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Problem solution · Python

Longest Common Subpath

Longest Common Subpath: a Python solution using sliding window or two pointers. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Sliding window or two pointers
Source
walkccc LeetCode Solutions
Length
50 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

Sliding window or two pointers

For Longest Common Subpath, the implementation maintains a moving interval and updates only the information that enters or leaves the window.

  1. Choose the invariant that makes a window valid or useful.
  2. Advance the right boundary and add the new element.
  3. Move the left boundary only as needed while maintaining the invariant and updating the answer.

Code notes

  • 50 lines of Python from the credited upstream file 1923.py.
  • The implementation visibly relies on sequence storage, hash lookup, ordered lookup.
  • No explicit loop blocks detected.

Complexity

Confirm that neither pointer moves backwards; if so, the scan is usually linear apart from the window’s data-structure operations.

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

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeLongest Common Subpath · PythonPython
Use this to learn the idea, then write your own version.
class Solution:  def __init__(self):    self.BASE = 165_131    self.HASH = 8_417_508_174_513   def longestCommonSubpath(self, n: int, paths: list[list[int]]) -> int:    l = 0    r = len(paths[0])     while l < r:      m = l + (r - l + 1) // 2      if self._checkCommonSubpath(paths, m):        l = m      else:        r = m - 1     return l   def _checkCommonSubpath(self, paths: list[list[int]], m: int) -> bool:    """    Returns True if there's a common subpath of length m for all the paths.    """    # Calculate the hash values for subpaths of length m for every path.    hashSets = [self._rabinKarp(path, m) for path in paths]     # Check if there is a common subpath of length m.    for subpathHash in hashSets[0]:      if all(subpathHash in hashSet for hashSet in hashSets):        return True     return False   def _rabinKarp(self, path: list[int], m: int) -> set[int]:    """Returns the hash values for subpaths of length m in the path."""    hashes = set()    maxPower = 1    hash = 0     for i, num in enumerate(path):      hash = (hash * self.BASE + num) % self.HASH      if i >= m:        hash = (hash - path[i - m] * maxPower %                self.HASH + self.HASH) % self.HASH      else:        maxPower = maxPower * self.BASE % self.HASH      if i >= m - 1:        hashes.add(hash)     return hashes

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