Is Max Chunks To Make Sorted II easy or hard?

Max Chunks To Make Sorted II is classified as a Hard problem on LeetCode. Handling duplicate values and determining safe partition boundaries makes it more complex than the original version. Efficient solutions require recognizing greedy merging patterns or prefix–suffix ordering checks.

Max Chunks To Make Sorted II Python/Java solution

Python and Java implementations usually follow either the monotonic stack pattern or the prefix max/suffix min approach. The stack solution iterates once and merges chunks dynamically, while the prefix-suffix solution precomputes boundary constraints before counting valid splits.

How to solve Max Chunks To Make Sorted II in O(n)?

Compute prefix maximums and suffix minimums, then count indices where prefixMax[i] <= suffixMin[i+1]. Each such index represents a valid chunk boundary. Another O(n) method uses a monotonic stack that merges chunks whenever a smaller element violates the ordering of previous chunks.

What is the best approach for Max Chunks To Make Sorted II?

The monotonic stack approach is considered the best solution. It processes the array in one pass while maintaining chunk boundaries using a stack of maximum values. When a smaller element appears, earlier chunks are merged to preserve global order. The algorithm runs in O(n) time with O(n) space.

Is Max Chunks To Make Sorted II asked at Google/Amazon/Meta?

Max Chunks To Make Sorted II appears in interview preparation lists for companies like Google, Amazon, and Meta because it tests array partitioning, greedy reasoning, and monotonic stack patterns. Variations of this problem often appear in coding interviews focused on segment boundaries and ordering constraints.

What data structure is used in Max Chunks To Make Sorted II?

The key data structure is a monotonic stack that stores the maximum value of each chunk. It allows efficient merging of previous chunks when a new element breaks sorted ordering. The alternative approach uses prefix and suffix arrays to track maximum and minimum values.

What is the time complexity of Max Chunks To Make Sorted II?

The optimal solutions run in O(n) time where n is the length of the array. Both the monotonic stack approach and the prefix maximum–suffix minimum method require a single or small constant number of passes through the array. Space complexity is O(n) due to either the auxiliary arrays or the stack.

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768. Max Chunks To Make Sorted II

Q: What is the best approach for Max Chunks To Make Sorted II?

The monotonic stack approach is considered the best solution. It processes the array in one pass while maintaining chunk boundaries using a stack of maximum values. When a smaller element appears, earlier chunks are merged to preserve global order. The algorithm runs in O(n) time with O(n) space.

Q: Is Max Chunks To Make Sorted II asked at Google/Amazon/Meta?

Max Chunks To Make Sorted II appears in interview preparation lists for companies like Google, Amazon, and Meta because it tests array partitioning, greedy reasoning, and monotonic stack patterns. Variations of this problem often appear in coding interviews focused on segment boundaries and ordering constraints.

Q: What data structure is used in Max Chunks To Make Sorted II?

The key data structure is a monotonic stack that stores the maximum value of each chunk. It allows efficient merging of previous chunks when a new element breaks sorted ordering. The alternative approach uses prefix and suffix arrays to track maximum and minimum values.

Q: What is the time complexity of Max Chunks To Make Sorted II?

The optimal solutions run in O(n) time where n is the length of the array. Both the monotonic stack approach and the prefix maximum–suffix minimum method require a single or small constant number of passes through the array. Space complexity is O(n) due to either the auxiliary arrays or the stack.

Hard54.7% acceptance5 companies

You are given an integer array arr.

We split arr into some number of chunks (i.e., partitions), and individually sort each chunk. After concatenating them, the result should equal the sorted array.

Return the largest number of chunks we can make to sort the array.

Example 1:

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.

Example 2:

Input: arr = [2,1,3,4,4]
Output: 4
Explanation:
We can split into two chunks, such as [2, 1], [3, 4, 4].
However, splitting into [2, 1], [3], [4], [4] is the highest number of chunks possible.

Constraints: