Topological sort is a sorting algorithm used to sort a directed acyclic graph (DAG) in a specific order. It is commonly used in many applications such as task scheduling, dependency resolution, and data processing.


Here is an implementation of topological sort in OCaml:

let topological_sort (graph: int list array) : int list =  
  let n = Array.length graph in  
  let visited = Array.make n false in  
  let stack = ref [] in  
  let rec dfs (u: int) : unit =  
    visited.(u) <- true;  
    List.iter (fun v -> if not visited.(v) then dfs v) graph.(u);  
    stack := u :: !stack  
  for i = 0 to n - 1 do  
    if not visited.(i) then dfs i  

Here, graph is a representation of the DAG in the form of an adjacency list. The function returns a list of nodes in topological order. Here is an example.

let courses = ["C1"; "C2"; "C3"; "C4"; "C5"]  
let prerequisites = [("C1", ["C2"; "C3"]); ("C2", ["C4"]); ("C3", ["C4"]); ("C4", ["C5"])]  
let course_graph =  
  let n = List.length courses in  
  let graph = Array.make n [] in  
  let index_of_course c =
    match find_index (fun x -> x = c) courses with
    | Some(index) -> index
    | None -> failwith "Course not found"
  List.iter (fun (c, prereqs) ->  
    let u = index_of_course c in  
    let vs = index_of_course prereqs in  
    List.iter (fun v -> graph.(u) <- v :: graph.(u)) vs  
  ) prerequisites;  
let course_order = topological_sort course_graph |> (fun i -> List.nth courses i)

let print_course_order course_order =
  List.iter (fun course -> Printf.printf "%s " course) course_order;
  Printf.printf "\n"

let () =
  print_course_order course_order

Step-by-step Explanation

  1. Create an array visited of size n, where n is the number of nodes in the graph. Initialize all elements to false.
  2. Create an empty stack stack.
  3. Define a recursive function dfs that takes a node u as input.
  4. Mark u as visited by setting visited.(u) to true.
  5. For each neighbor v of u in the graph, if v has not been visited, recursively call dfs v.
  6. Push u onto the stack.
  7. Loop through all nodes in the graph. If a node has not been visited, call dfs on it.
  8. Return the stack.

The algorithm works by performing a depth-first search (DFS) on the graph. When a node is finished being explored, it is pushed onto the stack. Since a DAG has no cycles, the nodes can be ordered in reverse order of their finishing times, which gives a valid topological order.

Complexity Analysis

The time complexity of topological sort is O(V+E), where V is the number of vertices and E is the number of edges in the graph. This is because each vertex and edge is visited once during the DFS traversal. The space complexity is also O(V+E), since the adjacency list takes up O(V+E) space and the stack can contain all vertices in the worst case.