Leetcode #768: Max Chunks To Make Sorted II
In this guide, we solve Leetcode #768 Max Chunks To Make Sorted II in Python and focus on the core idea that makes the solution efficient.
You will see the intuition, the step-by-step method, and a clean Python implementation you can use in interviews.
Problem Statement
You are given an integer array arr. We split arr into some number of chunks (i.e., partitions), and individually sort each chunk.
Quick Facts
- Difficulty: Hard
- Premium: No
- Tags: Stack, Greedy, Array, Sorting, Monotonic Stack
Intuition
A locally optimal choice leads to a globally optimal result for this structure.
That means we can commit to decisions as we scan without backtracking.
Approach
Sort or preprocess if needed, then repeatedly take the best available local choice.
Maintain the minimal state necessary to validate the greedy decision.
Steps:
- Sort or preprocess as needed.
- Iterate and pick the best local option.
- Track the current solution.
Example
Input: arr = [5,4,3,2,1]
Output: 1
Explanation:
Splitting into two or more chunks will not return the required result.
For example, splitting into [5, 4], [3, 2, 1] will result in [4, 5, 1, 2, 3], which isn't sorted.
Python Solution
class Solution:
def maxChunksToSorted(self, arr: List[int]) -> int:
stk = []
for v in arr:
if not stk or v >= stk[-1]:
stk.append(v)
else:
mx = stk.pop()
while stk and stk[-1] > v:
stk.pop()
stk.append(mx)
return len(stk)
Complexity
The time complexity is O(n log n). The space complexity is O(1) to O(n).
Edge Cases and Pitfalls
Watch for boundary values, empty inputs, and duplicate values where applicable. If the problem involves ordering or constraints, confirm the invariant is preserved at every step.
Summary
This Python solution focuses on the essential structure of the problem and keeps the implementation interview-friendly while meeting the constraints.