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Problem solution · Python

All Paths from Source Lead to Destination

All Paths from Source Lead to Destination: a Python solution using depth-first search. Learn the idea, check the complexity, and read the full code, with credit to Kamyu LeetCode Solutions.

Technique
Depth-first search
Source
Kamyu LeetCode Solutions
Length
36 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Depth-first search

For All Paths from Source Lead to Destination, the implementation follows one branch at a time, making it suitable for components, trees, backtracking, or dependency exploration.

  1. Define the state carried into one recursive or stack frame.
  2. Mark or choose the current state before exploring children.
  3. Combine child results or undo the choice when the branch finishes.

Code notes

  • 36 lines of Python from the credited upstream file all-paths-from-source-lead-to-destination.py.
  • The implementation visibly relies on sequence storage, hash lookup.
  • No explicit loop blocks detected, together with recursive traversal.

Complexity

Count unique states for graph traversal; for backtracking, count the branching factor and maximum depth.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from Kamyu LeetCode Solutions by kamyu104 and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeAll Paths from Source Lead to Destination · PythonPython
Use this to learn the idea, then write your own version.
# Time:  O(n + e)# Space: O(n + e) import collections  class Solution(object):    def leadsToDestination(self, n, edges, source, destination):        """        :type n: int        :type edges: List[List[int]]        :type source: int        :type destination: int        :rtype: bool        """        UNVISITED, VISITING, DONE = range(3)        def dfs(children, node, destination, status):            if status[node] == DONE:                return True            if status[node] == VISITING:                return False            status[node] = VISITING            if node not in children and node != destination:                return False            if node in children:                for child in children[node]:                    if not dfs(children, child, destination, status):                        return False            status[node] = DONE            return True                children = collections.defaultdict(list)        for parent, child in edges:            children[parent].append(child)        return dfs(children, source, destination, [0]*n)

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