Course Schedule II
There are a total of numCourses
courses you have to take, labeled from 0
to numCourses - 1
. You are given an array prerequisites
where prerequisites[i] = [ai, bi]
indicates that you must take course bi
first if you want to take course ai
.
- For example, the pair
[0, 1]
, indicates that to take course0
you have to first take course1
.
Return the ordering of courses you should take to finish all courses. If there are many valid answers, return any of them. If it is impossible to finish all courses, return an empty array.
Example 1:
Input: numCourses = 2, prerequisites = [[1,0]]
Output: [0,1]
Explanation: There are a total of 2 courses to take. To take course 1 you should have finished course 0. So the correct course order is [0,1].
Example 2:
Input: numCourses = 4, prerequisites = [[1,0],[2,0],[3,1],[3,2]]
Output: [0,2,1,3]
Explanation: There are a total of 4 courses to take. To take course 3 you should have finished both courses 1 and 2. Both courses 1 and 2 should be taken after you finished course 0.
So one correct course order is [0,1,2,3]. Another correct ordering is [0,2,1,3].
Example 3:
Input: numCourses = 1, prerequisites = []
Output: [0]
Constraints:
1 <= numCourses <= 2000
0 <= prerequisites.length <= numCourses * (numCourses - 1)
prerequisites[i].length == 2
0 <= ai, bi < numCourses
ai != bi
- All the pairs
[ai, bi]
are distinct.
Solution
The problems asks us to find the ordering of courses to take. We know this involves the dependency of courses, so we will find the topological ordering of these courses.
We will build a graph with directed edges from a course
's prereq
to course
itself, then find the topological sorting of the graph.
Implementation
def findOrder(numCourses: int, prerequisites: List[List[int]]) -> List[int]:
visited = [False for _ in range(numCourses)]
finished = []
graph = defaultdict(list)
for course, prereq in prerequisites:
graph[prereq].append(course)
def dfs(prereq):
for course in graph[prereq]:
if not visited[course]:
visited[course] = True
if not dfs(course): return False # unable to finish course
elif course not in finished: # cycle in graph
return False
finished.append(prereq)
return True
for i in range(numCourses): # visit all trees in graph
if not visited[i]:
visited[i] = True
if not dfs(i): return []
finished.reverse() # topological ordering = reverse finish time
return finished
Ready to land your dream job?
Unlock your dream job with a 2-minute evaluator for a personalized learning plan!
Start EvaluatorWhich of the following problems can be solved with backtracking (select multiple)
Recommended Readings
LeetCode Patterns Your Personal Dijkstra's Algorithm to Landing Your Dream Job The goal of AlgoMonster is to help you get a job in the shortest amount of time possible in a data driven way We compiled datasets of tech interview problems and broke them down by patterns This way we
Recursion Recursion is one of the most important concepts in computer science Simply speaking recursion is the process of a function calling itself Using a real life analogy imagine a scenario where you invite your friends to lunch https algomonster s3 us east 2 amazonaws com recursion jpg You first
Runtime Overview When learning about algorithms and data structures you'll frequently encounter the term time complexity This concept is fundamental in computer science and offers insights into how long an algorithm takes to complete given a certain input size What is Time Complexity Time complexity represents the amount of time
Want a Structured Path to Master System Design Too? Don’t Miss This!