorting:__

There are different algorithms available for performing topological sorting on DAGs. One of the most commonly used algorithms is known as “Depth First Search” or DFS.

The steps involved in performing topological sorting using DFS are as follows:

1. Choose any arbitrary vertex as the starting point. 2. Visit all of its adjacent vertices.

3. Recursively perform steps 1 and 2 for each unvisited adjacent vertex. 4. Once all adjacent vertices have been visited, add the current vertex to the topological sort order.

### Example:

Consider a scenario where we have a DAG representing courses and their prerequisites:

- Course A has no prerequisites.
- Course B has prerequisites – Course A.
- Course C has prerequisites – Course A and Course B.
- Course D has prerequisites – Course C.

To perform topological sorting on this DAG using the DFS algorithm, we start with an arbitrary vertex and apply the steps mentioned earlier.

The topological sort order for the above example would be: A → B → C → D.

### Time Complexity:

The time complexity of the topological sorting algorithm depends on the number of vertices and edges in the graph. It can be represented as O(V + E), where V represents the number of vertices and E represents the number of edges in the graph.

## Conclusion:

In summary, topological sorting is a crucial concept in data structures that helps us find a linear ordering of vertices in a directed acyclic graph. It ensures that all dependencies are satisfied by arranging the vertices in a specific order. The DFS algorithm is commonly used to perform topological sorting, providing an efficient solution for scheduling and task planning scenarios.

By understanding and implementing topological sorting algorithms, you can optimize your programs and applications by ensuring that tasks are executed in the correct order, avoiding any potential issues or conflicts.