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Problem solution · Python

Maximum Number of Tasks You Can Assign

Maximum Number of Tasks You Can Assign: 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
52 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 Maximum Number of Tasks You Can Assign, 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

  • 52 lines of Python from the credited upstream file 2071.py.
  • The implementation visibly relies on sequence storage.
  • 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 codeMaximum Number of Tasks You Can Assign · PythonPython
Use this to learn the idea, then write your own version.
from sortedcontainers import SortedList  class Solution:  def maxTaskAssign(      self,      tasks: list[int],      workers: list[int],      pills: int,      strength: int,  ) -> int:    tasks.sort()    workers.sort()     def canComplete(k: int, pillsLeft: int) -> bool:      """Returns True if we can finish k tasks."""      # k strongest workers      sortedWorkers = SortedList(workers[-k:])       # Out of the k smallest tasks, start from the biggest one.      for i in reversed(range(k)):        # Find the first worker that has strength >= tasks[i].        index = sortedWorkers.bisect_left(tasks[i])        if index < len(sortedWorkers):          sortedWorkers.pop(index)        elif pillsLeft > 0:          # Find the first worker that has strength >= tasks[i] - strength.          index = sortedWorkers.bisect_left(tasks[i] - strength)          if index < len(sortedWorkers):            sortedWorkers.pop(index)            pillsLeft -= 1          else:            return False        else:          return False       return True     ans = 0    l = 0    r = min(len(tasks), len(workers))     while l <= r:      m = (l + r) // 2      if canComplete(m, pills):        ans = m        l = m + 1      else:        r = m - 1     return ans

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